Here is a list of publications related to LIFEx software.

Publications 2025 - Journal papers (111)

(86)

  1. LIFEx-texture: Zhang K, Wang G, Wang X, Kan Y, Wang W, Yang J. The baseline 18F-FDG PET/CT imaging features in pediatric patients with congenital neuroblastoma. BMC Med Imaging. 2025 Aug 14;25(1):330. https://doi.org/10.1186/s12880-025-01863-2. PMID: 40813959; PMCID: PMC12355819.
  2. LIFEx-texture: Hidenori Amaike, Masatoshi Kanda, Hirotsugu Yamazaki, Koki Nakamura, Li Ma, Ken Nagahata, Hiroyuki Nakamura, Arata Osanami, Naoya Yama, Masamitsu Hatakenaka, Masato Furuhashi, Hiroki Takahashi; The effectiveness of lesion detection for IgG4-related kidney diseases by diffusion-weighted imaging. Nephron 2025; https://doi.org/10.1159/000547628
  3. LIFEx-texture: Ab Mumin, N., Liew, CH., Ong, SQ. et al. MRI-based texture analysis for breast cancer subtype classification in a multi-ethnic population. Magn Reson Mater Phy (2025). https://doi.org/10.1007/s10334-025-01285-9
  4. LIFEx-main: Chunqiao Wu, Hongjie Hu, Fanger Bao, Zhiying Fei. Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer. Am J Transl Res 2025;17(7):5441-5452 ajtr.org /ISSN:1943-8141/AJTR0163464 https://e-century.us/files/ajtr/17/7/ajtr0163464.pdf
  5. LIFEx-texture: Seify, N., Khezerloo, D., Jahanshahi, A. et al. Longitudinal assessment of MRI-based radiomics feature changes in caudate and putamen over progression of parkinson’s disease. Egypt J Radiol Nucl Med 56, 135 (2025). https://doi.org/10.1186/s43055-025-01552-8
  6. LIFEx-MTV: Zhang, H., Li, C., Chen, Y. et al. Vasculature cast sign on 18F-FDG PET/CT: a specific imaging marker for pediatric T-lymphoblastic lymphoma. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07488-5
  7. LIFEx-MTV: Kargar Samani I, Gheysens O, Regnier M, Collard A, André M, Van Den Neste E and Vander Borght T (2025) Prognostic value of a simple distance index derived from PET maximum intensity projection. Front. Med. 12:1565525. https://doi.org/10.3389/fmed.2025.1565525
  8. LIFEx-texture: Tang W, Chen H, Liu P, Zhang Y. Diagnostic value of CT radiomics and clinical features in differentiating focal organizing pneumonia from peripheral lung cancer. Front Oncol. 2025 Jun 25;15:1620217. https://doi.org/10.3389/fonc.2025.1620217 PMID: 40636691; PMCID: PMC12237632.
  9. LIFEx-texture: Serçe Unat D, Aguloğlu N, Unat ÖS, Aksu A, Erer OF, Özdemir Ö, Polat G. Non-invasive procedure in differential diagnosis of sarcoidosis and tuberculosis lymph nodes: Radiomic model of 18F-FDG PET-CT. Sarcoidosis Vasc Diffuse Lung Dis. 2025;42(2):16188. https://doi.org/10.36141/svdld.v42i2.16188 Epub 2025 Jun 25. PMCID: PMC12242878.
  10. LIFEx-texture: A Italiano, O Gautier, J Dupont, T Assi, L Dawi, L Lawrance, A Bone, G Jardali, A Choucair, S Ammari, A Bayle, E Rouleau, PH Cournede, I Borget, B Besse, F Barlesi, C Massard, N Lassau. The correlation of liquid biopsy genomic data to radiomics in colon, pancreatic, lung and prostatic cancer patients. European Journal of Cancer, Volume 226, 2025, 115609, ISSN 0959-8049, https://doi.org/10.1016/j.ejca.2025.115609
  11. LIFEx-mtv: Dondolin, R., Garrou, F., Almasri, M. et al. Integration of [18F]FDG-PET radiomics with liquid biopsy improves outcome prediction in newly diagnosed diffuse large B-cell lymphoma. Leukemia (2025). https://doi.org/10.1038/s41375-025-02688-2
  12. LIFEx-texture: M El-Jammal, C Chung. Paving a Path to Clinical Impact with Radiomics: Enabling Reproducibility and Reach. Cancer Res (2025) 85 (13): 2353–2355. https://doi.org/10.1158/0008-5472.CAN-25-2083
  13. LIFEx-main: B Delache, AR Garnier, C Herate, F Relouzat, P Lê-Bury, J Lemaitre, A Berriche, Q Sconosciuti, E Navarre, N Verguet, J Creppy, O Gorgé, JN Tournier, F Ducancel, L Vecellio, R Le Grand, T Naninck. Positron emission tomography-based comparison of methods for exposing macaques to respiratory pathogens, Journal of Aerosol Science, 2025, 106646, ISSN 0021-8502, https://doi.org/10.1016/j.jaerosci.2025.106646
  14. LIFEx-main: Kim, JY., Lee, C., Lee, JH. et al. Comparison of the maximum standardized uptake value and magnetic resonance imaging characteristics between temporomandibular joint osteoarthrosis and osteoarthritis. Sci Rep 15, 23542 (2025). https://doi.org/10.1038/s41598-025-06045-2
  15. LIFEx-texture: Valibeiglou, Z., Islamian, J.P., Soleymani, Y. et al. Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies. BMC Med Imaging 25, 223 (2025). https://doi.org/10.1186/s12880-025-01774-2
  16. LIFEx-MTV: Samani I, Gheysens O, Regnier M, Collard A, André M, Van Den Neste E, Vander Borght T. Prognostic value of a simple distance index derived from PET maximum intensity projection. Front. Med.Sec. Nuclear Medicine. Volume 12 - 2025, https://doi.org/10.3389/fmed.2025.1565525
  17. LIFEx-Main: Zhou X, Yin K, Hong H, Yi H, Li L. Combined Predictive Value of GLIM-Defined Malnutrition and Preoperative Adipose Tissue 18F-FDG Uptake for Recurrence-Free Survival After Radical Gastrectomy in Patients with Gastric Cancer. Current Oncology. 2025; 32(6):363. https://doi.org/10.3390/curroncol32060363
  18. LIFEx-texture: Ge, F., Wu, T., Yang, X. et al. Feasibility analysis of metabolic parameters based on baseline 18F-FDG PET/CT to predict heterogeneity and recurrence of diffuse large B-cell lymphoma. Ann Hematol (2025). https://doi.org/10.1007/s00277-025-06409-8
  19. LIFEx-texture: Fasmer, K.E., Gulati, A., Lindås, S. et al. Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [18F]FDG PET primary tumor radiomics. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07335-7
  20. LIFEx-texture: Kotaro Ito, Ryoga Atsumi, Hirotaka Muraoka, Satoshi Tokunaga, Yuta Kohinata, Tomohiro Komatsu, Takashi Kaneda. Quantitative Assessment of the Parotid Gland Using Computed Tomography Texture Analysis to Detect Patients With Dyslipidemia. Volume22, Issue3, September 2025, e70011, https://doi.org/10.1002/osi2.70011
  21. LIFEx-MTV: Guo, W., Jiang, H., Wang, C. et al. Predictive value of [18F]FDG PET-derived parameters for microsatellite instability and prognosis in patients with colorectal cancer. Eur Radiol (2025). https://doi.org/10.1007/s00330-025-11732-9
  22. LIFEx-dynPET: Hui Yuan, Fanghu Wang, Yang Chen, Xiaoqiang Pan, Qing Zhang, Tao Sun, Lei Jiang. Kinetic Modeling and Parametric Imaging of 13N-NH3 in Treatment-Naïve Lung Cancer. Published June 13, 2025. Mol. Pharmaceutics 2025, XXXX, XXX, XXX-XXX https://doi.org/10.1021/acs.molpharmaceut.5c00602
  23. LIFEx-texture: Zhigang Chen, Huiying Lu, Ao Liu, Jia Weng, Lei Gan, Lina Zhou, Xiao Ding and Shicheng Li. TRANS: a prediction model for EGFR mutation status in NSCLC based on radiomics and clinical features. Respiratory Research (2025) 26:211 https://doi.org/10.1186/s12931-025-03287-6
  24. LIFEx-Texture: Salvatore Pezzino, Tonia Luca, Mariacarla Castorina, Stefano Puleo and Sergio Castorina. Current trends and emerging themes in utilizing artificial intelligence to enhance anatomical diagnostic accuracy and efficiency in radiotherapy. 2025 Prog. Biomed. Eng. 7 032002 https://doi.org/10.1088/2516-1091/adc85e
  25. LIFEx-Texture: Eva Jambon, Camille Courtine, Bruno Soulabaille, David Chéchin, Benjamin Robert, Joffrey Sarrazin,  Gaëlle Margue, Jean-Christophe Bernhard, Yann Le Bras, Amandine Crombé. Improving the Accuracy and Repeatability of Renal Stiffness Measurement With Magnetic Resonance Elastography: Influence of Vibration Frequency, Acquisition Orientation, and Postprocessing - A Phantom and Volunteer Study. NMR in Biomedicine, 2025; 38:e70054 https://doi.org/10.1002/nbm.70054
  26. LIFEx-MTV: Rexhep Durmo, Benedetta Donati, Louis Rebaud, Anne Segolene Cottereau, Alessia Ruffini, Maria Elena Nizzoli, Sabino Ciavarella, Maria Carmela Vegliante, Christophe Nioche, Michel Meignan, Francesco Merli, Annibale Versari, Alessia Ciarrocchi, Irene Buvat, Stefano Luminari. Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine‐treated, interimPET‐negative classical Hodgkin Lymphoma patients: A radio‐genomic study. Hematological Oncology. 2022;40:645–657. http://doi.org/10.1002/hon.3025
  27. LIFEx-Texture: Pellegrino, S.; Panico, M.; Bologna, R.; Morra, R.; Servetto, A.; Bianco, R.; Del Vecchio, S.; Fonti, R. Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors. Biomedicines 2025, 13, 1286. https://doi.org/10.3390/biomedicines13061286
  28. LIFEx-Texture: Kartal, A.S., Kartal, M.O., Gülleroğlu, N.B. et al. Texture analysis and metabolic parameters of 18F-FDG PET/CT to predict primary tumour response and prognosis of paediatric soft tissue sarcomas. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07359-z
  29. LIFEx-Texture: Lian A, Ketcherside T, Liu A, Han C, Al Feghali KA, Maniyedath A, Amini A, Ladbury C. Evaluation of the stability of radiomic features of non-irradiated organs utilizing fan-beam kilovoltage computed tomography. Med Phys. 2025 May 29. https:doi.org/10.1002/mp.17914. Epub ahead of print. PMID: 40438900.
  30. LIFEx-texture: Salvatore Pezzino et al 2025. Current trends and emerging themes in utilizing artificial intelligence to enhance anatomical diagnostic accuracy and efficiency in radiotherapy. Prog. Biomed. Eng. 7 032002. https://doi.org/10.1088/2516-1091/adc85e
  31. LIFEx-texture: Andres A. Kohan, S.A. Mirshahvalad, R. Hinzpeter, R. Kulanthaivelu, L. Avery, C. Ortega, U. Metser, A. Hope, P. Veit-Haibach. External Validation of a CT-Based Radiogenomics Model for the Detection of EGFR Mutation in NSCLC and the Impact of Prevalence in Model Building by Using Synthetic Minority Over Sampling (SMOTE): Lessons Learned, Academic Radiology, 2025, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2025.04.064
  32. LIFEx-MTV: Zheng, C., Miao, J., Xu, L. et al. Novel PET imaging biomarkers as predictors of postoperative recurrence in lung adenocarcinoma. BMC Cancer 25, 874 (2025). https://doi.org/10.1186/s12885-025-14263-0
  33. LIFEx-texture: Kim, M., Gu, W., Iwakawa, R.K. et al. Integrative Analysis of 18F-FDG PET Radiomics and mRNA Expression in Recurrent/Metastatic Oral Squamous Cell Carcinoma: A Cross-Sectional Study. Mol Imaging Biol (2025). https://doi.org/10.1007/s11307-025-02012-5
  34. LIFEx-texture-MTV: Kleiburg, F., Geus-Oei, L. F. de, Spijkerman, R., Noortman, W. A., Velden, F. H. P. van, Manohar, S., … Heijmen, L. (2025). Baseline PSMA PET/CT parameters predict overall survival and treatment response in metastatic castration-resistant prostate cancer patients. European Radiology. https://doi.org/10.1007/s00330-025-11360-3
  35. LIFEx-texture: Chen, L., Wang, J., Jin, C. et al. Initial [18F]FDG PET/CT study for survival evaluation of lung transplantation patients with interstitial lung disease. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07315-x
  36. LIFEx-MTV: Yao, X., Wang, H., Lei, X. et al. 18F-FDG PET/CT for predicting prognosis of B-cell non-Hodgkin lymphoma patients treated with chimeric antigen receptor T cells: the value of pre-infusion and M1 image. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07302-2
  37. LIFEx-texture: Shin H-B,  Sheen H,  Oh J-H,  Choi YE,  Sung K,  Kim HJ.  Evaluating feature extraction reproducibility across image biomarker standardization initiative-compliant radiomics platforms using a digital phantom. J Appl Clin Med Phys.  2025;e70110. https://doi.org/10.1002/acm2.70110
  38. LIFEx-texture: Xu L, Chen Z, Zhu D, Wang Y. The Application Status of Radiomics-Based Machine Learning in Intrahepatic Cholangiocarcinoma: Systematic Review and Meta-Analysis. J Med Internet Res 2025;27:e69906. https://doi.org/10.2196/69906 PMID: 40323647
  39. LIFEx-texture: Cvachovec, P., Bicu, A.S., Schmidt, R. et al. Longitudinal stability of HyperSightTM-CBCT based radiomic features in patients with CT guided adaptive SBRT for prostate cancer.Sci Rep 15, 15863 (2025). https://doi.org/10.1038/s41598-025-99920-x
  40. LIFEx-MTV: Zelda Paquier; Jennifer Dhont; Thomas Guiot; Rita Saude Conde; Francesco Sclafani; Alain Hendlisz. Development and Validation of an 18F-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography–Based Imaging Score to Predict 12-Week Life Expectancy in Advanced Chemorefractory Colorectal Cancer. JCO Clin Cancer Inform 9:e2400207. https://doi.org/10.1200/CCI-24-00207
  41. LIFEx-texture: Adarve-Castro, A., Soria-Utrilla, V., Castro-García, J.M. et al. Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning-based analysis of CT images. Radiol med (2025). https://doi.org/10.1007/s11547-025-02004-z
  42. LIFEx-texture: Peng, Y., Shi, M., Xiong, D. et al. Preoperative assessment and prognostic prediction of gastric cancer patients with peritoneal metastasis using 18F-FDG PET/CT before conversion surgery. EJNMMI Res 15, 46 (2025). https://doi.org/10.1186/s13550-025-01244-4
  43. LIFEx-texture: Gemmell, A.J., Brown, C.M., Ray, S. et al. Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions. EJNMMI Phys 12, 40 (2025). https://doi.org/10.1186/s40658-025-00749-0
  44. LIFEx-Texture: Bilgin E, Bilgin EY, Bayrak A, Torenek S. Advanced Radiomics for Predicting Extracapsular Invasion of Metastatic Axillary Lymph Nodes in Breast Cancer Patients Using CT Imaging. J Coll Physicians Surg Pak 2025; 35(04):415-419 https://doi.org/10.29271/jcpsp.2025.04.415
  45. LIFEx-main: Stien, G., Zinsz, A., Ahrari, S. et al. PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): methodological application in [18F]-FDOPA PET imaging.EJNMMI Res 15, 44 (2025). https://doi.org/10.1186/s13550-025-01239-1 
  46. LIFEx-texture: Yali Cui, Yao Li, Wenhao Hu, Zhifang Wu, Sijin Li, Hongliang Wang. Evaluating ΔMTV%, ΔDmax%, and %ΔSUVmax of 18F-FDG PET/CT for mid-treatment efficacy and prognosis in diffuse large B-cell lymphoma. Discover Oncology; New York Vol. 16, Iss. 1,  (Dec 2025): 411. https://doi.org/10.1007/s12672-025-02126-w
  47. LIFEx-texture: Melek YAKAR, Durmus ETIZ, Eyyup GULBANDILAR, Kerem DURUER, Ergin ERDEN. Radiomic-Assistant Response Prediction to Stereotactic Body Radiotherapy in Early Stage Lung Cancer. UHOD Number: 1 Volume: 35 Year: 2025. https://doi.org/10.4999/uhod.257762
  48. LIFEx-texture: Zhao, J., Zhao, W., Chen, M. et al. 18F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study. J Cancer Res Clin Oncol 151, 125 (2025). https://doi.org/10.1007/s00432-025-06172-3
  49. LIFEx-texture: Cui, Y., Li, Y., Hu, W. et al. Evaluating ΔMTV%, ΔDmax%, and %ΔSUVmax of 18F-FDG PET/CT for mid-treatment efficacy and prognosis in diffuse large B-cell lymphoma. Discov Onc 16, 411 (2025). https://doi.org/10.1007/s12672-025-02126-w
  50. LIFEx-texture: Takeharu Kiso, Yukinori Okada, Satoru Kawata, Kouta Shichiji, Eiichiro Okumura, Noritaka Hatsumi, Ryohei Matsuura, Masaki Kaminaga, Hikaru Kuwano, Erika Okumura. Ultrasound-based radiomics and machine learning for enhanced diagnosis of knee osteoarthritis: Evaluation of diagnostic accuracy, sensitivity, specificity, and predictive value. European Journal of Radiology Open, Volume 14, 2025, 100649, ISSN 2352-0477, https://doi.org/10.1016/j.ejro.2025.100649
  51. LIFEx-main: Yexin Su, Hongyue Zhao, Zhehao Lyu, Peng Xu, Ziyue Zhang, Huiting Zhang, Mengjiao Wang, Lin Tian, Peng Fu. Quantification of Intratumoral Heterogeneity Based on Habitat Analysis for Preoperative Assessment of Lymphovascular Invasion in Colorectal Cancer. Academic Radiology, 2025, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2025.03.014
  52. LIFEx-texture: Mengye Peng, Menglu Wang, Xinyue Yang, Yanmei Wang, Lizhi Xie, Wenxin An, Fan Ge, Chen Yang, Kezheng Wang. Prediction of PD-L1 Expression in NSCLC patients Using PET/CT Radiomics and Prognostic Modeling for Immunotherapy in PD-L1-Positive NSCLC Patients. Clinical Radiology. 2025. 106915, ISSN 0009-9260, https://doi.org/10.1016/j.crad.2025.106915
  53. LIFEx-MTV: Hu, J., Cheng, R., Quan, M. et al. Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07215-0
  54. LIFEx-texture: Liao Z, Luo D, Tang X, Huang F, Zhang X. MRI-based radiomics for predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis. Front Oncol. 2025 Mar 10;15:1550838. https://doi.org/10.3389/fonc.2025.1550838 PMID: 40129922; PMCID: PMC11930822.
  55. LIFEx-main: Shrestha et al., (2025). RT-utils: A Minimal Python Library for RT-struct Manipulation. Journal of Open Source Software, 10(107), 7361, https://doi.org/10.21105/joss.07361
  56. LIFEx-texture: Isemoto, K., Waseda, Y., Fujiwara, M., Kimura, K., Hirahara, D., Saho, T., Takaya, E., Arita, Y., Kwee, T. C., Fukuda, S., Tanaka, H., Yoshida, S., & Fujii, Y. (2025). Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer. Diagnostics, 15(7), 801. https://doi.org/10.3390/diagnostics15070801
  57. LIFEx-texture: Esat Kaba, Hande Melike Bülbül, Mehmet Kıvrak, Nur Hürsoy. Multisequence combined magnetic resonance imaging radiomics model to noninvasively predict nuclear grade of clear cell renal cell carcinoma: interpretable model development. Rev Assoc Med Bras. 2025;71(1):e20241012. https://doi.org/10.1590/1806-9282.20241012
  58. LIFEx-texture: J. Wang, W. Tang, J. Zhu, J. Cui, Y. Chen, M. Gu, H. Xu, M. Zhan, Q. Chen, B. Xu. Predicting the pathological subdiagnosis of benign prostatic hyperplasia with MRI radiomics: A noninvasive approach. VIEW. 2025, 20240092. https://doi.org/10.1002/VIW.20240092
  59. LIFEx-texture: Magnin Cheryl Y., Lauer David, Ammeter Michael, Gote-Schniering Janine. From images to clinical insights: an educational review on radiomics in lung diseases. 21:1, 230225. https://doi.org/10.1183/20734735.0225-2023
  60. LIFEx-texture:  Sriramganesh Gokavarapu, K Venkata Rao, Gorla Srinivas, Dasari Siva Krishna, Enhancing Lung Cancer Detection: Optimizing Deep Learning with Convolutional Block Attention Module, Journal of Artificial Intelligence and Technology (2025), DOI: https://doi.org/10.37965/jait.2025.0668
  61. LIFEx-texture: Liu, H., Meng, X., Wang, G. et al. Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT. Ann Nucl Med(2025). https://doi.org/10.1007/s12149-025-02034-7
  62. LIFEx-texture: Alireza Safarian, Seyed Ali Mirshahvalad, Hadi Nasrollahi, Theresa Jung, Christian Pirich, Hossein Arabi, Mohsen Beheshti. Impact of [18F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role, Seminars in Nuclear Medicine, 2025, ISSN 0001-2998, https://doi.org/10.1053/j.semnuclmed.2025.02.006
  63. LIFEx-texture: Liu, H., Meng, X., Wang, G. et al. Differentiating second primary lung cancer from pulmonary metastasis in patients of single solitary pulmonary lesion with extrapulmonary tumor using multiparametric analysis of FDG PET/CT. Ann Nucl Med(2025). https://doi.org/10.1007/s12149-025-02034-7
  64. LIFEx-texture: Tan Qianqian, Teng Yue, Sun Yiwen, Xu Pei, Xu Yiduo, Chen Qiaoliang, He Jian, Lai Ruihe. Prognostic value of 18F-FDG PET/CT metabolic parameters TMTV in patients with stage Ⅳ endometrial cancer[J]. Int J Radiat Med Nucl Med. https://doi.org/10.3760/cma.j.cn121381-202403022-00506
  65. LIFEx-texture: Lazaros K, Adam S, Krokidis MG, Exarchos T, Vlamos P, Vrahatis AG. Non-Invasive Biomarkers in the Era of Big Data and Machine Learning. Sensors. 2025; 25(5):1396. https://doi.org/10.3390/s25051396
  66. LIFEx-texture: Yang, Mao et al. Multimodal integration of liquid biopsy and radiology for the noninvasive diagnosis of gallbladder cancer and benign disorders. Cancer Cell, Volume 43, Issue 3, 398 - 412.e4. https://www.cell.com/cancer-cell/fulltext/S1535-6108(25)00063-7
  67. LIFEx-texture: Lazaros K, Adam S, Krokidis MG, Exarchos T, Vlamos P, Vrahatis AG. Non-Invasive Biomarkers in the Era of Big Data and Machine Learning. Sensors. 2025; 25(5):1396. https://doi.org/10.3390/s25051396
  68. LIFEx-texture: Peng, M., Wang, M., An, W. et al. Predictive classification of lung cancer pathological based on PET/CT radiomics. Jpn J Radiol (2025). https://doi.org/10.1007/s11604-025-01742-4
  69. LIFEx-texture: Gennaro, N.; Soliman, M.; Borhani, A.A.; Kelahan, L.; Savas, H.; Avery, R.; Subedi, K.; Trabzonlu, T.A.; Krumpelman, C.; Yaghmai, V.; et al. Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal Cancer. Tomography 2025, 11, 20. https://doi.org/10.3390/tomography11030020
  70. LIFEx-Main: Ronga MG, Gesualdi F, Bonfrate A, et al. Comparison of secondary radiation dose between pencil beam scanning and scattered delivery for proton and VHEE radiotherapy. Med Phys. 2025;1-10. https://doi.org/10.1002/mp.17700
  71. Zhang, Yichi and Xue, Le and Zhang, Wenbo and Li, Lanlan and Liu, Yuchen and Jiang, Chen and Cheng, Yuan and Qi, Yuan. SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images. https://arxiv.org/pdf/2502.14351
  72. LIFEx-MTV: Albano, D., Temponi, A., Bertagna, F. et al. The prognostic role of staging [18F]PSMA-1007 PET/CT volumetric and dissemination features in prostate cancer. Ann Nucl Med (2025). https://doi.org/10.1007/s12149-025-02026-7
  73. LIFEx-main: Liu, Y., Wang, J., Du, B. et al. Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT. Cancer Imaging 25, 17 (2025). https://doi.org/10.1186/s40644-025-00834-8
  74. LIFEx-texture: Pellegrino S, Fonti R, Morra R, Di Donna E, Servetto A, Bianco R, Del Vecchio S. Prognostic Value of Tumor Dissemination (Dmax) Derived from Basal 18F-FDG Positron Emission Tomography/Computed Tomography in Patients with Advanced Non-Small-Cell Lung Cancer. Biomedicines. 2025; 13(2):477. https://www.mdpi.com/2227-9059/13/2/477
  75. LIFEx-texture: Dwivedi P, Sagar S, AK Jha, S Choudhury, Venkatesh R. Robustness of 18F-FDG PET Radiomic Features in Lung Cancer: Impact of Advanced Reconstruction Algorithm.  J. Nucl. Med. Technol. 2025/02/05. http://doi.org/10.2967/jnmt.124.268252
  76. LIFEx-texture: Filippi, L., Bianconi, F., Minestrini, M. et al. Multi-centre data harmonisation applied to heart-to-mediastinum quantification in parkinsonism (ITA-MIBG): a cross-calibration phantom study with tube and bottle. Clin Transl Imaging (2025). https://doi.org/10.1007/s40336-025-00681-4
  77. LIFEx-texture: Jiang, C., Jiang, Z., Zhang, Z. et al. An explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-025-07090-9
  78. LIFEx-texture: Beaumont H, Iannessi A, Thinnes A, Jacques S, Quintás-Cardama A. Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study. Cancers. 2025; 17(3):463. https://doi.org/10.3390/cancers17030463
  79. LIFEx-texture: Jiang, C., Qian, C., Jiang, Q. et al. Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT. BMC Med 23, 49 (2025). https://doi.org/10.1186/s12916-025-03893-7
  80. LIFEx-MTV: Wen, Z., Gao, X., Wu, Q. et al. Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP. BMC Cancer 25, 128 (2025). https://doi.org/10.1186/s12885-025-13507-3
  81. LIFEx-MTV-texture: Kleiburg, F., de Geus-Oei, LF., Spijkerman, R. et al. Baseline PSMA PET/CT parameters predict overall survival and treatment response in metastatic castration-resistant prostate cancer patients. Eur Radiol (2025). https://doi.org/10.1007/s00330-025-11360-3
  82. LIFEx-texture: Qi, L., Li, X., Ni, J. et al. Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA199. Cancer Cell Int 25, 19 (2025). https://doi.org/10.1186/s12935-025-03639-8
  83. LIFEx-texture: Ahrari, S., Zaragori, T., Zinsz, A. et al. Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma. Eur J Nucl Med Mol Imaging (2025). https://doi.org/10.1007/s00259-024-07053-6
  84. LIFEx-texture: Captier, N., Lerousseau, M., Orlhac, F. et al. Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer. Nat Commun 16, 614 (2025). https://doi.org/10.1038/s41467-025-55847-5
  85. LIFEx-texture: Zhou Y, Zhou XY, Xu YC, Ma XL, Tian R. Radiomics based on 18 F-FDG PET for predicting treatment response and prognosis in newly diagnosed diffuse large B-cell lymphoma patients: do lesion selection and segmentation methods matter? Quant Imaging Med Surg 2025;15(1):103-120. https://doi.org/10.21037/qims-24-585
  86. LIFEx-texture: Bei-Hui Xue, Shuang-Li Chen, Jun-Ping Lan, Li-Li Wang, Jia-Geng Xie, Xiang-wu Zheng, Liang-Xing Wang, Kun Tang. Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study, Academic Radiology, 2025, ISSN 1076-6332. https://doi.org/10.1016/j.acra.2024.12.038

Review (7):

  1. LIFEx-texture: Namdar, K., Wagner, M.W., Ertl-Wagner, B.B. et al. Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines. BMC Med Imaging 25, 312 (2025). https://doi.org/10.1186/s12880-025-01855-2
  2. LIFEx-texture: Zhang L, Li D, Su T, Xiao T, Zhao S. Effectiveness of Radiomics-Based Machine Learning Models in Differentiating Pancreatitis and Pancreatic Ductal Adenocarcinoma: Systematic Review and Meta-Analysis. J Med Internet Res 2025;27:e72420 https://doi.org/10.2196/72420
  3. LIFEx-texture: Chilaca-Rosas, M.F., Contreras-Aguilar, M.T., Pallach-Loose, F. et al. Systematic review and epistemic meta-analysis to advance binomial AI-radiomics integration for predicting high-grade glioma progression and enhancing patient management. Sci Rep 15, 16113 (2025). https://doi.org/10.1038/s41598-025-98058-0
  4. LIFEx-texture: Mohammadzadeh I, Hajikarimloo B, Niroomand B, Faizi N, Faizi N, Habibi MA, Mohammadzadeh S, Soltani R, Artificial Intelligence based radiomic model in Craniopharyngiomas: A Systematic Review and Meta-Analysis on Diagnosis, Segmentation, and Classification, World Neurosurgery (2025), doi: https://doi.org/10.1016/j.wneu.2025.124050
  5. LIFEx-texture: Asaf Raza, Antonella Guzzo, Michele Ianni, Rosamaria Lappano, Alfredo Zanolini, Marcello Maggiolini, Giancarlo Fortino. Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis. Computer Methods and Programs in Biomedicine. 2025, 108768, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2025.108768
  6. LIFEx-texture: Salvatore Pezzino, Tonia Luca, Mariacarla Castorina, Stefano Puleo, Sergio Casorina. Current Trends and Emerging Themes in Utilizing Artificial Intelligence to Enhance Anatomical Diagnostic Accuracy and Efficiency in Radiotherapy.  Salvatore Pezzino et al 2025 Prog. Biomed. Eng. https://doi.org/10.1088/2516-1091/adc85e
  7. LIFEx-texture: Keshavarz, Pedram et al. Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: A systematic review of efficacy. European Journal of Radiology, Volume 0, Issue 0, 111948. https://doi.org/10.1016/j.ejrad.2025.111948

Others (18):

  1. LIFEx-texture: Mohammad R. Salmanpour, and al. Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging. https://arxiv.org/pdf/2507.16065
  2. LIFEx-texture: Luboš Řehounek, Přírodovědecká fakulta, Masarykova univerzita, Ústav fyziky kondenzovaných látek. Thesis. Deep Learning in PET/CT Image Processing. 2024 (link)
  3. LIFEx-texture: Aron Österlund. Bachelor’s thesis 2025. A systematic review on software tools for CT- and MRI-based body composition analysis (link)
  4. LIFEx-texture: Charlotte Loisel, Julie Auriac, Fanny Orlhac, Manuel Pires, Romain-david Seban, Josef Yu, Marcus Hacker, Lalith kumar Shiyam sundar, Thomas Beyer and Irene Buvat. Reproducibility of the metabolic network identified using whole-body [18F]FDG-PET/CT in healthy women. Journal of Nuclear Medicine June 2025, 66 (supplement 1) 251365; https://jnm.snmjournals.org/content/66/supplement_1/251365.abstract
  5. LIFEx-texture: Charlotte Loisel, Julie Auriac, Fanny Orlhac, Manuel Pires, Romain-david Seban, Josef Yu, Marcus Hacker, Lalith kumar Shiyam sundar, Thomas Beyer and Irene Buvat. Comparison of inter-organ metabolic networks between healthy females and breast cancer patients based on whole-body [18F]FDG-PET/CT. Journal of Nuclear Medicine June 2025, 66 (supplement 1) 251361; https://jnm.snmjournals.org/content/66/supplement_1/251361.abstract
  6. LIFEx-texture: Hugo Lopez, Hornella Fokem fosso, Che M'vondo, Aurélie Bertaut, Magali Rouffiac, Jihane Boustani, Fanny Orlhac, Christophe Nioche, Gilles Crehange and Irene Buvat. Clinico-radiomic models using pre-treatment [18F]-FDG PET/CT predict outcome in esophageal cancers treated with exclusive chemoradiotherapy. Journal of Nuclear Medicine June 2025, 66 (supplement 1) 251588; https://jnm.snmjournals.org/content/66/supplement_1/251588.abstract
  7. LIFEx-texture: Monica Luo, Hamid Abdollahi, Fereshteh Yousefirizi, Sara Harsini and Arman Rahmim. Lung Cancer Radiopathomics: Correlating PET/CT Radiomics and Histopathological Features in Lung Adenocarcinoma and Squamous Cell Carcinoma. Journal of Nuclear Medicine June 2025, 66 (supplement 1) 251687; https://jnm.snmjournals.org/content/66/supplement_1/251687.abstract
  8. LIFEx-texture: Pooja Dwivedi, Sagar Barage, Ashish Jha, Rajshri Singh, Sayak Choudhury, Archi Agrawal and Venkatesh Rangarajan. Effect of ComBat Harmonization in PET-Based Radiomic Biomarkers for Tumor Phenotyping. Journal of Nuclear Medicine June 2025, 66 (supplement 1) 25133; https://jnm.snmjournals.org/content/66/supplement_1/25133.abstract
  9. LIFEx-texture: Tiancheng Li. Development and Validation of a Machine Learning Model Using Preoperative 18F-PSMA PET/MR to Predict Biochemical Recurrence-Free Survival After Radical Prostatectomy. Journal of Nuclear Medicine June 2025, 66 (supplement 1) 251341; https://jnm.snmjournals.org/content/66/supplement_1/251341.abstract
  10. LIFEx-Texture: Hubert Beaumont, Emilie Khayat, Alexandre Thinnes, and Antoine Iannessi. Technical performance of the L3 Skeletal Muscle Index in CT.  Journal of Clinical Oncology Volume 43, Number 16_suppl https://doi.org/10.1200/JCO.2025.43.16_suppl.e2407
  11. LIFEx-Texture: Filippo Gustavo Dall'Olio, Wael Zrafi, Adsaya Rathakrishnan, Maeva Moreau, Rebecca Ibrahim, Matthieu Texier, Adrien Laville, Damien Vasseur, Patrick Saulnier, Amaury Daste, Karim Benihoud, Pierre Busson, Ivan Bieche, Clemence Toullec, Mariana Iacob, Roger Sun, Anne Auperin, Laure Monard, Ludovic Lacroix, and Caroline Even. ctDNA tumor fraction (TF) to predict response to nivolumab in recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC): An analysis of the multicentric phase 2 TOPNIVO trial. Journal of Clinical Oncology. Volume 43, Number 16_suppl https://doi.org/10.1200/JCO.2025.43.16_suppl.6047
  12. LIFEx-texture: Lorenzo Tucci, Giulio Vara, Antonio De Leo, Kassiani Skordilis, Lisa James, Cristina Mosconi, Balraj Dhesi, Dario De Biase, Juliane Lippert, Abubaker Mohamed, Guido Di Dalmazi, Cristina L Ronchi. Radiogenomics pilot study in adrenocortical carcinoma: assessing the relationship between genetic background and computerized tomography texture. Endocrine Abstracts (2025) 110 P464.  https://doi.org/10.1530/endoabs.110.P464
  13. LIFEx-texture: Tan Qianqian, Zhao Lianjun, He Jian, Lai Ruihe. Study on the assessment of intratumoral metabolic heterogeneity parameters of metastatic lesions by 18F-FDG PET/CT for predicting the prognosis of patients with malignant melanoma[J]. Int J Radiat Med Nucl Med, 2025, 49(2): 78-86. DOI: 10.3760/cma.j.cn121381-202408031-00513
  14. LIFEx-texture: HangYu Watson, Seher Berzingi, Karthik Seetharam, Samuel A. Mensah, Syed Ahmad and Brijesh D. Patel. Myocardial tissue texture improves diagnostic accuracy of echocardiogram to diagnosis of hypertrophic cardiomyopathy. JACC. 2025 Apr, 85 (12_Supplement) 2575. https://www.jacc.org/doi/abs/10.1016/S0735-1097%2825%2903059-1
  15. LIFEx-texture:TERZI ATHINA Marina. Radiogenomic Analysis of Lung Cancer. MSc program “Biomedical Engineering and Technology”. https://polynoe.lib.uniwa.gr/xmlui/bitstream/handle/11400/8616/Terzi_bmet2305.pdf
  16. LIFEx-texture: Yunus Soleymani, Farahnaz Aghahosseini, Peyman Sheikhzadeh. Correlation of radiomics features extracted from nuclear medicine images with lesion metabolism in patients with colon cancer. February 2025Tehran University Medical Journal 82(5). link
  17. LIFEx-texture: Malhaire C thesis. Optimization of the Prediction of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer by Breast MRI : Contributions of Semantic Descriptors, Radiomics, and Segmentation Methods. HAL Id: tel-04931114. https://theses.hal.science/tel-04931114v1
  18. LIFEx-texture: S. Gülbahar Ates, B. B. Demirel, E. Kekilli, E. Öztürk, G. Uçmak. Primary Tumor Heterogeneity on Pre-treatment [68Ga]Ga-PSMA PET/CT for the Prediction of Biochemical Recurrence in Prostate Cancer. Revista española de medicina nuclear e imagen molecular, ISSN 2253-654X, Vol. 43, Nº. 6 (Noviembre-Diciembre), 2024, págs. 4-4. https://dialnet.unirioja.es/ejemplar/686513

Publications 2024 - Journal papers (220)

(165)

  1. LIFEx-MTV: Albano D, Ravanelli M, Durmo R, Versari A, Filice A, Rizzo A, Racca M, Pizzuto DA, Bertagna F and Annunziata S (2024) Semiquantitative 2-[18F]FDG PET/CT-based parameters role in lymphoma. Front. Med. 11:1515040. https://doi.org/10.3389/fmed.2024.1515040
  2. LIFEx-main: Kim, JY., Lee, C., Park, Y.L. et al. Diagnostic criteria for temporomandibular joint osteoarthritis using standardized uptake value in single-photon emission computed tomography–computed tomography. Sci Rep 14, 31569 (2024). https://doi.org/10.1038/s41598-024-71639-1
  3. LIFEx-texture: Huang L, Kong W, Luo Y, Xie H, Liu J, Zhang X, Zhang G. Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning. Front Oncol. 2024 Dec 13;14:1458374. https://doi.org/10.3389/fonc.2024.1458374. PMID: 39735601; PMCID: PMC11671303.
  4. LIFEx-texture: Nakajo, M., Hirahara, D., Jinguji, M. et al. Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01722-0
  5. LIFEx-texture: Ali, M.A., Zahra, O.S., Morsi, M.I. et al. Predictive role of [18F]FDG PET-CT radiomic parameters for KRAS/BRAF/EGFR mutations in metastatic colorectal cancer patients.EJNMMI Rep. 8, 42 (2024). https://doi.org/10.1186/s41824-024-00233-5
  6. LIFEx-texture: Talebi A,  Bitarafan-Rajabi A,  Alizadeh-asl A, et al.  Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer. J Appl Clin Med Phys.  2024;e14614. https://doi.org/10.1002/acm2.14614
  7. LIFEx-texture: Furui Duan, Minghui Zhang, Chunyan Yang, Xuewei Wang, Dalong Wang. Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From 18F-FDG PET/CT Based on Interpretable Machine Learning. Academic Radiology, 2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.11.037
  8. LIFEx-texture: Berti, V., Fasciglione, E., Charpiot, A. et al. Deciphering 18F-DOPA uptake in SDH-related head and neck paragangliomas: a radiomics approach. J Endocrinol Invest (2024). https://doi.org/10.1007/s40618-024-02515-y
  9. LIFEx-texture: Bo Zhao, Ya-Qi Wang, Hai-Tao Zhu, Xiao-Ting Li, Yan-Jie Shi, Ying-Shi Sun. Integrating Tumour and Lymph Node Radiomics Features for Predicting Disease-free Survival in Locally Advanced Esophageal Squamous Cell Cancer After Neoadjuvant Chemotherapy and Complete Resection, European Journal of Surgical Oncology, 2024, 109547, ISSN 0748-7983, https://doi.org/10.1016/j.ejso.2024.109547
  10. LIFEx-texture: J. Fields et al., "CEM Radiomics for Distinguishing Benign vs Malignant Lesions in Patients with Invasive Breast Cancer or Benign Breast Lesions," 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM), Antigua, Guatemala, 2024, pp. 1-8, https://doi.org/10.1109/SIPAIM62974.2024.10783603
  11. LIFEx-texture: Albano, D., Bianchetti, N., Talin, A., Dondi, F., Re, A., Tucci, A. and Bertagna, F. (2025), Prognostic Role of Pretreatment Tumor Burden and Dissemination Features From 2-[18F]FDG PET/CT in Advanced Mantle Cell Lymphoma. Hematological Oncology, 43: e70009. https://doi.org/10.1002/hon.70009
  12. LIFEx-texture: Rongqin Fan, Xueqin Long, Xiaoliang Chen, Yanmei Wang, Demei Chen, Rui Zhou. The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with 68Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma, Academic Radiology, 2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.11.034
  13. LIFEx-texture: Luo Y, Li Y, Yang Z, Zhang Y, Yu H, Zhao Z, Yu K, Guo Y, Wang X, Yang N, Zhang Y, Zheng T, Zhou J. A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine. BMC Cancer. 2024 Dec 5;24(1):1495. https://doi.org/10.1186/s12885-024-13266-7. PMID: 39639258; PMCID: PMC11619205.
  14. LIFEx-texture: Kim H-S. Image Biomarker Analysis of Ultrasonography Images of the Parotid Gland for Baseline Characteristic Establishment with Reduced Shape Effects. Applied Sciences. 2024; 14(23):11041. https://doi.org/10.3390/app142311041
  15. LIFEx-texture: Xia, H., Yu, J., Nie, K. et al. CT radiomics and human-machine hybrid system for differentiating mediastinal lymphomas from thymic epithelial tumors. Cancer Imaging 24, 163 (2024). https://doi.org/10.1186/s40644-024-00808-2
  16. LIFEx-texture: Wu, M., Wang, P., Cheng, H. et al. Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma. Int J Legal Med (2024). https://doi.org/10.1007/s00414-024-03374-1
  17. LIFEx-texture: Kang, Yk., Ha, S., Jeong, J.B. et al. The value of PET/CT radiomics for predicting survival outcomes in patients with pancreatic ductal adenocarcinoma. Sci Rep 14, 28958 (2024). https://doi.org/10.1038/s41598-024-77022-4
  18. LIFEx-Main: Han, Y., Wang, G., Zhang, J. et al. The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma. EJNMMI Res 14, 115 (2024). https://doi.org/10.1186/s13550-024-01182-7
  19. LIFEx-texture: Mori, Y.; Ren, H.; Mori, N.; Watanuki, M.; Hitachi, S.; Watanabe, M.; Mugikura, S.; Takase, K. Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma. Diagnostics 2024, 14, 2562. https://doi.org/10.3390/diagnostics14222562
  20. LIFEx-texture: Zhou, Y., Zhou, J., Cai, X. et al. Integrating 18F-FDG PET/CT radiomics and body composition for enhanced prognostic assessment in patients with esophageal cancer.BMC Cancer 24, 1402 (2024). https://doi.org/10.1186/s12885-024-13157-x
  21. LIFEx-texture: Bini, F.; Missori, E.; Pucci, G.; Pasini, G.; Marinozzi, F.; Forte, G.I.; Russo, G.; Stefano, A. Preclinical Implementation of matRadiomics: A Case Study for Early Malformation Prediction in Zebrafish Model. J. Imaging 2024, 10, 290. https://doi.org/10.3390/jimaging10110290
  22. LIFEx-texture: Bianconi, F.; Salis, R.; Fravolini, M.L.; Khan, M.U.; Filippi, L.; Marongiu, A.; Nuvoli, S.; Spanu, A.; Palumbo, B. Radiomics Features from Positron Emission Tomography with [18F] Fluorodeoxyglucose Can Help Predict Cervical Nodal Status in Patients with Head and Neck Cancer. Cancers 2024, 16, 3759. https://doi.org/10.3390/cancers16223759
  23. LIFEx-texture: Ali, Fayzan; Baldelomar, Edwin; Charlton, Jennifer R.; Wahl, Richard L.; Marklin, Gary F.; Bennett, Kevin M. Radiomic Texture Features in CT Images of Kidneys in Ventilated Deceased Donors Predict Delayed Graft Function: TH-PO788. Journal of the American Society of Nephrology 35(10S):10.1681/ASN.20242nnbk6de, October 2024. https://doi.org/10.1681/ASN.20242nnbk6de
  24. LIFEx-MTV: Qiu YJ, Zhou LL, Li J, Zhang YF, Wang Y, Yang YS. The repeatability and consistency of different methods for measuring the volume parameters of the primary rectal cancer on diffusion weighted images. Front Oncol. 2023 Mar 9;13:993888. https://doi.org/10.3389/fonc.2023.993888. PMID: 36969078; PMCID: PMC10034158.
  25. LIFEx-texture: Mariani, I.; Maino, C.; Giandola, T.P.; Franco, P.N.; Drago, S.G.; Corso, R.; Talei Franzesi, C.; Ippolito, D. Texture Analysis and Prediction of Response to Neoadjuvant Treatment in Patients with Locally Advanced Rectal Cancer. Gastrointest. Disord. 2024, 6, 858–870. https://doi.org/10.3390/gidisord6040060
  26. LIFEx-texture: Yang, T., Sun, Z., Shi, Y. et al. Development and validation of prognostic models based on 18F-FDG PET radiomics, metabolic parameters, and clinical factors for elderly DLBCL patients. Ann Hematol (2024). https://doi.org/10.1007/s00277-024-06071-6
  27. LIFEx-texture: Malik, M.M.U.D.; Alqahtani, M.M.; Hadadi, I.; Kanbayti, I.; Alawaji, Z.; Aloufi, B.A. Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications. Diagnostics 2024, 14, 2459. https://doi.org/10.3390/diagnostics14212459
  28. LIFEx-texture: Jafari, E., Dadgar, H., Zarei, A. et al. The role of [68Ga]Ga-PSMA PET/CT in primary staging of newly diagnosed prostate cancer: predictive value of PET-derived parameters for risk stratification through machine learning. Clin Transl Imaging (2024). https://doi.org/10.1007/s40336-024-00666-9
  29. LIFEx-texture: Piaopiao Ying, Jiajing Chen, Yinchai Ye, Chang Xu, Jianzhong Ye. Prognostic Value of Computed Tomography-Measured Visceral Adipose Tissue in Patients with Pulmonary Infection Caused by Carbapenem-Resistant Klebsiella pneumoniae. Infection and Drug Resistance 2024:17 4741–4752. https://doi.org/10.2147/IDR.S479302
  30. LIFEx-texture: Ogün Bülbül, Demet Nak, Sibel Göksel; Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning. Urol Int 2024; https://doi.org/10.1159/000541628
  31. LIFEx-texture: Liping Yang, Hongchao Ding, Xing Gao, Yuchao Xu, Shichuan Xu and Kezheng Wang. Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics. Yang et al. BMC Cancer (2024) 24:1316 https://doi.org/10.1186/s12885-024-13031-w
  32. LIFEx-texture: Daniel Stocker, Stefanie Hectors, Brett Marinelli, Guillermo Carbonell, Octavia Bane, Miriam Hulkower, Paul Kennedy, Weiping Ma, Sara Lewis, Edward Kim, Pei Wang, Bachir Taouli. Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI‑based machine learning approach. Abdominal Radiology, accepted: 17 September 2024
    https://doi.org/10.1007/s00261-024-04606-z
  33. LIFEx-texture: Kallos-Balogh P, Vas NF, Toth Z, Szakall S, Szabo P, Garai I, et al. (2024) Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom. PLoS ONE 19(10): e0309540. https://doi.org/10.1371/journal.pone.0309540
  34. LIFEx-texture: Barioni, E.D.; Lopes, S.L.P.d.C.; Silvestre, P.R.; Yasuda, C.L.; Costa, A.L.F. Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions. J. Imaging 2024, 10, 263. https://doi.org/10.3390/jimaging10110263
  35. LIFEx-texture: Gelardi, F.; Cavinato, L.; De Sanctis, R.; Ninatti, G.; Tiberio, P.; Rodari, M.; Zambelli, A.; Santoro, A.; Fernandes, B.; Chiti, A.; et al. The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort. Diagnostics 2024, 14, 2312. https://doi.org/10.3390/diagnostics14202312
  36. LIFEx-texture: Michel Destine and Alain Seret. Quantitative assessment of kidney split function and mean transit time in healthy patients using dynamic 18 F‑FDG PET/MRI studies with denoising and deconvolution methods making use of Legendre polynomials. Destine and Seret EJNMMI Reports (2024) 8:33. https://doi.org/10.1186/s41824‑024‑00221‑9
  37. LIFEx-texture: Soleymani Y, Valibeiglou Z, Fazel Ghaziani M, Jahanshahi A, Khezerloo D. Radiomics reproducibility in computed tomography through changes of ROI size, resolution, and hounsfield unit: A phantom study. Radiography (Lond). 2024 Oct 17;30(6):1629-1636. https://doi.org/10.1016/j.radi.2024.10.003. Epub ahead of print. PMID: 39423630.
  38. LIFEx-texture: Rajgor AD, Kui C, McQueen A, Cowley J, Gillespie C, Mill A, Rushton S, Obara B, Bigirumurame T, Kallas K, O'Hara J, Aboagye E, Hamilton DW. Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study. J Laryngol Otol. 2024 Jun;138(6):685-691. https://doi.org/10.1017/S0022215123002372. Epub 2023 Dec 14. PMID: 38095096; PMCID: PMC11096831.
  39. LIFEx-texture: Mahmoud M, Lin KH, Lee RC, Liu CA. Assessment of Y-90 Radioembolization Treatment Response for Hepatocellular Carcinoma Cases Using MRI Radiomics. Mol Imaging Radionucl Ther. 2024 Oct 7;33(3):156-166. https://doi.org/10.4274/mirt.galenos.2024.59365. PMID: 39373149.
  40. LIFEx-texture: Ran CQ, Su Y, Li J, Wu K, Liu ZL, Yang Y, Zhang MX, Yuan G, Yu XF, He WT. Epicardial adipose tissue volume highly correlates with left ventricular diastolic dysfunction in endogenous Cushing's syndrome. Ann Med. 2024 Dec;56(1):2387302. https://doi.org/10.1080/07853890.2024.2387302. Epub 2024 Aug 5. PMID: 39101236; PMCID: PMC11302473.
  41. LIFEx-texture: Mahmoud M, Lin K, Lee R, Liu C. Treatment Response for Hepatocellular Carcinoma Cases Using MRI Radiomics. Mol Imaging Radionucl Ther. 2024 Oct;33(3):156-166. https://doi.org/10.4274/mirt.galenos.2024.59365
  42. LIFEx-texture: Crimì, F., Turatto, F., D’Alessandro, C. et al. Texture analysis can predict response to etoposide-doxorubicin-cisplatin in patients with adrenocortical carcinoma. J Endocrinol Invest (2024). https://doi.org/10.1007/s40618-024-02476-2
  43. LIFEx-main: Zhang, X., Xiang, Z., Wang, F. et al. Feasibility of shortening scan duration of 18F-FDG myocardial metabolism imaging using a total-body PET/CT scanner. EJNMMI Phys 11, 83 (2024). https://doi.org/10.1186/s40658-024-00689-1
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  114. LIFEx-texture: Hinzpeter, R.; Mirshahvalad, S.A.; Murad, V.; Avery, L.; Kulanthaivelu, R.; Kohan, A.; Ortega, C.; Elimova, E.; Yeung, J.; Hope, A.; et al. The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle Study. Cancers 2024, 16, 1873. https://doi.org/10.3390/cancers16101873
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  116. Yu, Yu1; Zhu, Jing2; Sang, Shibiao1; Yang, Yi3; Zhang, Bin1; Deng, Shengming1,4. Application of 18F-FDG PET/CT imaging radiomics in the differential diagnosis of single-nodule pulmonary metastases and second primary lung cancer in patients with colorectal cancer. Journal of Cancer Research and Therapeutics 20(2):p 599-607, April 2024. https://doi.org/10.4103/jcrt.jcrt_1674_23
  117. LIFEx-main: Laudicella, R., Comelli, A., Schwyzer, M. et al. PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI. Radiol med (2024). https://doi.org/10.1007/s11547-024-01820-z
  118. LIFEx-texture: Yesh Datar, Sarah A.M. Cuddy, Gavin Ovsak, Gerard T. Giblin, Mathew S. Maurer, Frederick L. Ruberg, Rima Arnaout, Sharmila Dorbala. Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis, Journal of the American Society of Echocardiography, 2024, ISSN 0894-7317, https://doi.org/10.1016/j.echo.2024.02.005
  119. LIFEx-MTV: Tricarico P, Chardin D, Martin N, et al. Total metabolic tumor volume on 18 F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy. Journal for ImmunoTherapy of Cancer 2024;12:e007628. https://doi.org/10.1136/jitc-2023-007628
  120. LIFEx-texture: Troiano, G., Rapani, A., Fanelli, F., Berton, F., Caroprese, M., Lombardi, T., Zhurakivska, K., & Stacchi, C. (2024). Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges. Clinical Oral Implants Research, 00, 1–10. https://doi.org/10.1111/clr.14271
  121. LIFEx-main: Zinsz, A., Pouget, C., Rech, F. et al. The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions. Eur J Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s00259-024-06720-y
  122. LIFEx-texture: Qian, L., Zhou, Z., Li, S., Liu, J., Zhang, S., Ren, J., Wang, W., & Yang, J. (2024). 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category. Quantitative Imaging In Medicine And Surgery, 14(4), 3131-3145. https://doi.org/10.21037/qims-23-494
  123. LIFEx-texture: Hathaway, Q.A., Abdeen, Y., Conte, J. et al. Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer. Int J Cardiovasc Imaging (2024). https://doi.org/10.1007/s10554-024-03101-2
  124. LIFEx-texture: Lee, J.W., Ahn, H., Yoo, I.D. et al. Relationship of FDG PET/CT imaging features with tumor immune microenvironment and prognosis in colorectal cancer: a retrospective study. Cancer Imaging 24, 53 (2024). https://doi.org/10.1186/s40644-024-00698-4
  125. LIFEx-texture: Laskov V, Rothbauer D, Malikova H (2024) Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan. PLoS ONE 19(4): e0301978. https://doi.org/10.1371/journal.pone.0301978
  126. LIFEx-Texure: UEDA, Cristina Emiko; DIAS, Laís Flausino; CARNEIRO, Camila de Godoi; SAPIENZA, Marcelo Tatit; BUCHPIGUEL, Carlos Alberto; DUARTE, Paulo Schiavom. Correlation of 18F-sodium fluoride uptake and radiodensity in extraosseous metastases of medullary thyroid carcinoma. Arch. Endocrinol. Metab., v. 68, e230152, Apr. 2024. https://doi.org/10.20945/2359-4292-2023-0152
  127. LIFEx-texture: Guillaume Declaux, Baudouin Denis de Senneville, Hervé Trillaud, Paulette Bioulac-Sage, Charles Balabaud, Jean-Frédéric Blanc, Laurent Facq, Nora Frulio. Assessment of a multivariable model using MRI-radiomics, age and sex for the classification of hepatocellular adenoma subtypes, Research in Diagnostic and Interventional Imaging, Volume 10, 2024, 100046, ISSN 2772-6525, https://doi.org/10.1016/j.redii.2024.100046
  128. LIFEx-texture: Bülbül, H.M., Burakgazi, G., Kesimal, U. et al. Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01558-8
  129. LIFEx-texture: Simone Famularo et al., European Journal of Surgical Oncology, https://doi.org/10.1016/j.ejso.2024.108274
  130. LIFEx-texture: Khateri, M., Babapour Mofrad, F., Geramifar, P. et al. Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade. Phys Eng Sci Med (2024). https://doi.org/10.1007/s13246-024-01402-3
  131. LIFEx-texture: Lanzarin-Minero AM, Reyes-Gonzalez JP, Fajardo-Fregoso BF. Predictores radiómicos F18-FDG PET/CT en la respuesta patológicacompleta a la quimioterapia neoadyuvante en pacientes con cáncer de mama. Anales de Radiología México. 2022;21(4):225-237. https://webcir.org/revistavirtual/3_2024/pdf/mexicoAnales/1_anales_en.pdf
  132. LIFEx-texture: Russo L et al., Radiomics for clinical decision support in radiation oncology, Clinical Oncology, https://doi.org/10.1016/ j.clon.2024.03.003
  133. LIFEx-texture: Nakajo, M., Hirahara, D., Jinguji, M. et al. Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01546-y
  134. LIFEx-texture: Bai, J., He, M., Gao, E. et al. High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10686-8
  135. LIFEx-texture: Seong-O Shim, Lal Hussain, Wajid Aziz, Abdulrahman A. Alshdadi, Abdulrahman Alzahrani, Abdulfattah Omar. Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach. International journal of Imaging systems and technology. Volume34, Issue2, March 2024, e23059. https://doi.org/10.1002/ima.23059
  136. LIFEx-texture: Takeyama, N., Sasaki, Y., Ueda, Y. et al. Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01545-z
  137. LIFEx-texture: Yang, T., Feng, J., Yao, R. et al. CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naive patients with Crohn’s disease. Insights Imaging 15, 69 (2024). https://doi.org/10.1186/s13244-024-01637-4
  138. LIFEx-texture: Norikane, T.; Ishimura, M.; Mitamura, K.; Yamamoto, Y.; Arai-Okuda, H.; Manabe, Y.; Murao, M.; Morita, R.; Obata, T.; Tanaka, K.; et al. Texture Features of 18F-Fluorodeoxyglucose Positron Emission Tomography for Predicting Programmed Death-Ligand-1 Levels in Non-Small Cell Lung Cancer. J. Clin. Med. 2024, 13, 1625. https://doi.org/10.3390/jcm13061625
  139. LIFEx-texture: Graillon, T., Salgues, B., Horowitz, T. et al. Peptide radionuclide radiation therapy with Lutathera in multirecurrent nonanaplastic meningiomas: antitumoral activity study by growth rate analysis. J Neurooncol (2024). https://doi.org/10.1007/s11060-024-04622-5
  140. LIFEx-texture: Hovhannisyan-Baghdasarian N. , Luporsi M., Captier N., Nioche C , Cuplov V , Woff E, Hegarat N. , Livartowski A. , Girard N., Buvat I and Orlhac F. Promising Candidate Prognostic Biomarkers in [18F]FDGPET Images: Evaluation in Independent Cohorts ofNon–Small Cell Lung Cancer Patients. J Nucl Med 2024; 00:1–8. http://doi.org/10.2967/jnumed.123.266331
  141. LIFEx-texture: Abenavoli EM, Linguanti F, Anichini M, Miele V, Mungai F, Palazzo M, Nassi L, Puccini B, Romano I, Sordi B, Sciagrà R, Simontacchi G, Vannucchi AM, Berti V. Texture analysis of 18F-FDG PET/CT and CECT: Prediction of refractoriness of Hodgkin lymphoma with mediastinal bulk involvement. Hematol Oncol. 2024 Mar;42(2):e3261. http://doi.org/10.1002/hon.3261 PMID: 38454623
  142. LIFEx-texture: Graillon, T., Salgues, B., Horowitz, T. et al. Peptide radionuclide radiation therapy with Lutathera in multirecurrent nonanaplastic meningiomas: antitumoral activity study by growth rate analysis. J Neurooncol (2024). https://doi.org/10.1007/s11060-024-04622-5
  143. LIFEx-texture: V. Navyasree, M. Meghana, N. Vaishnavi, N. Bhargavi, P. Mounika. Customized 3D CNN Model-based Lung Cancer Classification from Chest X-ray Images. International Journal For Advanced Research In Science & Technology. Volume 13, Issue 12, Dec 2023 ISSN 2457-0362. p849. https://www.ijarst.in/public/uploads/paper/397961708781960.pdf
  144. LIFEx-texture: Karabay N, Odaman H, Vahaplar A, Kizmazoglu C, Kalemci O. MRI-based Texture Analysis in Differentiation of Benign and Malignant Vertebral Compression Fractures. Current Medical Imaging. 2024 Feb. https://doi.org/10.2174/0115734056290762240209071656. PMID: 38415478.
  145. LIFEx-texture: A. Kohan, R. Hinzpeter, R. Kulanthaivelu, SA Mirshahvalad, L. Avery, M. Tsao, Q. Li, C. Ortega, U. Metser, A. Hope, P. Veit-Haibach, Contrast Enhanced CT Radiogenomics in a Retrospective NSCLC Cohort: Models, Attempted Validation of a Published Model and the Relevance of the Clinical Context, Academic Radiology, 2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.01.031
  146. LIFEx-texture: Hakkak Moghadam Torbati A, Pellegrino S, Fonti R, Morra R, De Placido S, Del Vecchio S. Machine Learning and Texture Analysis of [18F]FDG PET/CT Images for the Prediction of Distant Metastases in Non-Small-Cell Lung Cancer Patients. Biomedicines. 2024; 12(3):472. https://doi.org/10.3390/biomedicines12030472
  147. LIFEx-texture: Fan X, Zhang H, Wang Z, et al. Diagnosing postoperative lymph node metastasis in thyroid cancer with multimodal radiomics and clinical features. DIGITAL HEALTH. 2024;10. https://doi.org/10.1177/20552076241233244
  148. LIFEx-texture: Fukushima, Yasuhiroa; Suzuki, Keisukeb; Kim, Maib; Gu, Wenchaoc,d; Yokoo, Satoshib; Tsushima, Yoshitod. Evaluation of bone marrow invasion on the machine learning of 18F-FDG PET texture analysis in lower gingival squamous cell carcinoma. Nuclear Medicine Communications ():10.1097/MNM.0000000000001826, February 19, 2024. https://doi.org/10.1097/MNM.0000000000001826
  149. LIFEx-texture: Albano, D., Calabrò, A., Talin, A. et al. 2-[18]F FDG PET/CT dissemination features in adult burkitt lymphoma Are predictive of outcome. Ann Hematol (2024). https://doi.org/10.1007/s00277-024-05672-5
  150. LIFEx-main: Hongyue Zhao, Yexin Su, Yan Wang, Zhehao Lyu, Peng Xu, Wenchao Gu, Lin Tian and Peng Fu. Using tumor habitat-derived radiomic analysis during pretreatment 18 F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer. Zhao et al. Cancer Imaging (2024) 24:2. https://doi.org/10.1186/s40644-024-00670-2
  151. LIFEx-texture: Palomino-Fernandez D, Milara E, Galiana A, Sanchez-Ortiz M, Seiffert AP, Jiménez-Almonacid J, Gomez-Grande A, Ruiz-Solis S, Ruiz-Alonso A, Gomez EJ, et al. Textural and Conventional Pretherapeutic [18F]FDG PET/CT Parameters for Survival Outcome Prediction in Stage III and IV Oropharyngeal Cancer Patients. Applied Sciences. 2024; 14(4):1454. https://doi.org/10.3390/app14041454
  152. LIFEx-Main: Ahrari, S., Zaragori, T., Zinsz, A. et al. Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma. Sci Rep 14, 3256 (2024). https://doi.org/10.1038/s41598-024-53693-x
  153. LIFEx-texture: The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P. Apte, Alexandre Ayotte, Bhakti Baheti, Spyridon Bakas, Andrea Bettinelli, Ronald Boellaard, Luca Boldrini, Irène Buvat, Gary J. R. Cook, Florian Dietsche, Nicola Dinapoli, Hubert S. Gabrys, Vicky Goh, Matthias Guckenberger, Mathieu Hatt, Mahdi Hosseinzadeh, Aditi Iyer, Jacopo Lenkowicz, Mahdi A. L. Loutfi, Steffen Löck, Francesca Marturano, Olivier Morin, Christophe Nioche, Fanny Orlhac, Sarthak Pati, Arman Rahmim, Seyed Masoud Rezaeijo, Christopher G. Rookyard, Mohammad R. Salmanpour, Andreas Schindele, Isaac Shiri, Emiliano Spezi, Stephanie Tanadini-Lang, Florent Tixier, Taman Upadhaya, Vincenzo Valentini, Joost J. M. van Griethuysen, Fereshteh Yousefirizi, Habib Zaidi, Henning Müller, Martin Vallières, and Adrien Depeursinge. Radiology 2024 310:2 https://doi.org/10.1148/radiol.231319
  154. LIFEx-texture: Wang, Menglua; Peng, Mengyea; Yang, Xinyuea; Zhang, Yinga; Wu, Tingtinga; Wang, Zeyub; Wang, Kezhenga. Preoperative prediction of microsatellite instability status: development and validation of a pan-cancer PET/CT-based radiomics model. Nuclear Medicine Communications, February 05, 2024. https://doi.org/10.1097/MNM.0000000000001816
  155. LIFEx-texture: Hajri R, Nicod-Lalonde M, Hottinger AF, Prior JO, Dunet V. Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis. Diagnostics (Basel). 2023 Aug 5;13(15):2604. doi: https://doi.org/10.3390/diagnostics13152604. PMID: 37568967; PMCID: PMC10417545.
  156. LIFEx-Main: Ha S, O JH, Park C, Boo SH, Yoo IR, Moon HW, Chi DY, Lee JY. Dosimetric Analysis of a Phase I Study of PSMA-Targeting Radiopharmaceutical Therapy With [177Lu]Ludotadipep in Patients With Metastatic Castration-Resistant Prostate Cancer. Korean J Radiol. 2024 Feb;25(2):179-188. https://doi.org/10.3348/kjr.2023.0656
  157. LIFEx-Main: Albano, D.; Calabrò, A.; Dondi, F.; Bertagna, F. 2-[18F]-FDG PET/CT Semiquantitative and Radiomics Predictive Parameters of Richter’s Transformation in CLL Patients. Medicina 2024, 60, 203. https://doi.org/10.3390/medicina60020203
  158. LIFEx-texture: Xiaojing Jiang, Tianyue Li, Jianfang Wang, Zhaoqi Zhang, Xiaolin Chen, Jingmian Zhang, and Xinming Zhao. Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study. Cancer Biotherapy and Radiopharmaceuticals. https://doi.org/10.1089/cbr.2023.0162
  159. LIFEx-Main: Pellegrino, S.; Fonti, R.; Vallone, C.; Morra, R.; Matano, E.; De Placido, S.; Del Vecchio, S. Coefficient of Variation in Metastatic Lymph Nodes Determined by 18F-FDG PET/CT in Patients with Advanced NSCLC: Combination with Coefficient of Variation in Primary Tumors. Cancers 2024, 16, 279. https://doi.org/10.3390/cancers16020279
  160. LIFEx-texture: Kumar, R., Ramachandran, A., Mittal, B.R. et al. Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on 68 Ga PSMA PET/CT Delayed Imaging by Analyzing Radiomic Features. Nucl Med Mol Imaging (2024). https://doi.org/10.1007/s13139-023-00832-3
  161. LIFEx-texture: Martin, A.; Marcelin, C.; Petitpierre, F.; Jambon, E.; Maaloum, R.; Grenier, N.; Le Bras, Y.; Crombé, A. Clinical, Technical, and MRI Features Associated with Patients’ Outcome at 3 Months and 2 Years following Prostate Artery Embolization: Is There an Added Value of Radiomics? J. Pers. Med. 2024, 14, 67. https://doi.org/10.3390/jpm14010067
  162. LIFEx-texture: Saleh T. Alanezi, Waleed M. Almutairi, Michelle Cronin, Oliviero Gobbo, Shane M. O’Mara, Declan Sheppard, William T. O’Connor, Michael D. Gilchrist, Christoph KleefeldNiall Colgan. Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis. Journal of Neuropathology & Experimental Neurology, 2024, 1–13. https://doi.org/10.1093/jnen/nlad110
  163. LIFEx-texture: Alanezi ST, Almutairi WM, Cronin M, Gobbo O, O'Mara SM, Sheppard D, O'Connor WT, Gilchrist MD, Kleefeld C, Colgan N. Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis. J Neuropathol Exp Neurol. 2024 Jan 2:nlad110. https://doi.org/10.1093/jnen/nlad110. Epub ahead of print. PMID: 38164986
  164. LIFEx-texture: van Staalduinen EK, Matthews R, Khan A, Punn I, Cattell RF, Li H, Franceschi A, Samara GJ, Czerwonka L, Bangiyev L, et al. Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone. Diagnostics. 2024; 14(1):71. https://doi.org/10.3390/diagnostics14010071
  165. LIFEx-texture: Leszczyński W, Kazimierczak W, Lemanowicz A, Serafin Z. Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia. Pol J Radiol. 2024 Jan 25;89:e49-e53. https://doi.org/10.5114/pjr.2024.134818. PMID: 38371891; PMCID: PMC10867972.

 

Thesis (4):

  1. LIFEx-texture: JM Steger. Texturale und kinetische Analyse von Aminosäure-PET-Daten: Radiomics “zum Monitoring der antiangiogenen Therapie beim Glioblastom. 2024. https://kups.ub.uni-koeln.de/74094/1/DissertationsschriftJanSteger.pdf
  2. LIFEx-texture: Louis Rebaud. Whole-body / total-body biomarkers in PET imaging. https://theses.hal.science/tel-04618815
  3. LIFEx-texture: Evaluation of texture analysis capabilities computed tomographic images in complex diagnostics of hepatocellular cancer. National Medical Center Vidshnevsky, Russian Federation. Dissertation. 2023. (link)
  4. LIFEx-texture: Dominik Steube. Deep Learning Ansätze zur automatischen Klassifikation und Segmentierung von PET/CT Daten. Universität Ulm. https://doi.org/10.18725/OPARU-53062

 

Conference (8) :

  1. LIFEx-texture: Francesco Bianconi, Mario L. Fravolini, Elena Caltana. Muhammad U. Khan1,2 Barbara Palumbo. Classification of lung nodules on CT via pseudo-colour images and deep features from pre-trained convolutional networks. CCIW 2024, Milan, 25–27 Sep. 2024 https://www.bianconif.net/stuff/CCIW-2024-bianconi.pdf
  2. LIFEx-texture: A. Kordonis, K. Niapou, S. Paisiou, M.-E. Tomazinaki, A. Karaiskou, N. Bertsekas, P. Rondogianni, A. Samartzis. Comparison of PET Textural Metrics in Different Platforms based on Phantom Studies. 2nd Panhellenic congress of medical physics. oct 2024, Eugenides foundation https://pcmp2024.medical-physics.eu/wp-content/uploads/2024/10/P_3_6.pdf
  3. LIFEx-texture: Sharma, N., Balogova, S., Noskovicova, L., Montravers, F., Talbot, JN., Trentin, E. (2024). Automatic Interpretation of F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks. In: Suen, C.Y., Krzyzak, A., Ravanelli, M., Trentin, E., Subakan, C., Nobile, N. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2024. Lecture Notes in Computer Science(), vol 15154. Springer, Cham. https://doi.org/10.1007/978-3-031-71602-7_7
  4. LIFEx-texture: 925P External validation of the CD8 radiomics signature as a prognostic marker in recurrent or metastatic head and neck cancer treated with nivolumab. Adrien, L. et al. Annals of Oncology, Volume 35, S646 - S647
  5. LIFEx-texture: Kuznetsov A.I. Development of a prognostic model for diagnosis of prostate cancer based on radiomics of biparametric magnetic resonance imaging apparent diffusion coefficient maps and stacking of machine learning algorithms // Digital Diagnostics. - 2024. - Vol. 5. - N. 1S. - P. 80-82. https://doi.org/10.17816/DD626145
  6. LIFEx-texture: Prediction of adrenal masses nature through texture analysis and deep learning: Preliminary results from ENS@T RADIO-AI multicentric study. Lorenzo Tucci, Giulio Vara, Valentina Morelli, Edelmiro Luis Menendez Torre, Ulrich Dischinger, Athina Markou, Massimo Terzolo, Ariadni Spyroglou, Chiara Parazzoli, Aresta Carmen, Iacopo Chiodini, Diego Rivas, Alba Gutiérrez, Wiebke Schlötelburg, Krystallenia Alexandraki, Soraya Puglisi, Ilaria Improta, Antonio De Leo, Saverio Selva, Laura Alberici, Andrea De Giglio, Maria Abbondanza Pantaleo, Caterina Balacchi, Cristina Mosconi, Valentina Vicennati, Uberto Pagotto & Guido Di Dalmazi. Endocrine Abstracts (2024) 99 OC11.3, https://doi.org/10.1530/endoabs.99.OC11.3
  7. LIFEx-texture: Lorenzo Tucci, Antonio De Leo, Giulio Vara, Kimberly Coscia, Saverio Selva, Claudio Ricci, Laura Alberici, Caterina Balacchi, Donatella Santini, Valentina Vicennati, Uberto Pagotto, Cristina Mosconi, Giovanni Tallini & Guido Di Dalmazi. Radiomics for immunohistochemistry prediction in pheochromocytoma: a pilot study. Endocrine Abstracts (2024) 99 EP326, https//doi.org/10.1530/endoabs.99.EP326
  8. LIFEx-texture: Philip, M., Watts, J., Welch, A., McKiddie, F., Nath, M. XGBoost classifier-based survival prediction in head and neck cancer patients using pre-treatment PET images. 27th Conference on Medical Image Understanding and Analysis 2023. Foresterhill, Aberdeen, Scotland p192. https://www.pure.ed.ac.uk/ws/portalfiles/portal/409666338/9782832512319_1_.PDF

 

Review (17):

  1. LIFEx-texture: Patel K, Sanghvi H, Gill G S, et al. (December 10, 2024) Differentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective Review of the Novel Diagnostic Modalities. Cureus 16(12): e75476. https://doi.org/10.7759/cureus.75476
  2. LIFEx-texture: Cè, M.; Chiriac, M.D.; Cozzi, A.; Macrì, L.; Rabaiotti, F.L.; Irmici, G.; Fazzini, D.; Carrafiello, G.; Cellina, M. Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies. Diagnostics 2024, 14, 2473. https://doi.org/10.3390/diagnostics14222473
  3. LIFEx-texture: Andria Nicolaou, Christos P. Loizou, Marios Pantzaris, and Constantinos S. Pattichis. A Systematic Review of Quantitative MRI Brain Analysis Studies in Multiple Sclerosis Disease. IEEEAccess. https://doi.org/10.1109/ACCESS.2024.3489798
  4. LIFEx-texture: Víctor M. Oyervides-Juárez, Alder E. Perales-Mendoza, Sofía N. Sánchez-Morales, Marianela Madrazo-Morales, Mayela Z. Gutiérrez-Guajardo*, and Oscar Vidal-Gutiérrez. The innovation of mediastinal staging in lung cancer with artificial intelligence. Medicina Universitaria,  2024;26(3):86-91 https://doi.org/10.24875/RMU.24000007
  5. LIFEx-texture: Zhang, Y., Huang, W., Jiao, H. et al. PET radiomics in lung cancer: advances and translational challenges. EJNMMI Phys 11, 81 (2024). https://doi.org/10.1186/s40658-024-00685-5
  6. LIFEx-texture: Aouadi, Souha, et al. ‘Review of Cervix Cancer Classification Using Radiomics on Diffusion-Weighted Imaging’. Biomedical Engineering, IntechOpen, 31 July 2024. Crossref, https://doi.org/10.5772/intechopen.107497
  7. LIFEx-texture: Dong, D. et al. (2024). Radiomics and Multiomics Research. In: Liu, S. (eds) Artificial Intelligence in Medical Imaging in China. Springer, Singapore. https://doi.org/10.1007/978-981-99-8441-1_4
  8. LIFEx-texture: Amrane, K., Meur, C.L., Thuillier, P. et al. Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes. Cancer Imaging 24, 87 (2024). https://doi.org/10.1186/s40644-024-00732-5
  9. LIFEx-texture: Zhaoshuo Diao, Huiyan Jiang. A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features. Computers in Biology and Medicine, 2024, 108461, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2024.108461
  10. LIFEx-main: Varlamova, E.V.; Butakova, M.A.; Semyonova, V.V.; Soldatov, S.A.; Poltavskiy, A.V.; Kit, O.I.; Soldatov, A.V. Machine Learning Meets Cancer. Cancers 2024, 16, 1100. https://doi.org/10.3390/cancers16061100
  11. LIFEx-texture: Tapper, W.; Carneiro, G.; Mikropoulos, C.; Thomas, S.A.; Evans, P.M.; Boussios, S. The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer. J. Pers. Med. 2024, 14, 287. https://doi.org/ 10.3390/jpm14030287
  12. LIFEx-texture: Anghel, C.; Grasu, M.C.; Anghel, D.A.; Rusu-Munteanu, G.-I.; Dumitru, R.L.; Lupescu, I.G. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics 2024, 14, 438. https://doi.org/10.3390/diagnostics14040438
  13. LIFEx-texture: Shiva Singh, Bahram Mohajer, Shane A. Wells, Tushar Garg, Kate Hanneman, Takashi Takahashi, Omran AlDandan, Morgan P. McBee, Anugayathri Jawahar. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research, Academic Radiology,
    2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.01.024
  14. LIFEx-texture: Ballal et al. (2023). A systematic review of the management and implications of radiation-induced lymphopenia and the predictive rate of radiomic-based approaches in lung cancer Multidiscip. Rev. (2023) 6:e2023ss008, Supplementary Issue: Medical (AlliedCon 2023). https://doi.org/10.31893/multirev.2023ss008
  15. LIFEx-texture: Akin, O.; Lema-Dopico, A.; Paudyal, R.; Konar, A.S.; Chenevert, T.L.; Malyarenko, D.; Hadjiiski, L.; Al-Ahmadie, H.; Goh, A.C.; Bochner, B.; et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers 2023, 15, 5468. https://doi.org/10.3390/ cancers15225468
  16. LIFEx-texture: Shiva Singh, Bahram Mohajer, Shane A. Wells, Tushar Garg, Kate Hanneman, Takashi Takahashi, Omran AlDandan, Morgan P. McBee, Anugayathri Jawahar. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research,
    2024, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2024.01.024
  17. LIFEx-texture: Liu, J.; Cundy, T.P.; Woon, D.T.S.; Lawrentschuk, N. A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancers 2024, 16, 486. https://doi.org/10.3390/cancers16030486

 

Supplement (16):

  1. LIFEx-main: S Soares Brandao, A G S M Saura Martins, R J C A M Cavalcanti Amorim Martins, J M D R S Duarte Ribeiro Sobrinho, M M C B De Moraes Chaves Becker, R O B De Oliveira Buril, V O M De Oliveira Menezes, F A M Alves Mourato, Nearly perfect reproducibility degree of computed tomography in the evaluation of subcutaneous, visceral, and epicardial adipose volumes and radiodensities in lymphoma patients, European Heart Journal - Cardiovascular Imaging, Volume 25, Issue Supplement_1, July 2024, jeae142.015, https://doi.org/10.1093/ehjci/jeae142.015
  2. LIFEx-main: S Soares Brandao, R J C A M Cavalcanti Amorim Martins, A G S M Saura Martins, J M D R S Duarte Ribeiro Sobrinho, M M C B De Moraes Chaves Becker, R O B De Oliveira Buril, V O M De Oliveira Menezes, F A M Alves Mourato, Comparative analysis of volume and distribution of body fat in patients with lymphoma before and after chemotherapy, European Heart Journal - Cardiovascular Imaging, Volume 25, Issue Supplement_1, July 2024, jeae142.014, https://doi.org/10.1093/ehjci/jeae142.014
  3. LIFEx-texture: Auriac Julie, Mathilde Droguet, Lalith Kumar Shiyam Sundar, Romain-David Seban, Marie Luporsi, Manuel Pires, Christophe Nioche, Thomas Beyer, François-Clément Bidard, Irene Buvat, Fanny Orlhac. Prognostic stratification of metastatic triple-negative breast cancer patients using PET-radiomic features from malignant and tumor-free regions. Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241952; http://jnm.snmjournals.org/content/65/supplement_2/241952.abstract
  4. LIFEx-MTV: Fanny Orlhac, Narinée Hovhannisyan Baghdasarian, Hornella Fokem-Fosso, Marie Luporsi, HubertTissot, Christophe Nioche, Alain Livartowski, Paulette Salamoun-Feghali, Nadia Hegarat, NicolasGirard, Irene Buvat. Quantification of lesion dissemination (Dmax) in [18F]FDG-PET/CT imaging: a prognostic factor complementary to Total Metabolic Tumor Volume (TMTV) for advanced non-small cell lung cancer patients. Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241937; http://jnm.snmjournals.org/content/65/supplement_2/241927.abstract
  5. LIFEx-main: Auriac Julie, Lalith Kumar Shiyam Sundar, Romain-David Seban, Marie Luporsi, Christophe Nioche, Thomas Beyer, Irene Buvat, Fanny Orlhac. MOOSE vs TotalSegmentator: Comparison of feature values of segmented anatomical regions in [18F]FDG PET/CT images Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241948; http://jnm.snmjournals.org/content/65/supplement_2/241927.abstract
  6. LIFEx-MTV: Mathilde Droguet, Lalith Kumar Shiyam Sundar, Manuel Pires, Narinée Hovhannisyan Baghdasarian, Nicolas Captier, Marie Luporsi, Erwin Woff, Christophe Nioche, Frédérique Frouin, Thomas Beyer, Nicolas Girard, Irene Buvat, Fanny Orlhac. Automated segmentation of lesions in [18F]FDG PET/CT images of lung cancer patients: external evaluation of an AI-driven lesion segmentation tool (LION). Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241927; http://jnm.snmjournals.org/content/65/supplement_2/241927.abstract
  7. LIFEx-texture: Victor Comte, Hornella Fokem-Fosso, Olivier Humbert, Narinée Hovhannisyan Baghdasarian, NicolasCaptier, Marie Luporsi, Erwin Woff, Christophe Nioche, Nicolas Girard, Irene Buvat, Fanny Orlhac. Development and external validation of a PET-radiomic model to predict overall survival in advanced NSCLC patients treated by immunotherapy. Journal of Nuclear Medicine Jun 2024, 65 (supplement 2) 241256; ; http://jnm.snmjournals.org/content/65/supplement_2/241256.abstract
  8. LIFEx-texture: Dwivedi Pooja, Jha Ashish, Choudhury Sayak, Barage Sagar and RANGARAJAN, VENKATESH. Exploring the impact of feature selection methods and classification algorithms on the predictive performance of PET radiomic ML models in lung cancer ; Journal of Nuclear Medicine, J Nucl Med, 24133, 24133, 65, supplement 2, 2024/06/01; http://jnm.snmjournals.org/content/65/supplement_2/24133.abstract
  9. LIFEx-texture:  Monica Yadav, Jeeyeon Lee, Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Trie Arni Djunadi, Liam IL Young Chung, Jisang Yu, DarrenRodrigues, Nicolo Gennaro, Leeseul Kim, Yuchan Kim, Myungwoo Nam, Ilene Hong, Jessica Jang, Amy Cho, Grace Kang, Yury Velichko, and Young Kwang Chae. Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC). Meeting Abstract: 2024 ASCO Annual Meeting I. Journal of Clinical Oncology. Volume 42, Number 16_suppl. https://ascopubs.org/doi/abs/10.1200/JCO.2024.42.16_suppl.12142
  10. LIFEx-texture: Koki Enomoto, Soichiro Yoshida, Haruto Izumi, Sho Uehara, Yoh Matsuoka, Kohei Yamamoto, Daisuke Hirahara, Tatsunori Saho, Eichi Takaya, Shohei Fukuda, Yuma Waseda, Hajime Tanaka, Kenichi Ohashi and Yasuhisa Fujii. Are the differences in MRI findings between CRIBRIFORM and NON-CRIBRIFORM Cancer? An analysis using radiomics and delta-radiomics. The Journal of urology. Vol. 211, No. 5S, Supplement, Saturday, May 4, 2024; e443.https://doi.org/10.1097/01.JU.0001009448.41537.64.09
  11. LIFEx-texture: M Winkelmann, V Blumenberg, K Rejeski, V Bücklein, C Schmidt, F Dekorsy, P Bartenstein, J Ricke, M Subklewe, W Kunz. Charakterisierung des International Metabolic Prognostic Index (IMPI) und seiner Komponenten im Rahmen der CAR-T-Zell-Behandlung von Lymphomen. Rofo 2024; 196(S 01): S51. https://doi.org/10.1055/s-0044-1781616
  12. LIFEx-texture: Abstracts - 23rd FHNO Conference, 2023. Journal of Head & Neck Physicians and Surgeons 12(Suppl 2):p S1-S115, April 2024. | DOI: 10.4103/2347-8128.243190
  13. LIFEx-texture: Seyoung Lee, Kai Zhang, Jeeyeon Lee, Peter Haseok Kim, Amogh Hiremath, Salie Lee, Monica Yadav, Maria J. Chuchuca, Taegyu Um, Myungwoo Nam, Liam Il-Young Chung, Hye Sung Kim, Jisang Yu, Trie Arni Djunadi, Leeseul Kim, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Grace Kang, Jessica Jang, Amy Cho, Soowon Lee, Cecilia Nam, Timothy Hong, Yuri S. Velichko, Anant Madabhushi, Nathaniel Braman, Young Kwang Chae. Accelerated and precise tumor segmentation in NSCLC: A comparative analysis of automated ClickSeg and manual annotation for radiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2595. https://doi.org/10.1158/1538-7445.AM2024-2595
  14. LIFEx-texture: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics models to predict tumor response in non-small cell lung cancer (NSCLC) patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7530. https://doi.org/10.1158/1538-7445.AM2024-7530
  15. LIFEx-texture: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics model to predict immune checkpoint inhibitor-related pneumonitis (CIP) in non small cell lung cancer (NSCLC) in patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7529. https://doi.org/10.1158/1538-7445.AM2024-7529
  16. LIFEx-texture: Seyoung Lee, Amogh Hiremath, Jeeyeon Lee, Peter Haseok Kim, Kai Zhang, Salie Lee, Monica Yadav, Maria J. Chuchuca, Taegyu Um, Myungwoo Nam, Liam Il-Young Chung, Hye Sung Kim, Jisang Yu, Trie Arni Djunadi, Leeseul Kim, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Grace Kang, Jessica Jang, Amy Cho, Soowon Lee, Cecilia Nam, Timothy Hong, Yuri S. Velichko, Vamsidhar Velcheti, Anant Madabhushi, Nathaniel Braman, Young Kwang Chae. AI-powered radiomics model predicts immune checkpoint inhibitor-related pneumonitis (CIP) in advanced NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2594. https://doi.org/10.1158/1538-7445.AM2024-2594

Others (11):

  1. LIFEx-texture: DP Morán, MA Gómez Bermejo, E Canales Lachen, E García Santana, R García Latorre, M Cámara Gallego, R Colmenares Fernández, A Belén Capuz Suárez, MJ Béjar Navarro, JD García Fuentes, DS Martinez, R Morís Pablos. J Blázquez Sanchez, F García Vicente. Comparison of different machine learning methods for the classification of indeterminate adrenal lesions incidentally diagnosed in contrast enhanced CT. Rev Fis Med 2024;25(2)(Julio-Diciembre):13-23. https://doi.org/10.37004/sefm/2024.25.2.001
  2. LIFEx-texture: DP Morán, MA Gómez Bermejo, E Canales Lachen, E García Santana, R García Latorre, M Cámara Gallego, R Colmenares Fernández, A Belén Capuz Suárez, MJ Béjar Navarro, JD García Fuentes, D Sevillano Martinez, R Morís Pablos, J Blázquez Sanchez, F García Vicente. Comparativa de diferentes modelos radiómicos para la clasificación de lesiones adrenales indeterminadas diagnosticadas de forma incidental en TC con contraste. Rev Fis Med 2024;25(2)(Julio-Diciembre):13-23. https://doi.org/10.37004/sefm/2024.25.2.001
  3. LIFEx-texture: Lian, A. et al. Correlation between Changes in Primary Tumor Radiomic Features and Response during Definitive Radiation for Lung Cancer. International Journal of Radiation Oncology, Biology, Physics, Volume 120, Issue 2, e155 - e156 (link)
  4. LIFEx-texture: The value of machine learning models based on 18F-FDG PET/CT radiomics for predicting the degree of tumour differentiation in patients with non-small cell lung cancer YU Jun, LI Yang, YANG Xue, BI Xiaofeng, REN Dongdong, REN Chunling, HUANG Lei. Department of Nuclear Medicine, Ningbo Mingzhou. Hospital, Ningbo 315100, China. R734.2; R445.6 DOI: 10.3969/j.issn.2095-9400.2024.09.003 ;
  5. LIFEx-MTV: S. Gülbahar Ateş, B.B. Demirel, E. Kekilli, E. Öztürk, G. Uçmak. Heterogeneidad del tumor primario en la PET/TC con [68Ga]Ga-PSMA previa al tratamiento para la predicción de la recurrencia bioquímica en el cáncer de próstata. Revista Española de Medicina Nuclear e Imagen Molecular, 2024, 500032, ISSN 2253-654X, https://doi.org/10.1016/j.remn.2024.500032
  6. LIFEx-texture: N. Agüloğlu, A. Aksu, D.S. Unat, Ö. Selim Unat. Valor del análisis de la textura radiómica de la masa primaria y el ganglio linfático mediastínico de la PET/TC en la supervivencia de pacientes con cáncer de pulmón de célula no pequeña. Revista Española de Medicina Nuclear e Imagen Molecular. 2024, 500027, ISSN 2253-654X, https://doi.org/10.1016/j.remn.2024.500027
  7. LIFEx-texture: Shiyuan Liu. Artificial Intelligence in Medical Imaging in China. Springer Singapore, 978-981-99-8441-1, 02 August 2024 https://doi.org/10.1007/978-981-99-8441-1
  8. LIFEx-texture: Yesh Datar, Sarah A.M. Cuddy, Gavin Ovsak, Gerard T. Giblin, BCh, Mathew S. Maurer, Frederick L. Ruberg, Rima Arnaout, Sharmila Dorbala. Myocardial Texture Analysis of Echocardiograms in Cardiac Transthyretin Amyloidosis. Brief Research Communication| Volume 37, ISSUE 5, P570-573, May 2024. https://doi.org/10.1016/j.echo.2024.02.005
  9. LIFEx-MTV: Jiang Chong, Teng Yue, Ding Chongyang. Survival prognosis analysis of diffuse large B-cell lymphoma patients using tumor distribution patterns and metabolic tumor volume prediction with 18F-FDG PET[J]. International Journal of Radiation Medicine and Nuclear Medicine, 2024, 48(0): 1-8. https://doi.org/10.3760/cma.j.cn121381-202306031-00412
  10. LIFEx-texture: Contreras Aguilar, M. T., Salazar Calderon, D. R., Moreno Jimenez, S., & Chilaca Rosas, M. F. (2024). Determination of volumetry and compacity with a radiomics platform of high-grade CNS gliomas treated with radiotherapy. Archivos De Neurociencias, 29(S1). Retrieved from https://archivosdeneurociencias.org/index.php/ADN/article/view/522
  11. LIFEx-texture: Khromova S.V., Karmazanovsky G.G., Karelskaya N.A., Gruzdev I.S. The texture analysis of computed tomography studies in clear cell renal cell carcinoma: reproducibility of 2D and 3D segmentation. Almanac of clinical medicine. ISSN 2587-9294. Vol 51, No 8 (2023) https://doi.org/10.18786/2072-0505-2024-52-007

Publications 2023 - Journal papers (175)

(138)

  1. LIFEx-texture: Qiu YJ, Zhou LL, Li J, Zhang YF, Wang Y, Yang YS. The repeatability and consistency of different methods for measuring the volume parameters of the primary rectal cancer on diffusion weighted images. Front Oncol. 2023 Mar 9;13:993888. https://doi.org/10.3389/fonc.2023.993888. PMID: 36969078; PMCID: PMC10034158.
  2. LIFEx-texture: Agüloğlu N, Acar Akkaya E, Binicier ÖB. Volumetric and radiomics assessment of lesions with incidental [18F]FDG uptake in the colon: a retrospective radiomics study. Q J Nucl Med Mol Imaging. 2023 Jun;67(2):145-151. https://doi.org/10.23736/S1824-4785.21.03346-X. Epub 2021 Oct 19. PMID: 34664824.
  3. LIFEx-texture: Murtas F, Landoni V, Ordòñez P, Greco L, Ferranti FR, Russo A, Perracchio L, Vidiri A. Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis. Front Oncol. 2023 May 12;13:1152158. https://doi.org/10.3389/fonc.2023.1152158. PMID: 37251915; PMCID: PMC10213670.
  4. LIFEx-texture: Kawashima Y, Miyakoshi M, Kawabata Y, Indo H. Efficacy of texture analysis of ultrasonographic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023 Aug;136(2):247-254. https://doi.org/10.1016/j.oooo.2023.04.012. Epub 2023 May 1. PMID: 37353468.
  5. LIFEx-texture: Agüloğlu N, Aksu A, Unat DS. Machine learning approach using 18 F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection. Nucl Med Commun. 2023 Apr 1;44(4):302-308. https://doi.org/10.1097/MNM.0000000000001667. Epub 2023 Feb 9. PMID: 36756766.
  6. LIFEx-texture-MTV: Agüloğlu N, Aksu A. Evaluation of survival of the patients with metastatic rectal cancer by staging 18F-FDG PET/CT radiomic and volumetric parameters. Rev Esp Med Nucl Imagen Mol (Engl Ed). 2023 Mar-Apr;42(2):122-128. https://doi.org/10.1016/j.remnie.2022.09.010. Epub 2022 Sep 24. PMID: 36162744.
  7. LIFEx-texture: Bomhals B, Cossement L, Maes A, Sathekge M, Mokoala KMG, Sathekge C, Ghysen K, Van de Wiele C. Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules. J Clin Med. 2023 Dec 17;12(24):7731. https://doi.org/10.3390/jcm12247731. PMID: 38137800; PMCID: PMC10743692.
  8. LIFEx-texture: van Eijnatten EJM, Camps G, Guerville M, Fogliano V, Hettinga K, Smeets PAM. Milk coagulation and gastric emptying in women experiencing gastrointestinal symptoms after ingestion of cow's milk. Neurogastroenterology & Motility. 2024;36:e14696. https://doi.org/10.1111/nmo.14696
  9. LIFEx-MTV: Voltin, CA., Paccagnella, A., Winkelmann, M. et al. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06554-0
  10. LIFEx-texture: E. Babu, Ravi Krishna, Dathu Anushka, Medharimetla Lokesh, Boda Laila, Begari Mohan. Customized 3D CNN Model-based Lung Cancer Classification from Chest X-ray Images. IJARST. Volume 13, Issue 12, Dec 2023 ISSN 2457-0362, Page 268. https://www.ijarst.in/public/uploads/paper/516491702544047.pdf
  11. LIFEx-MTV: Alexander Dierks, Alexander Gäble, Andreas Rinscheid, Georgine Wienand, Christian H. Pfob, MalteKircher, Johanna S. Enke, Tilman Janzen, Marianne Patt, Martin Trepel, Dorothea Weckermann, Ralph A. Bundschuh, Constantin Lapa. First Safety and Efficacy Data with the Radiohybrid 177Lu-rhPSMA-10.1 for the Treatment of Metastatic Prostate Cancer. Journal of Nuclear Medicine Dec 2023, jnumed.123.266741; https://doi.org/10.2967/jnumed.123.266741
  12. LIFEx-Main: Pitarch, G., Rotstein Habarnau, Y., Chirico, R. et al. Exploring the applicability of a lesion segmentation method on [18F]fluorothymidine PET/CT images in diffuse large B-cell lymphoma. European J Hybrid Imaging 7, 28 (2023). https://doi.org/10.1186/s41824-023-00184-3
  13. LIFEx-MTV: Voltin, CA., Paccagnella, A., Winkelmann, M. et al. Multicenter development of a PET-based risk assessment tool for product-specific outcome prediction in large B-cell lymphoma patients undergoing CAR T-cell therapy. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06554-0
  14. LIFEx-texture: Hsiao C-C, Peng C-H, Wu F-Z, Cheng D-C. Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT). Diagnostics. 2023; 13(24):3690. https://doi.org/10.3390/diagnostics13243690
  15. LIFEx-TMTV: Jafari, E., Zarei, A., Dadgar, H. et al. A convolutional neural network–based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06555-z
  16. LIFEx-texture: Zhang Jianyua, Zhao Xinming, Zhao Yan, Zhang Jingmian, Zhang Zhaoqi. Prediction of epidermal growth factor receptor mutation subtypes in patients with non-small cell lung cancer by 18F-FDG PET/CT radiomic ; (6): 480-485, 2023. Chinese Journal of Nuclear Medicine and Molecular Imaging. https://doi.org/ 10.3760/cma.j.cn321828-20220109-00008
  17. LIFEx-texture: Zhao, W., Ozawa, Y., Hara, M. et al. Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes. Jpn J Radiol (2023). https://doi.org/10.1007/s11604-023-01512-0
  18. LIFEx-texture: Philip Whybra, Alex Zwanenburg, Vincent Andrearczyk, Roger Schaer, Aditya P Apte, et al.. The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics Authors and affiliations. 2023. https://hal.science/hal-04305625
  19. LIFEx-MTV: Kibrom B Girum, Anne-Ségolène Cottereau, Laetitia Vercellino, Louis Rebaud, Jérôme Clerc, et al.. Tumor location relative to the spleen is a prognostic factor in lymphoma patients: a demonstration from the REMARC trial. Journal of Nuclear Medicine, In press. https://hal.science/hal-04305558
  20. LIFEx-MTV: Zhaoting Cheng, Sijuan Zou, Jianyuan Zhou, Shuang Song, Yuankai Zhu, Jun Zhao, Xiaohua Zhu; Prognostic Value of Somatostatin Receptor-Derived Volumetric Parameters from a Hybrid Standardized Uptake Value Thresholding Method in Patients with 68Ga-DOTATATE-Avid Stage IV Neuroendocrine Neoplasms: A Preliminary Study. Neuroendocrinology 2023 ; https://doi.org/10.1159/000530771
  21. LIFEx-MTV: Aksu, A., Küçüker, K.A., Solmaz, Ş. et al. A different perspective on PET/CT before treatment in patients with Hodgkin lymphoma: importance of volumetric and dissemination parameters. Ann Hematol (2023). https://doi.org/10.1007/s00277-023-05547-1
  22. LIFEx-MTV: Dang, J., Peng, X., Wu, P. et al. Predictive value of Dmax and %ΔSUVmax of 18F-FDG PET/CT for the prognosis of patients with diffuse large B-cell lymphoma. BMC Med Imaging 23, 173 (2023). https://doi.org/10.1186/s12880-023-01138-8
  23. LIFEx-texture: Liu, J., Tang, M., Zhu, D. et al. The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma. EJNMMI Res 13, 94 (2023). https://doi.org/10.1186/s13550-023-01046-6
  24. LIFEx-texture: Frood, R., Mercer, J., Brown, P. et al. Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10340-9
  25. LIFEx-texture: Lara Cavinato, Michela Carlotta Massi, Martina Sollini, Margarita Kirienko &
    Francesca Ieva. Dual adversarial deconfounding autoencoder for joint batch‑effects removal from multi‑center and multi‑scanner radiomics data. Scientific Reports | (2023) 13:18857.  https://doi.org/10.1038/s41598-023-45983-7
  26. LIFEx-texture: Magdalena Belyanova, Martin Krupev. Texture analysis of adenomatous and metastatic adrenal lesions on native and contrast-enhanced computed tomography. Comptes rendus de l’Academie bulgare des Sciences. Tome 76, No 10, 2023. https://doig.org/10.7546/CRABS.2023.10.16
  27. LIFEx-MTV: Jing, F., Liu, Y., Zhao, X. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 13, 92 (2023). https://doi.org/10.1186/s13550-023-01047-5
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  97. LIFEx-texture: Na Wang, Meng Dai, Yan Zhao, Zhaoqi Zhang, Jianfang Wang, Jingmian Zhang, Yingchen Wang, Yunuan Liu, Fenglian Jing, Xinming Zhao. Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer. European Journal of Radiology Open 10 (2023) 100480. https://doi.org/10.1016/j.ejro.2023.100480
  98. LIFEx-viewer: Jing Gao, Si Xu, Huijun Ju, Yu Pan and Yifan Zhang. The potential application of MR‑derived ADCmin values from 68Ga‑DOTATATE and 18F‑FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors. Gao et al. EJNMMI Research. https://doi.org/10.1186/s13550-023-00960-z
  99. LIFEx-texture: Agüloğlu N, Aksu A, Unat DS. Machine learning approach using 18F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection. Nucl Med Commun. 2023 Feb 9. doi: 10.1097/MNM.0000000000001667. Epub ahead of print. PMID: 36756766
  100. LIFEx-texture: Crimì, F.; Agostini, E.; Toniolo, A.; Torresan, F.; Iacobone, M.; Tizianel, I.; Scaroni, C.; Quaia, E.; Campi, C.; Ceccato, F. CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study. Curr. Oncol. 2023, 30, 2169–2177. https://doi.org/10.3390/curroncol30020167
  101. LIFEx-texture: N. Stogiannos, H. Bougias, E. Georgiadou, S. Leandrou, P. Papavasileiou. Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study. Radiography 29 (2023) 355e361. https://doi.org/10.1016/j.radi.2023.01.010
  102. LIFEx-texture: Nicosia, L.; Pesapane, F.; Bozzini, A.C.; Latronico, A.; Rotili, A.; Ferrari, F.; Signorelli, G.; Raimondi, S.; Vignati, S.; Gaeta, A.; et al. Prediction of the Malignancy of a Breast Lesion Detected on Breast Ultrasound: Radiomics Applied to Clinical Practice. Cancers 2023, 15, 964. https://doi.org/10.3390/cancers15030964
  103. LIFEx-texture: Ju, H.M.; Kim, B.-C.; Lim, I.; Byun, B.H.; Woo, S.-K. Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes. Int. J. Mol. Sci. 2023, 24, 2794. https://doi.org/10.3390/ijms24032794
  104. LIFEx-texture: Wang, X., Dai, Y., Lin, H. et al. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09412-7
  105. LIFEx-texture: Cavinato, L.; Sollini, M.; Ragni, A.; Bartoli, F.; Zanca, R.; Pasqualetti, F.; Marciano, A.; Ieva, F.; Erba, P.A. Radiomics-Based Inter-Lesion Relation Network to Describe [18 F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer. Cancers 2023, 15, 823. https://doi.org/10.3390/cancers15030823
  106. LIFEx-texture: Seda Gülbaha Ates, Gülay Bilir Dilek, Gülin Ucmak. Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy. 2253-8089/© 2023 Sociedad Espanola de Medicina Nuclear e Imagen Molecular. https://doi.org/10.1016/j.remnie.2023.01.001
  107. LIFEx-CalciumQuantitation: Nardone, V.; Reginelli, A.; De Marco, G.; Natale, G.; Patanè, V.; De Chiara, M.; Buono, M.; Russo, G.M.; Monti, R.; Balestrucci, G.; Salvarezza, M.; Di Guida, G.; D’Ippolito, E.; Sangiovanni, A.; Grassi, R.; D’Onofrio, I.; Belfiore, M.P.; Cimmino, G.; Della Corte, C.M.; Vicidomini, G.; Fiorelli, A.; Gambardella, A.; Morgillo, F.; Cappabianca, S. Role of Cardiac Biomarkers in Non-Small Cell Lung Cancer Patients. Diagnostics 2023, 13, 400. https://doi.org/10.3390/diagnostics13030400
  108. LIFEx-texture: Mendes, B.; Domingues, I.; Dias, F.; Santos, J. Cone Beam Computed Tomography Radiomics for Prostate Cancer: Favourable vs. Unfavourable Prognosis Prediction. Appl. Sci. 2023, 13, 1378. https://doi.org/10.3390/app13031378
  109. LIFEx-texture: Degtiarova G, Garefa C, Boehm R, CianconeD, Sepulcri D, Gebhard C, Giannopoulos Aju, Pazhenkottil P, Kaufmann P.A. and Buechel R.R Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of concept study using 13N-ammonia positron emission tomography. J Nucl Cardiol 1071-3581. http://dx.doi.org/10.1007/s12350-022-03179-y
  110. LIFEx-texture: Annovazzi, A.; Ferraresi, V.; Covello, R.; Ascione, A.; Vari, S.; Petrongari, M.G.; Baldi, J.; Biagini, R.; Sciuto, R. Prognostic Value of Pre-Treatment [18F]FDG PET/CT Texture Analysis in Undifferentiated Soft-Tissue Sarcoma. J. Clin. Med. 2023, 12, 279. https://doi.org/10.3390/jcm12010279
  111. LIFEx-texture: Yang, M., Li, X., Cai, C. et al. [18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10503-8
  112. LIFEx-texture: Awais, M., Khan, N., Khan, A.K. et al. CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study. Abdom Radiol (2023). https://doi.org/10.1007/s00261-023-04103-9
  113. LIFEx-texture: Daria Kifjak, Maximilian Hochmair, Daniel Sobotka, Alexander R. Haug, Raphael Ambros, Florian Prayer, Benedikt H. Heidinger, Sebastian Roehrich, Ruxandra-Iulia Milos, Wolfgang Wadsak, Thorsten Fuereder, Dagmar Krenbek, Andreas Fazekas, Michael Meilinger, Marius E. Mayerhoefer, Georg Langs, Christian Herold, Helmut Prosch, Lucian Beer, Metabolic tumor volume and sites of organ involvement predict outcome in NSCLC immune-checkpoint inhibitor therapy. European Journal of Radiology, Volume 170, 2024, 111198, ISSN 0720-048X, https://doi.org/10.1016/j.ejrad.2023.111198
  114. LIFEx-texture: Müller, L., Tibyampansha, D., Mildenberger, P. et al. Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.BMC Med Imaging 23, 187 (2023). https://doi.org/10.1186/s12880-023-01142-y
  115. LIFEx-texture: She, J., Huang, H., Ye, Z. et al. Automatic biometry of fetal brain MRIs using deep and machine learning techniques. Sci Rep 13, 17860 (2023). https://doi.org/10.1038/s41598-023-43867-4
  116. LIFEx-texture: Akıncı Ö, Türkoğlu F, Nalbant MO, İnci E. Differentiating Renal Cell Carcinoma and Minimal Fat Angiomyolipoma with Volumetric MRI Histogram Analysis. Med J Bakirkoy 2023;19:256-262. https://doi.org/10.4274/BMJ.galenos.2023.2023.3-19
  117. Toffoli T, Saut O, Etchegaray C, Jambon E, Le Bras Y, Grenier N, Marcelin C. Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy. Journal of Personalized Medicine. 2023; 13(10):1444. https://doi.org/10.3390/jpm13101444 
  118. Lu J, Jiang N, Zhang Y and Li D (2023) A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Front. Oncol. 13:979437 https://doi.org/10.3389/fonc.2023.979437
  119. LIFEx-texture: Hasan, A.M., Al-Waely, N.K.N., Aljobouri, H.K., Jalab, H.A., Ibrahim, R.W., Meziane, F., Molecular Subtypes Classification of Breast Cancer in DCE-MRI Using Deep Features, Expert Systems with Applications (2023), doi: https://doi.org/10.1016/j.eswa.2023.121371
  120. Niyoteka, S., Seban, RD., Rouhi, R. et al. A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06320-2
  121. LIFEx-texture: Li, J., Du, J., Li, Y. et al. A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy. BMC Gastroenterol 23, 274 (2023). https://doi.org/10.1186/s12876-023-02902-4
  122. LIFEx-texture: Hermet P, Delache B, Herate C, Wolf E, Kivi G, Juronen E, et al. (2023) Broadly neutralizing humanized SARS-CoV-2 antibody binds to a conserved epitope on Spike and provides antiviral protection through inhalation-based delivery in non-human primates. PLoS Pathog 19(8): e1011532. https://doi.org/10.1371/journal.ppat.1011532
  123. Chen, J., Xu, K., Li, C. et al. [68Ga]Ga-FAPI-04 PET/CT in the evaluation of epithelial ovarian cancer: comparison with [18F]F-FDG PET/CT. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06369-z
  124. LIFEx-texture: Ma, H., Zhang, D., Wang, Y. et al. Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry 23, 466 (2023). https://doi.org/10.1186/s12888-023-04966-8
  125. LIFEx-texture: Ma, H., Zhang, D., Wang, Y. et al. Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI. BMC Psychiatry 23, 466 (2023). https://doi.org/10.1186/s12888-023-04966-8
  126. Šedienė, S.; Kulakienė, I.; Urbonavičius, B.G.; Korobeinikova, E.; Rudžianskas, V.; Povilonis, P.A.; Jaselskė, E.; Adlienė, D.; Juozaitytė, E. Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment. Medicina 2023, 59, 1173. https://doi.org/10.3390/medicina59061173
  127. LIFEx-texture: Malet J, Ancel J, Moubtakir A, Papathanassiou D, Deslée G, Dewolf M. Assessment of the Association between Entropy in PET/CT and Response to Anti-PD-1/PD-L1 Monotherapy in Stage III or IV NSCLC. Life. 2023; 13(4):1051. https://doi.org/10.3390/life13041051
  128. LIFEx-texture: Vani Rajasekar, M.P. Vaishnnave, S. Premkumar, Velliangiri Sarveshwaran, V. Rangaraaj, Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques, Results in Engineering, Volume 18, 2023, 101111, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2023.101111
  129. LIFEx-texture: Fu-Zong Wu, Yun-Ju Wu, Chi-Shen Chen, En-Kuei Tang. Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features, Academic Radiology, 2023, ISSN 1076-6332, https://doi.org/10.1016/j.acra.2023.02.033
  130. LIFEx-MTV: Elahmadawy MA, Ashraf A, Moustafa H, Kotb M, Abd El-Gaid S. Prognostic value of initial [18F]FDG PET/computed tomography volumetric and texture analysis-based parameters in patients with head and neck squamous cell carcinoma. Nucl Med Commun. 2023 Apr 10. doi: 10.1097/MNM.0000000000001695. Epub ahead of print. PMID: 37038954.
  131. LIFEx-texture: Amrane, K., Thuillier, P., Bourhis, D. et al. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci Rep 13, 5789 (2023). https://doi.org/10.1038/s41598-023-31587-8
  132. LIFEx-texture: Ari Lee, Gun-Chan Park, Eunae Sandra Cho, Yoon Joo Choi, Kug Jin Jeon, Sang Sun Han, Chena Lee. Radiomics-based sialadenitis staging in contrast-enhanced computed tomography and ultrasonography: A preliminary rat model study, Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 2023, ISSN 2212-4403, https://doi.org/10.1016/j.oooo.2023.04.005
  133. LIFEx-texture: Özgül, H.A., Akin, I.B., Mutlu, U. et al. Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton. Skeletal Radiol (2023). https://doi.org/10.1007/s00256-023-04333-4
  134. LIFEx-texture: Xie Y, Teng Y, Jiang C, Ding C, Zhou Z. Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL). Jpn J Radiol. 2023 Feb 8. doi: 10.1007/s11604-023-01398-y. Epub ahead of print. PMID: 36752954.
  135. LIFEx-texture: Bhatt, M., Shende, P. Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment. Arch Computat Methods Eng (2023). https://doi.org/10.1007/s11831-023-09886-0
  136. LIFEx-texture: Zhao, X., Zhao, Y., Zhang, J. et al. Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics. EJNMMI Res 13, 4 (2023). https://doi.org/10.1186/s13550-023-00956-9
  137. LIFEx-texture: Yutao Yang, Hao Chen, Min Ji, Jianzhang Wu, Xiaoshan Chen, Fenglin Liu, Shengxiang Rao, A new radiomics approach combining the tumor and peri-tumor regions to predict lymph node metastasis and prognosis in gastric cancer, Gastroenterology Report, Volume 11, 2023, goac080, https://doi.org/10.1093/gastro/goac080

 

Thesis (6):

  1. LIFEx-texture: Emre Uysal. Nazofarenks karsinomunda tedavi oncesi cekilen kontrastli manyetik rezonans goruntulemeden erken tedavi yaniti ongorulebilir mi? Thesis · October 2023. https://doi.org/10.13140/RG.2.2.22343.47524
  2. LIFEx-texture: SANAAT, Amirhossein. Strategies for improvement of PET instrumentation performance and imaging methodology. 2023. https://doi.org/10.13097/archive-ouverte/unige:171571
  3. LIFEx-texture: Giulia Colelli, Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology, Università degli Studi di Pavia, Università della Svizzera italiana, 2022 (link)
  4. LIFEx-texture: Hamza CHEGRAOUI. Machine learning for genomics and imaging data integration applied to neuro-oncology. Paris-Saclay, le 23 mars 2023 (link)
  5. LIFEx-texture: Federico Loi. Radiogenomic features of CNS tumors and MiRNAs correlation phenotypes analysis. Jan 2020. Università degli Studi di Cagliari. (link)
  6. LIFEx-texture: Artificial intelligence in molecular imaging: from machine to deep learning. Riccardo Laudicella. https://iris.unime.it/retrieve/e234d14a-b211-415e-8bef-0640248279de/Tesi.pdf

 

Poster (1):

  1. LIFEx-texture: Giulia Martini, Valerio Nardone, Davide Ciardiello, Marco De Chiara, Teresa Troiani, Luca D'ambrosio, Stefania Napolitano, Claudia Cardone, Chiara Cremolini, Filippo Pietrantonio, Evaristo Maiello, Antonio Avallone, Salvatore Cappabianca, Fortunato Ciardiello, Alfonso Reginelli, and Erika Martinelli. Journal of Clinical Oncology 2023 41:4_suppl, 241-241(link)

 

Article not in English (1):

  1. LIFEx-texture: SG Ates, GB Dilek, G Uçmak. Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras. Revista Española de Medicina Nuclear e Imagen …, 2023, ISSN 2253-654X. https://doi.org/10.1016/j.remn.2023.01.001

 

Review (30):

  1. LIFEx-texture: Xue Yang, Kexin Huang, Dewei Yang, Weiling Zhao, and Xiaobo Zho. Biomedical Big Data Technologies, Applications, andChallenges for Precision Medicine: A Review. 2023 2300163 . Global Challenges published. http://doi/org/10.1002/gch2.202300163
  2. LIFEx-texture: Hugo C. Temperley, Niall J. O’Sullivan, Caitlin Waters, Alison Corr, Brian J. Mehigan, Grainne O’Kane, Paul McCormick, Charles Gillham, Emanuele Rausa, John O. Larkin, James F. Meaney, Ian Brennan,and Michael E. Kelly. Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review. The American Surgeon 2023, Vol. 0(0) 1–10. http://doi.org/10.1177/00031348231216494
  3. LIFEx-texture: Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, et al. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers. 2023; 15(22):5468. https://doi.org/10.3390/cancers15225468
  4. LIFEx-texture: Filippi, L., Ferrari, C., Nuvoli, S. et al. Pet-radiomics in lymphoma and multiple myeloma: update of current literature. Clin Transl Imaging (2023). https://doi.org/10.1007/s40336-023-00604-1
  5. LIFEx-texture: Xinyi Chen, Xiang Liu, Yuke Wu, Zhenglei Wang, Shuo Hong Wang. Research related to the diagnosis of prostate cancer based on machine learning medical images: a review. International Journal of Medical Informatics. 2023, 105279, ISSN 1386-5056, https://doi.org/10.1016/j.ijmedinf.2023.105279
  6. LIFEx-texture: Yaru Feng1,2, Jing Gong1,2, Tingdan Hu1,2, Zonglin Liu1,2, Yiqun Sun1,2, Tong Tong. Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis. Quant Imaging Med Surg 2023. https://dx.doi.org/10.21037/qims-23-69
  7. LIFEx-texture: L. Tong et al., "Integrating Multi-omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence," in IEEE Reviews in Biomedical Engineering, https://doi.org/10.1109/RBME.2023.3324264
  8. LIFEx-texture: Zhang, W.; Guo, Y.; Jin, Q. Radiomics and Its Feature Selection: A Review. Symmetry 2023, 15, 1834. https://doi.org/10.3390/sym15101834
  9. LIFEx-texture: Hirata, K., Kamagata, K., Ueda, D. et al. From FDG and beyond: the evolving potential of nuclear medicine. Ann Nucl Med (2023). https://doi.org/10.1007/s12149-023-01865-6
  10. LIFEx-texture: Michail E. Klontzas, Salvatore Claudio Fanni, Emanuele Neri. Introduction to Artificial Intelligence. Springer Nature, 15 sept. 2023 - 165 pages (link)
  11. IFEx-texture: Stamoulou, E. et al. (2023). Using Commercial and Open-Source Tools for Artificial Intelligence: A Case Demonstration on a Complete Radiomics Pipeline. In: Klontzas, M.E., Fanni, S.C., Neri, E. (eds) Introduction to Artificial Intelligence. Imaging Informatics for Healthcare Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-25928-9_2
  12. LIFEx-texture: Burak Kocak, Sabahattin Yuzkan, Samet Mutlu, Elif Bulut, Irem Kavukoglu, Publications poorly report the essential RadiOmics ParametERs (PROPER): a meta-research on quality of reporting, European Journal of Radiology, 2023, 111088, ISSN 0720-048X, https://doi.org/10.1016/j.ejrad.2023.111088
  13. LIFEx-texture: Xiaorong Wu, Andreas Polychronis. Application of radiomics and artificial intelligence in lung cancer immunotherapy: a guide and hurdles from clinical trials. Wu et al. J Cancer Metastasis Treat 2023;9:29 https://doi.org.10.20517/2394-4722.2023.10
  14. LIFEx-texture: Tabassum, M.; Suman, A.A.; Suero Molina, E.; Pan, E.; Di Ieva, A.; Liu, S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers 2023, 15, 3845. https://doi.org/10.3390/cancers15153845
  15. LIFEx-texture: Liu, Z., Duan, T., Zhang, Y. et al. Radiogenomics: a key component of precision cancer medicine. Br J Cancer (2023). https://doi.org/10.1038/s41416-023-02317-8
  16. LIFEx-texture: Albalkhi, I., Bhatia, A., Lösch, N. et al. Current state of radiomics in pediatric neuro-oncology practice: a systematic review. Pediatr Radiol (2023). https://doi.org/10.1007/s00247-023-05679-6
  17. LIFEx-texture: Jahanshahi, A., Soleymani, Y., Fazel Ghaziani, M. et al. Radiomics reproducibility challenge in computed tomography imaging as a nuisance to clinical generalization: a mini-review. Egypt J Radiol Nucl Med 54, 83 (2023). https://doi.org/10.1186/s43055-023-01029-6
  18. LIFEx-texture: Carole Koechli, Daniel R. Zwahlen, Philippe Schucht, Paul Windisch, Radiomics and Machine Learning for Predicting the Consistency of Benign Tumors of the Central Nervous System: A Systematic Review, European Journal of Radiology, 2023, 110866, ISSN 0720-048X, https://doi.org/10.1016/j.ejrad.2023.110866
  19. LIFEx-texture: Jahanshahi, A., Soleymani, Y., Fazel Ghaziani, M. et al. Radiomics reproducibility challenge in computed tomography imaging as a nuisance to clinical generalization: a mini-review. Egypt J Radiol Nucl Med 54, 83 (2023). https://doi.org/10.1186/s43055-023-01029-6
  20. Albano, D.; Treglia, G.; Dondi, F.; Calabrò, A.; Rizzo, A.; Annunziata, S.; Guerra, L.; Morbelli, S.; Tucci, A.; Bertagna, F. 18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor? Cancers 2023,15,2494. https://doi.org/ 10.3390/cancers15092494
  21. LIFEx-texture: Ekmekcioglu, O., Terry, S.Y.A., Morbelli, S. et al. Superfluous, controversial and luxury issues in nuclear medicine. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06228-x
  22. LIFEx-texture: Malcolm, J.A., Tacey, M., Gibbs, P. et al. Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09653-6
  23. LIFEx-texture: Matteo Ferro & al. Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review.,Ther Adv Urol 2023, Vol. 15: 1–26 https://doi.org/10.1177/17562872231164803
  24. LIFEx-texture: E Pfaehler, I Zhovannik, L Wei, R Boellaard, A Dekker. The state of Reproducibility and Repeatability in Radiomics. Pitfalls of Image Biomarke (link)
  25. LIFEx-texture: Abler, D., Schaer, R., Oreiller, V. et al. QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research. Eur Radiol Exp 7, 16 (2023). https://doi.org/10.1186/s41747-023-00326-z
  26. LIFEx-texture: Mirestean, C.C.; Iancu, R.I.; Iancu, D.P.T. Simultaneous Integrated Boost (SIB) vs. Sequential Boost in Head and Neck Cancer (HNC) Radiotherapy: A Radiomics-Based Decision Proof of Concept. J. Clin. Med.2023,12,2413. https:// doi.org/10.3390/jcm12062413
  27. LIFEx-texture: Oliveira C, Oliveira F, Vaz SC, Marques HP, Cardoso F. Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer. Br J Radiol (2023) https://doi.org/10.1259/bjr.20220655
  28. LIFEx-texture: Zhou Huijie, Luo Qian, Wu Wanchun, Li Na, Yang Chunli, Zou Liqun. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front. Immunol., 01 March 2023, Sec. Cancer Immunity and Immunotherapy, Volume 14 - 2023 | https://doi.org/10.3389/fimmu.2023.1088874
  29. LIFEx-texture: Abou Karam, G.; Malhotra, A. PET/CT May Assist in Avoiding Pointless Thyroidectomy in Indeterminate Thyroid Nodules: A Narrative Review. Cancers 2023, 15, 1547. https://doi.org/10.3390/cancers15051547
  30. LIFEx-texture: Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma. Eren M., Veziroglu, Faraz Farhadi, Navid Hasani, Moozhan Nikpanah, Mark Roschewski, Ronald M. Summers and Babak Saboury. Semin Nucl Med 00:1-23. https://doi.org/10.1053/j.semnuclmed.2022.11.003
 
 

 

 

Publications 2022 - Journal papers (166)

  1. LIFEx-texture: Ketcherside T, Shi C, Chen Q, et al. Evaluation of repeatability and reproducibility of radiomic features produced by the fan-beam kV-CT on a novel ring gantry-based PET/CT linear accelerator. Med Phys. 2023;50:3719–3725. https://doi.org/10.1002/mp.16399
  2. LIFEx-texture: Liu JJ, Wang YZ, Chen N, Wang QN, Liu L, Li Y, Lei L, Wu Y. Hypothesis generation: Quantitative research to levator ani muscle injury based on MRI texture analysis. J Obstet Gynaecol Res. 2022 Dec;48(12):3269-3278. https://doi.org/10.1111/jog.15440. Epub 2022 Sep 27. PMID: 36167929.
  3. LIFEx-texture: Altay C, Başara Akın I, Özgül AH, Adıyaman SC, Yener AS, Seçil M. Machine learning analysis of adrenal lesions: Preliminary study evaluating texture analysis in the differentiation of adrenal lesions. Diagn Interv Radiol. DOI: 10.5152/dir.2022.21266 (doi)
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Review (4):

  1. LIFEx-texture: Mirestean C.C., Iancu R.I. Iancu D.T. Delta-radiomics Entropy Based on Tumor Heterogeneity Concept-Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma. Modern Medicine | 2022, Vol. 29, No. 4. (link)
  2. Hung, K.F.; Ai, Q.Y.H.; Wong, L.M.; Yeung, A.W.K.; Li, D.T.S.; Leung, Y.Y. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics 2023, 13, 110. https://doi.org/ 10.3390/diagnostics13010110 (doi)
  3. LIFEx-texture: Sotiris Raptis, Christos Ilioudis, Vasiliki Softa and Kiki Theodorou. Artificial Intelligence in Predicting Treatment Response in Non-Small-Cell Lung Cancer (NSCLC) BioMedical Journal of Scientific & Technical Research, Dec 2022 DOI: 10.26717/BJSTR.2022.47.007497 (doi)
  4.  LIFEx-texture:Li, S., Zhou, B. A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat Oncol 17, 217 (2022). https://doi.org/10.1186/s13014-022-02192-2 (doi)

 

Thesis (1):

  1. LIFEx-texture: Negreros Osuna A.A. Análisis de la textura tomográfica en tumores renales en etapa avanzada como biomarcador para la predicción de respuesta al tratamiento sistémico con inhibidores de la tirosina quinasa. Doctor en medicina, Nov 2022. http://eprints.uanl.mx/24772/1/1080328720.pdf
 
 

 

 

Publications 2021 - Journal papers (157)

  1. LIFEx-texture: Zhou J, Zou S, Kuang D, Yan J, Zhao J, Zhu X. A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer. Front Oncol. 2021 Nov 17;11:769272. https://doi.org/10.3389/fonc.2021.769272. PMID: 34868999; PMCID: PMC8635743.
  2. LIFEx-texture: Wei M, Gu B, Song S, Zhang B, Wang W, Xu J, Yu X, Shi S. A Novel Validated Recurrence Stratification System Based on 18F-FDG PET/CT Radiomics to Guide Surveillance After Resection of Pancreatic Cancer. Front Oncol. 2021 May 12;11:650266. https://doi.org/10.3389/fonc.2021.650266. PMID: 34055620; PMCID: PMC8149949.
  3. LIFEx-texture: Vara G, Rustici A, Sechi A, Mosconi C, Lucidi V, Golfieri R. Texture Analysis on Ultrasound: The Effect of Time Gain Compensation on Histogram Metrics and Gray-Level Matrices. J Med Phys. 2020 Oct-Dec;45(4):249-255. https://doi.org/10.4103/jmp.JMP_82_20. Epub 2021 Feb 2. PMID: 33953501; PMCID: PMC8074715.
  4. LIFEx-texture: Han X, Yang J, Luo J, Chen P, Zhang Z, Alu A, Xiao Y, Ma X. Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods. Front Oncol. 2021 Jul 22;11:606677. https://doi.org/10.3389/fonc.2021.606677. PMID: 34367940; PMCID: PMC8339967.
  5. LIFEx-texture: Thuillier P, Liberini V, Grimaldi S, Rampado O, Gallio E, Santi B, Arvat E, Piovesan A, Filippi R, Abgral R, Molinari F, Deandreis D. Prognostic Value of Whole-Body PET Volumetric Parameters Extracted from 68Ga-DOTATOC PET/CT in Well-Differentiated Neuroendocrine Tumors. J Nucl Med. 2022 Jul;63(7):1014-1020. https://doi.org/10.2967/jnumed.121.262652. Epub 2021 Nov 5. PMID: 34740949.
  6. LIFEx-texture:Aksu A, Karahan Şen NP, Tuna EB, Aslan G, Çapa Kaya G. Evaluation of 68Ga-PSMA PET/CT with volumetric parameters for staging of prostate cancer patients. Nucl Med Commun. 2021 May 1;42(5):503-509. doi: 10.1097/MNM.0000000000001370. Erratum in: Nucl Med Commun. 2021 Oct 1;42(10):1186. https://doi.org/10.1097/MNM.0000000000001482. PMID: 33560717.
  7. LIFEx-texture:Yamashita S, Okuda K, Nakaichi T, Yamamoto H, Yokoyama K. Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET. J Nucl Med Technol. 2021 Mar;49(1):58-64. https://doi.org/10.2967/jnmt.120.246157. Epub 2020 Oct 5. PMID: 33020230
  8. LIFEx-viewer: Martinez- Perez R, Kortz MW, Ung TH, Rayo N, Lagares A, Cepeda S. Third Ventricle Volume Predicts Functional Outcome in Chronic Subdural Hematoma. Acta Neurol Scand.  2021;00:1– 8 (doi)
  9. LIFEx-texture: Urraro F, Nardone V, Reginelli A, Varelli C, Angrisani A, Patanè V, D’Ambrosio L, Roccatagliata P, Russo GM, Gallo L, De Chiara M, Altucci L and Cappabianca S (2021) MRI Radiomics in Prostate Cancer: A Reliability Study. Front. Oncol. 11:805137 (doi)
  10. LIFEx-texture: Guilherme D. Kolinger, David Vállez García, Gerbrand Maria Kramer, Virginie Frings, Gerben J.C. Zwezerijnen, Egbert F. Smit, Adrianus Johannes de Langen, Irène Buvat and Ronald Boellaard. Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics. Journal of Nuclear Medicine, published on December 21, 2021 (doi)
  11. LIFEx-texture: Fatma Ceren Sarioglu, Orkun Sarioglu, Handan Guleryuz, Burak Deliloglu, Funda Tuzun, Nuray Duman, Hasan Ozkan. The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia. British Institute of Radiology. Published Online:17 Dec 2021 (doi)
  12. LIFEx-texture:  Li J, Ge S, Sang S, Hu C and Deng S (2021) Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics. Front. Oncol 11:789014 (doi)
  13. Russo, G.; Stefano, A.; Alongi, P.; Comelli, A.; Catalfamo, B.; Mantarro, C.; Longo, C.; Altieri, R.; Certo, F.; Cosentino, S.; et al. Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model. Curr. Oncol. 2021, 28, 5318–5331 (doi)
  14. LIFEx-texture: Ahrari, S.; Zaragori, T.; Rozenblum, L.; Oster, J.; Imbert, L.; Kas, A.; Verger, A. Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from TreatmentRelated Changes. Biomedicines 2021, 9, 1924 (doi)
  15. LIFEx-texture: Julie Faudemer, Nicolas Aide, Anne‑Claire Gac, Ghandi Damaj, Jean‑Pierre Vilque, Charline Lasnon. Diagnostic value of baseline 18 FDG PET/CT skeletal textural features in follicular lymphoma. Scientific Reports (2021) 11:23812 (doi)
  16. LIFEx-viewer: Martina Sollini, Francesco Bartoli, Lara Cavinato, Francesca Ieva, Alessandra Ragni, Andrea Marciano, Roberta Zanca, Luca Galli, Fabiola Paiar, Francesco Pasqualetti and Paola Anna Erba. [18 F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Research (2021) 11:119 (doi)
  17. LIFEx-texture: Sollini, M., Bartoli, F., Cavinato, L. et al. [18F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Res 11, 119 (2021) (doi)
  18. LIFEx-texture: Guilherme D. Kolinger, David Vállez García, Gerbrand M. Kramer, Virginie Frings, Gerben J.C. Zwezerijnen, Egbert F. Smit, Adrianus J. de Langen, Irène Buvat, and Ronald Boellaard. Use-cases for 18F-FDG PET radiomics based on uptake time dependence in non-small cell lung cancer. Journal of Nuclear Medicine, SNMMI, 2021 (doi)
  19. LIFEx-texture:Yeye Zhou, Yuchun Zhu, Zhiqiang Chen, Jihui Li, Shibiao Sang and Shengming Deng. Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes. Contrast Media & Molecular Imaging Volume 2021, Article ID 6347404, 8 pages (doi)
  20. LIFEx-MTV-texture: M Kimura, I Kato, K Ishibashi, K Hashimoto, H Tsuji, Y Sone, M Umemura, T Nagao. Texture analysis of 18F-FDG PET images for the detection of cervical lymph node metastases in patients with oral squamous cell carcinoma. Advances in Oral and Maxillofacial Surgery. Volume 5, January–March 2022, 100228 (doi)
  21. LIFEx-MTV-texture: Hyein Ahn, Jeong Won Lee, Si-Hyong Jang, Hyun Ju Lee, Ji-Hye Lee, Mee-Hye Oh, Sang Mi Lee,
    Prognostic significance of imaging features of peritumoral adipose tissue in FDG PET/CT of patients with colorectal cancer,
    European Journal of Radiology, Volume 145, 2021 (doi)
  22. LIFEx-texture: Kotaro Ito, Hirotaka Muraoka, Naohisa Hirahara, Eri Sawada, Shoya Hirohata, Kohei Otsuka, Shunya Okada, Takashi Kaneda. Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw. European Journal of Radiology, Volume 145, 110030, December 01, 2021 (doi)
  23. LIFEx-MTV-texture: Xue Beihui, Jiang Jia, Chen Lei, Wu Sunjie, Zheng Xuan, Zheng Xiangwu, Tang Kun. Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front. Oncol., 26 October 2021 (doi)
  24. LIFEx-viewer: Stefano, A.; Leal, A.; Richiusa, S.; Trang, P.; Comelli, A.; Benfante, V.; Cosentino, S.; Sabini, M.G.; Tuttolomondo, A.; Altieri, R.;et al. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. Appl. Sci. 2021, 11, 10170 (doi)
  25. LIFEx-texture: Mendes, B.; Domingues, I.; Silva, A.; Santos, J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life 2021, 11, 1164 (doi)
  26. LIFEx-viewer: Lee, JH., Kim, S., Lee, H.S. et al. Different prognostic impact of glucose uptake in visceral adipose tissue according to sex in patients with colorectal cancer. Sci Rep 11, 21556 (2021) (doi)
  27. LIFEx-texture: Kim, J.; Jeong, S.Y.; Kim, B.-C.; Byun, B.-H.; Lim, I.; Kong, C.-B.; Song, W.S.; Lim, S.M.; Woo, S.-K. Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images. Diagnostics 2021, 11, 1976 (doi)
  28. LIFEx-texture: Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X and Tang K (2021) Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front. Oncol. 11:740111 (doi)
  29. LIFEx-texture: Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim. Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics. PET Clinics, Volume 17, Issue 1, 2021 (doi)
  30. LIFEx-texture: Zhao Y, Chen R, Zhang T, Chen C, Muhelisa M, Huang J, Xu Y and Ma X (2021) MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions. Front. Oncol. 11:552634 (doi)
  31. LIFEx-MTV: Hande Melike Bülbül, Ogün Bülbül, Sülen Sarıoğlu, Özhan Özdoğan, Ersoy Doğan, Nuri Karabay. Relationships Between DCE-MRI, DWI, and 18 F-FDG PET/CT Parameters with Tumor Grade and Stage in Patients with Head and Neck Squamous Cell Carcinoma. Mol Imaging Radionucl Ther 2021;30:177-186 (doi)
  32. LIFEx-texture: Lee JW, Kim SY, Han SW, Lee JE, Hong SH, Lee SM, Jo IY. Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. Journal of Personalized Medicine. 2021; 11(10):1029 (doi)
  33. LIFEx-PT-COMP: Zaragori T, Doyen M, Rech F, Blonski M, Taillandier L, Imbert L and Verger A (2021) Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient? Front. Oncol. 11:735257. doi: 10.3389/fonc.2021.735257(doi)
  34. LIFEx-texture: Maëlle Dade, Marine Giry, Giulia Berzero, Marion Benazra, Gilles Huberfeld, Delphine Leclercq, Vincent Navarro, Jean-Yves Delattre, Dimitri Psimaras, Agusti Alentorn. Quantitative brain imaging analysis of neurological syndromes associated with anti-GAD antibodies. NeuroImage: Clinical, Volume 32, 2021, 102826, ISSN 2213-1582 (doi)
  35. LIFEx-texture: valuation of 68Ga-PSMA PET/CT with Volumetric Parameters for Staging of Prostate Cancer Patients: Erratum, Nuclear Medicine Communications: October 2021 - Volume 42 - Issue 10 - p 1186 (doi)
  36. LIFEx-texture: Flaus, A., Habouzit, V., De Leiris, N. et al. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep 11, 18795 (2021) (doi)
  37. LIFEx-texture: Shozo Yamashita, Koichi Okuda, Tetsu Nakaichi, Haruki Yamamoto and Kunihiko Yokoyama.Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET. Journal of Nuclear Medicine Technology March 2021, 49 (1) 58-64 (doi)
  38. LIFEx-texture: Mengmeng Yan, Weidong Wang. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Science Progress
    2021, Vol. 104(1) 1-10 (doi)
  39. LIFEx-texture: Zhang, L., Zhao, H., Jiang, H. et al. 18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (2021) (doi)
  40. LIFEx-texture: Xu, Hanyue; Zou, Xiuhe; Zhao, Yunuo; Zhang, Tao; Tang, Youyin; Zheng, Aiping; Zhou, Xianghong; Ma, Xuelei. Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography. Technol Cancer Res Treat ; 20: 15330338211039125, 2021 (doi)
  41. LIFEx-viewer: Wallis, D., Soussan, M., Lacroix, M. et al. An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging (2021). (doi)
  42. LIFEx-texture: Zhong-Wei Chen, Kun Tang, You-Fan Zhao, Yang-Zong Chen, Liang-Jie Tang, Gang Li, Ou-Yang Huang, Xiao-Dong Wang, Giovanni Targher, Christopher D. Byrne, Xiang-Wu Zheng and Ming-Hua Zheng. Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study. Int. J. Med. Sci. 2021, Vol 18(16): 3624-3630 (doi)
  43. LIFEx-texture: Yang, N., Liu, F., Li, C. et al. Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images. Sci Rep 11, 17885 (2021) (doi)
  44. LIFEx-texture: Chong, G.O.; Park, S.-H.; Jeong, S.Y.; Kim, S.J.; Park, N.J.-Y.; Lee, Y.H.; Lee, S.-W.; Hong, D.G.; Park, J.Y.; Han, H.S. Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer. Diagnostics 2021, 11, 1517 (doi)
  45. LIFEx-texture: Yuki Arita and Soichiro Yoshida and Thomas C. Kwee and Hirotaka Akita and Shigeo and Yuki Iwaita and Kiyoko Mukai and Shunya Matsumoto and Ryo and Ryota and Ryuichi Mizuno and Yasuhisa Fujii and Mototsugu Oya and Masahiro Jinzaki. Diagnostic Value of Texture Analysis of Apparent Diffusion Coefficient Maps for Differentiating Fat-Poor Angiomyolipoma from Non-Clear-Cell Renal Cell Carcinoma. European Journal of Radiology. p109895, 0720-048X; 2021 (doi)
  46. LIFEx-texture: Beaumont, H., Iannessi, A., Cucchi, J.M. et al. Intra-scan inter-tissue variability can help harmonize radiomics features in CT. Eur Radiol (2021) (doi)
  47. LIFEx-texture: Ren, H., Mori, N., Mugikura, S. et al. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. Abdom Radiol (2021) (doi)
  48. LIFEx-texture: Tang, Y., Zhang, T., Zhou, X. et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Onc 19, 45 (2021) (doi)
  49. LIFEx-texture: Lafata, K.J., Wang, Y., Konkel, B. et al. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (2021) (doi)
  50. LIFEx-texture: Su C-W, Lee J-C, Chang Y-F, et al. Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer. Tumori Journal. August 2021 (doi)
  51. LIFEx-texture: Javier González-Viguera, Gabriel Reynés-Llompart, Alicia Lozano. Outcomes and computed tomography radiomic features extraction in soft tissue sarcomas treated with neoadjuvant radiation therapy. Reports of practical oncology and radiotherapy. 2021-08-13 (doi)
  52. LIFEx-texture: Karmazanovsky, G., Gruzdev, I., Tikhonova, V. et al. Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol med (2021) (doi)
  53. LIFEx-viewer: Chen C, Cheng Y, Xu J, Zhang T, Shu X, Huang W, Hua Y, Zhang Y, Teng Y, Zhang L, Xu J. Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method. Journal of Personalized Medicine. 2021; 11(8):786 (doi)
  54. LIFEx-texture: Bracci, S., Dolciami, M., Trobiani, C. et al. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. Radiol med (2021) (doi)
  55. LIFEx-texture: Tang, Y., Zhang, T., Zhou, X. et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Onc 19, 45 (2021) (doi)
  56. LIFEx-texture: Hainan Ren, Naoko Mori, Shunji Mugikura, Hiroaki Shimizu, Sakiko Kageyama, Masatoshi Saito, Kei Takase. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2‑weighted imaging. Abdominal Radiology. 20 July 2021 (doi)
  57. LIFEx-texture: Orkun Sarioglu, Fatma C Sarioglu, Ahmet E Capar, Demet Fb Sokmez, Berna D Mete, Umit Belet. Clot-based radiomics features predict first pass effect in acute ischemic stroke. Interv Neuroradiol. 2021 May 18;15910199211019176 (doi)
  58. LIFEx-texture: Baba, A., Kessoku, H., Akutsu, T. et al. Pre-treatment MRI predictor of high-grade malignant parotid gland cancer. Oral Radiol (2021)(doi)
  59. LIFEx-texture: Chen B, Chen C, Wang J, Teng Y, Ma X and Xu J (2021) Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques. Front. Oncol. 11:521313. (doi)
  60. LIFEx-texture: Girot, C., Volk, A., Walczak, C. et al. New method for quantification of intratumoral heterogeneity: a feasibility study on Ktrans maps from preclinical DCE-MRI. Magn Reson Mater Phy (2021) (doi)
  61. LIFEx-texture: Elkilany, A., Fehrenbach, U., Auer, T.A. et al. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep 11, 10778 (2021) (doi)
  62. LIFEx-texture: Timothée Zaragori, Julien Oster, Veronique Roch, Gabriela Hossu, Mohammad Bilal Chawki, Rachel Grignon, Celso Pouget, Guillaume Gauchotte, Fabien Rech, Marie Blonski, Luc Taillandier, Laëtitia Imbert and Antoine Verger. 18F-FDOPA PET for the non-invasive prediction of glioma molecular parameters: a radiomics study. Journal of Nuclear Medicine May 2021, jnumed.120.261545 (doi)
  63. LIFEx-MTV: François Allioux, Damaj Gandhi, Jean-Pierre Vilque, Cathy Nganoa, AnneClaire Gac, Nicolas Aide & Charline Lasnon (2021): End-of-treatment 18F-FDG PET/CT in diffuse large B cell lymphoma patients: ∆SUV outperforms Deauville score, Leukemia & Lymphoma (doi)
  64. LIFEx-viewer: Francesco Bianconi, Mario Luca Fravolini, Sofia Pizzoli, Isabella Palumbo, Matteo Minestrini, Maria Rondini, Susanna Nuvoli, Angela Spanu, Barbara Palumbo. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant Imaging Med Surg 2021;11(7):3286-3305 (doi)
  65. LIFEx-texture: Mahmoud M.A., Shihab M., Saad SS., Elhussiny F., Houseni M. Imaging differentiation of malignant hepatic tumors: radiomics and metabolic features of 18F-FDG PET/CT. REJR 2021; 11(2):165-170 (doi)
  66. LIFEx-texture: Friconnet, G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol 4, 105–115 (2021)(doi)
  67. LIFEx-texture: Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol (2021) (doi)
  68. Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol 2021 (doi)
  69. LIFEx-texture: P Vincenta, ME. Maeder, B Hunt, B Linn, T MangelsDick, T Hasan, KK. Wan and BW. Pogue. CT Radiomic Features of Photodynamic Priming in Clinical Pancreatic Adenocarcinoma Treatment. 2021 Phys. Med. Biol (doi)
  70. LIFEx-texture: Lee JW, Park S-H, Ahn H, Lee SM, Jang SJ. Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT. Cancers. 2021; 13(14):3563 (doi)
  71. LIFEx-texture: Tu SJ, Tran VT, Teo JM, Chong WC, Tseng JR. Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11 C-choline PET/MRI acquisition in prostate cancer patients. Med Phys. 2021 Jul 2 (doi)
  72. LIFEx-texture: Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848 (doi)
  73. LIFEx-texture: Kimura, K., Yoshida, S., Tsuchiya, J. et al. Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer. Eur Radiol (2021)(doi)
  74. LIFEx-texture: Mitamuraa K, Norikanea T, Yamamotoa Y＊, Nishishitaa AI, Kobataa T, Fujimotoa K, Takamia Y, Kudomib N, Hoshikawac H , and Nishiyamaa Y. Texture Indices of 18F-FDG PET/CT for Differentiating Squamous Cell Carcinoma and Non-Hodgkin’s Lymphoma of the Oropharynx. Acta Med. Okayama, 2021 Vol. 75, No. 3, pp. 351-356 (doi)
  75. LIFEx-texture: Costa, G.; Cavinato, L.; Masci, C.; Fiz, F.; Sollini, M.; Politi, L.S.; Chiti, A.; Balzarini, L.; Aghemo, A.; di Tommaso, L.; et al. Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases. Cancers 2021, 13, 3077 (doi)
  76. LIFEx-texture: The Clinical Impact of the Late Imaging with 18F-Fluorodeoxyglucose PET Texture Analysis in Invasive Lobular Breast Cancer. FO FALAY, H SEYMEN - Turk J Oncol 2021;36(3):273–84 (doi)
  77. LIFEx-texture: Fiz, F.; Costa, G.; Gennaro, N.; la Bella, L.; Boichuk, A.; Sollini, M.; Politi, L.S.; Balzarini, L.; Torzilli, G.; Chiti, A.; et al. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the “Radiological” Tumour Microenvironment. Diagnostics 2021, 11, 1162 (doi)
  78. LIFEx-texture: Y Chen, H Li, J Feng, S Suo, Q Feng, J Shen. A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease - Journal of Inflammation Research, june 2021 (doi)
  79. LIFEx-texture: Orlhac, F.; Buvat, I. Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848. Cancers 2021, 13, 3037 (doi)
  80. LIFEx-texture: Ai Y, Zhang J, Jin J, Zhang J, Zhu H and Jin X (2021) Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors. Front. Oncol. 11:610742. (doi)
  81. LIFEx-texture: Gill A.B., Rundo L, Wan  JCM, Lau D, Zawaideh JP,  Woitek R, Zaccagna F,  Beer L, Gale D, Sala E, Couturier DL, Corrie PG, Rosenfeld N, Gallagher FA. Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma. 2020 Cancers Nov 24;12(12):3493 (doi).
  82. LIFEx-texture: Karahan Şen, N.P., Aksu, A. & Çapa Kaya, G. A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Ann Nucl Med (2021) (doi)
  83. LIFEx-texture: Masci, G.M., Iafrate, F., Ciccarelli, F. et al. Tocilizumab effects in COVID-19 pneumonia: role of CT texture analysis in quantitative assessment of response to therapy. Radiol med (2021) (doi)
  84. LIFEx-texture: Aboelyazid Elkilany, Uli Fehrenbach, Timo Alexander Auer, Tobias Müller, Wenzel Schöning, Bernd Hamm1 & Dominik Geisel.  A radiomics‑based model to classify the etiology of liver cirrhosis using gadoxetic acid‑enhanced MRIScientific Reports | (2021) 11:10778 (doi)
  85. LIFEx-viewer: Bordonneet al. High-quality brain perfusion SPECT images may be achieved with a high-speed recording using 360° CZT camera. EJNMMI Physics (2020) 7:65 (doi)
  86. LIFEx-texture: Kim, M., Gu, W., Nakajima, T. et al. Texture analysis of [18F]-fluorodeoxyglucose-positron emission tomography/computed tomography for predicting the treatment response of postoperative recurrent or metastatic oral squamous cell carcinoma treated with cetuximab. Ann Nucl Med (2021) (doi)
  87. LIFEx-texture: Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status by Lukas Lenga,Simon Bernatz,Simon S. Martin,Christian Booz,Christine Solbach,Rotraud Mulert-Ernst,Thomas J. Vogl andDoris Leithner.Cancers 2021, 13(10), 2431 (doi)
  88. LIFEx-texture: MA Mazzei, L Di Giacomo, GBagnacci, V Nardone, & al. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer—a multicenter study of GIRCG (Italian Research Group for Gastric Cancer) Quant Imaging Med Surg2021;11(6):2376-238 (doi)
  89. LIFEx-texture: Friconnet, G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol (2021) (doi)
  90. LIFEx-texture: Tomita, H., Yamashiro, T., Heianna, J. et al. Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography. Eur Radiol (2021) (doi)
  91. LIFEx-texture: Daria Ripani, Carmelo Caldarella, Tommaso Za, Elena Rossi, Valerio De Stefano, Alessandro Giordano. Progression to symptomatic multiple myeloma predicted by texture analysis-derived parameters in patients without focal disease at 18F-FDG PET/CT. Clinical Lymphoma Myeloma and Leukemia 2021 (doi)
  92. LIFEx-texture: Mazzei, M., Giacomo, L., Bagnacci, G., Nardone, V., Gentili, F., Lucii, G., Tini, P., Marrelli, D., Morgagni, P., Mura, G., Baiocchi, G., Pittiani, F., Volterrani, L., Roviello, F. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer - a multicenter study of GIRCG (Italian Research Group for Gastric Cancer ; Quant Imaging Med Surg 2021;11(6):2376-2387 (doi)
  93. LIFEx-texture: Markich, R., Palussière, J., Catena, V. et al. Radiomics complements clinical, radiological, and technical features to assess local control of colorectal cancer lung metastases treated with radiofrequency ablation. Eur Radiol (2021) (doi)
  94. LIFEx-texture: Hotta, M., Minamimoto, R., Gohda, Y. et al. Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med (2021) (doi)
  95. LIFEx-texture: Crombé, A., Buy, X., Han, F., Toupin, S. and Kind, M. (2021), Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping‐Based Radiomics Features: A Comparative Study. J Magn Reson Imaging (doi)
  96. LIFEx-MTV: Have Volume-based Parameters of Positron Emission Tomography/Computed Tomography Prognostic Relevance for Patients With Potentially Platinum-responsive Recurrent Ovarian Cancer? A Single Center Italian Study. A Gadduci, E Simonetti, F Guidoccio, G Manca, A Giorgetti, T Depalo, S Cosio, M Miccoli and D Volterrani. Anticancer Research 41: 1937-1944 (2021) (doi)
  97. LIFEx-texture: Paolo Florent Felisaz, Giulia Colelli, Elena Ballante, Francesca Solazzo, Matteo Paoletti, Giancarlo Germani, Francesco Santini, Xeni Deligianni, Niels Bergsland, Mauro Monforte, Giorgio Tasca, Enzo Ricci, Stefano Bastianello, Silvia Figini, Anna Pichiecchio ; Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy ; European Journal of Radiology 134 (2021) 109460 (doi)
  98. LIFEx-texture: Nakajo, M., Jinguji, M., Tani, A. et al. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer. Mol Imaging Biol (2021) (doi)
  99. LIFEx-texture: Claudia E. Weber, Matthias Wittayer, Matthias Kraemer, Andreas Dabringhaus, Michael Platten, Achim Gass, Philipp Eisele ; Quantitative MRI texture analysis in chronic active multiple sclerosis lesions, Magnetic Resonance Imaging, Volume 79, 2021, Pages 97-102 (doi)
  100. LIFEx-texture: Xue, B., Wu, S., Zhang, M. et al. A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis. Abdom Radiol (2021) (doi)
  101. LIFEx-texture: Krajnc, D.; Papp, L.; Nakuz, T.S.; Magometschnigg, H.F.; Grahovac, M.; pielvogel, C.P.; Ecsedi, B.; Bago-Horvath, Z.; Haug, A.; Karanikas, G.; et al. Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers 2021, 13, 1249 (doi)
  102. LIFEx-texture: Thuillier, P.; Liberini, V.; Rampado, O.; Gallio, E.; De Santi, B.; Ceci, F.; Metovic, J.; Papotti, M.; Volante, M.; Molinari, F.; et al. Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms. Biomedicines 2021, 9, 281 (doi)
  103. LIFEx-texture: Sollini, M., Kirienko, M., Cavinato, L. et al. Methodological framework for radiomics applications in Hodgkin’s lymphoma. European J Hybrid Imaging 4, 9 (2020) (doi)
  104. LIFEx-texture: Amandine Crombé, Xavier Buy, Fei Han, Solenn Toupin and Michèle Kind. ORIGINAL RESEARCHAssessment of Repeatability,Reproducibility, and Performances of T2Mapping-Based Radiomics Features:A Comparative Study. J. MAGN. RESON. IMAGING 2021 (doi)
  105. LIFEx-texture: Liberini, V., De Santi, B., Rampado, O. et al. Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor. EJNMMI Phys 8, 21 (2021) (doi)
  106. LIFEx-texture: E. Forde, M. Leech, C. Robert, E. Herron, L. Marignol. Influence of inter-observer delineation variability on radiomic features of the parotid gland. Physica Medica 82 (2021) 240-248 (doi)
  107. LIFEx-texture: Junjie Hang, Kequn Xu, Ruohan Yin, Yueting Shao, Muhan Liu, Haifeng Shi, Xiaoyong Wang3 and Lixia Wu. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases. Journal of Cancer 2021; 12(8): 2351-2358. doi: 10.7150/jca.49569 (doi)
  108. LIFEx-texture: Zhang Tao, Zhang YueHua, Liu Xinglong, Xu Hanyue, Chen Chaoyue, Zhou Xuan, Liu Yichun, Ma Xuelei. Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades. Front. Oncol., 11 February 2021 (10)-3227 (doi)
  109. LIFEx-texture: Liberini, V., De Santi, B., Rampado, O. et al. Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor. EJNMMI Phys 8, 21 (2021) (doi)
  110. LIFEx-texture: Hayato Tomita, Tsuneo Yamashiro, Gyo Iida, Maho Tsubakimoto, Hidefumi Mimura and Sadayuki Murayama. Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma. Nagoya J. Med. Sci. 83. 135–149, 2021 (doi)
  111. LIFEx-texture-MTV: Hirata, Kenji and Tamaki, Nagara. Quantitative FDG PET Assessment for Oncology Therapy. Cancers 2021, 13(4) 869 (doi)
  112. LIFEx-texture: Cepeda, S., García-García, S., Arrese, I. et al. Relationship between the overall survival in glioblastomas and the radiomic features of intraoperative ultrasound: a feasibility study. J Ultrasound (2021) (doi)
  113. LIFEx-texture: Larobina, M., Megna, R. & Solla, R. Comparison of three freeware software packages for 18F-FDG PET texture feature calculation. Jpn J Radiol (2021) (doi)
  114. LIFEx-texture: Yoon, H., Ha, S., Kwon, S.J. et al. Prognostic value of tumor metabolic imaging phenotype by FDG PET radiomics in HNSCC. Ann Nucl Med (2021) (doi)
  115. LIFEx-texture: Virginia Liberini , Osvaldo Rampado, Elena Gallio, Bruno De Santi, Francesco Ceci, Beatrice Dionisi, Dionisi, Beatrice, Philippe Thuillier, Libero Ciuffreda, Alessandro Piovesan, Federica Fioroni, Annibale Versari, Filippo Molinari, Désirée Deandreis ; 68Ga-DOTATOC PET/CT-Based Radiomic Analysis and PRRT Outcome: A Preliminary Evaluation Based on an Exploratory Radiomic Analysis on Two Patients. Front. Med., 26 January 2021 (doi)
  116. LIFEx-texture: Sarioglu, O., Sarioglu, F.C., Capar, A.E. et al. The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy. Eur Radiol (2021) (doi)
  117. LIFEx-texture: Youyin Tang, Tao Zhang, Yunuo Zhao, Zheyu Chen and Xuelei Ma. Development and validation of a comprehensive radiomics nomogram for prognostic prediction of primary hepatic sarcomatoid carcinoma after surgical resection. International Journal of Medical Sciences 2021; 18(7): 1711-1720 (doi)
  118. LIFEx-texture: Jeonghyun Kang, Jae-Hoon Lee, Hye Sun Lee, Eun-Suk Cho, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Chihyun Park, Yunku Yeu, Jean R. Clemenceau, Sunho Park, Hongming Xu, Changjin Hong and Tae Hyun Hwang. Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer. Cancers 2021, 13, 392 (doi)
  119. LIFEx-texture: Zhou, Y., Ma, Xl., Zhang, T. et al. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging (2021) (doi)
  120. LIFEx-texture: Beaumont, H., Iannessi, A., Bertrand, AS. et al. Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging. Eur Radiol (2021) (doi)
  121. LIFEx-texture: Alongi, P., Stefano, A., Comelli, A. et al. Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients. Eur Radiol (2021) (doi)
  122. LIFEx-texture: A Radiomics-Based Imaging Tool to Monitor Tumor-Lymphocyte Infiltration and Outcome in Cancer Patients Treated by Anti-PD-1/PD-L1. United States Patent Application 20210003555. Ferte Charles (Bethesda, MD, US), Limkin Elaine Johanna (Cachan, FR), Sun Roger (Paris, FR), Deutsch Eric (Paris, FR) (doi)
  123. LIFEx-texture: Wu, YJ., Liu, YC., Liao, CY. et al. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules. Sci Rep 11, 66 (2021) (doi)
  124. LIFEx-MTV: Hotta, M., Minamimoto, R., Toyohara, J. et al. Efficacy of cell proliferation imaging with 4DST PET/CT for predicting the prognosis of patients with esophageal cancer: a comparison study with FDG PET/CT. Eur J Nucl Med Mol Imaging (2021) (doi)
  125. LIFEx-texture: Yoo, S., Kang, S., Yoon, J. et al. Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy. Sci Rep 11, 296 (2021) (doi)
  126. LIFEx-MTV: Hotta, M., Minamimoto, R., Toyohara, J. et al. Efficacy of cell proliferation imaging with 4DST PET/CT for predicting the prognosis of patients with esophageal cancer: a comparison study with FDG PET/CT. Eur J Nucl Med Mol Imaging (2021) (doi)
  127. LIFEx-texture: Nicolas Aide, Nicolas Elie, Cécile Blanc-Fournier, Christelle Levy, Thibault Salomon and Charline Lasnon ; Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers ; Front. Oncol., 12 January 2021 (doi)
  128. LIFEx-MTV: Prigent, K., Lasnon, C., Ezine, E. et al. Assessing immune organs on 18F-FDG PET/CT imaging for therapy monitoring of immune checkpoint inhibitors: inter-observer variability, prognostic value and evolution during the treatment course of melanoma patients. Eur J Nucl Med Mol Imaging (2021) (doi)
  129. LIFEx-texture: Li, Y., Zhang, Y., Fang, Q. et al.  Radiomics analysis of [18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma. Eur J Nucl Med Mol Imaging (2021) (doi)
  130. LIFEx-texture: Sheen, H., Kim, J.S., Lee, J.K. et al. A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (2021) (doi)
  131. LIFEx-texture: Sheen, H., Kim, J.S., Lee, J.K. et al A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (2021). (doi)
  132. LIFEx-texture: Peter McAnena, Brian Moloney, Robert Browne, Niamh O’Halloran, Leon Walsh, Sinead Walsh, Declan Sheppard, Karl Sweeney, Michael Kerin, Aoife Lowery. A Radiomic Model To Classify Response To Neoadjuvant Chemotherapy in Breast Cancer. Research Square 2021 (doi)
  133. LIFEx-texture: Xue, Xiu-Qing; Yu, Wen-Ji; Shao, Xiao-Liang; Li, Xiao-Feng; Niu, Rong; Zhang, Fei-Fei; Shi, Yun-Mei; Wang, Yue-Tao Radiomics model based on preoperative 18F-fluorodeoxyglucose PET predicts N2-3b lymph node metastasis in gastric cancer patients, Nuclear Medicine Communications: December 23, 2021 - (doi)
  134. LIFEx-texture: Nakajo, M., Jinguji, M., Tani, A. et al. Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients. Abdom Radiol (2021) (doi)
  135. LIFEx-texture: Navid Hasani, Sriram S. Paravastu, Faraz Farhadi, Fereshteh Yousefirizi, Michael A. Morris, Arman Rahmim, Mark Roschewski, Ronald M. Summers, Babak Saboury. Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions). PET Clinics. Vol 17, Issue 1, P145-174, Jan 01, 2022 (doi)
  136. LIFEx-texture: Kevin Ma, Stephanie A, Harmon, Ivan S, Klyuzhin, Arman Rahmim, DABSNM Baris Turkbey. Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer. PET Clinics Vol 17, Issue 1, P137-143, Jan 01, 2022 (doi)
  137. LIFEx-texture : Isil Basara Akin, Hakan Abdullah Ozgul, Canan Altay, Merih Guray Durak, Suleyman Ozkan Akso, Ali Ibrahim Sevinc, Mustafa Secil, Hakan Gulmez, Pinar Balci. Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors. Ultraschall Me 2021 (doi)
  138. LIFEx-texture: Dhirajlal Rajgor A., Patel S, McCulloch D, Obara B, Bacardit J, McQueen A, Aboagye E, Ali T, O’Hara J and Winston Hamilton D. The application of radiomics in laryngeal cancer.  The British Institute of Radiology. 29 Sep 2021 (doi)
  139. LIFEx-viewer: Thuilliera P, Liberinia V, Grimaldia S, Rampado O, Gallio E, De Santi B, Arvat E, Piovesan A, Filippi R, Molinari F, Deandreis A. Valeur pronostique des paramètres volumétriques corps entier extraits de la TEP/TDM au 68Ga-DOTATOC dans les tumeurs neuroendocrines bien différenciées. JO  - Annales d'Endocrinologie Volume 82, issue 5, October 2021, Page 274 (doi)
  140. LIFEx-texture: Thuillier, Philippe; Bourhis, David; Schick, Ulrike; Alavi, Zarrin; Guezennec, Catherine; Robin, Philippe; Kerlan, Véronique; Salaun, Pierre-Yve; Abgral, Ronan. Diagnostic value of positron-emission tomography textural indices for malignancy of 18F-fluorodeoxyglucose-avid adrenal lesions. Q J Nucl Med Mol Imaging ; 65(1): 79-87, 2021 Mar (doi)
  141. LIFEx-texture: Hyun, Seung Hyup; Ahn, Mi Sun; Koh, Young Wha; Lee, Su Jin. A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer. Clinical Nuclear Medicine: December 2019 - Volume 44 - Issue 12 - p 956-960 (doi)
  142. LIFEx-texture: Sha ZHU, Hui XU, Chuyu SHEN, Yingjie WANG, Wenting XU, Shihao DUAN, Hanxiao CHEN, Xuejin OU, Linyan CHEN, Xuelei MA. Differential diagnostic ability of 18F-FDG PET/CT radiomics features between renal cell carcinoma and renal lymphoma. The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2021 March;65(1):72-8 (doi)
  143. LIFEx-texture: Tutino, Francesca; Puccini, Giulia; Linguanti, Flavia; Puccini, Benedetta; Rigacci, Luigi; Kovalchuk, Sofya; Sciagrà, Roberto; Berti, Valentina. Baseline metabolic tumor volume calculation using different SUV thresholding methods in Hodgkin lymphoma patients: interobserver agreement and reproducibility across software platforms. Nucl Med Commun ; 42(3): 284-291, 2021 Mar 01 (doi)
  144. LIFEx-texture: Vernuccio, F., Cannella, R., Bartolotta, T.V. et al. Advances in liver US, CT, and MRI: moving toward the future. Eur Radiol Exp 5, 52 (2021) (doi)
  145. LIFEx-MTV: Annovazzi, A., Ferraresi, V., Rea, S. et al. Prognostic value of total metabolic tumour volume and therapy-response assessment by [18F]FDG PET/CT in patients with metastatic melanoma treated with BRAF/MEK inhibitors. Eur Radiol (2021) (doi)
  146. LIFEx-texture: Kotaro Ito DDS, PhD , Hirotaka Muraoka DDS, PhD , Naohisa Hirahara DDS, PhD , Eri Sawada DDS, PhD , Satoshi Tokunaga DDS, PhD , Takashi Kaneda DDS, PhD , Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis, Oral Surg Oral Med Oral Pathol Oral Radiol (2021), (doi)
  147. LIFEx-texture: Mahmoud M.A., Shihab M., Saad SS., Elhussiny F., Houseni M. Effect of standardized uptake value discretization on radiomics features of liver tumors using 18FDG-PET/CT scan. REJR 2021; 11(3):132-137. DOI: 10.21569/2222-7415-2021-11-3-132-137 (doi)
  148. LIFEx-texture: Zhang, X., Chen, L., Jiang, H. et al. A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT. Eur J Nucl Med Mol Imaging (2021). (doi)
  149. LIFEx-texture: Mengmeng Yan and Weidong Wang. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Science Progress 2021, Vol. 104(1) 1–10 (doi)
  150. LIFEx-texture: Wallis, D., Soussan, M., Lacroix, M. et al. An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging (2021). (doi)
  151. LIFEx-texture: Piaopaio Ying, Wenyi Jin, Xiaoli Wu and Weiyang Cai. Association between CT-Quantified Body Composition and Recurrence, Survival in Nonmetastasis Colorectal Cancer Patients Underwent Regular Chemotherapy after Surgery" recently published in BioMed Research International. Artificial Intelligence for Medical Image Analysis.Volume 2021, Article ID 6657566 (doi)
  152. LIFEx-texture: Samy Ammari, Stephanie Pitre Champagnat, Laurent Dercle, sylvain reuze, sebastien Diffetocq, tite mokoyoko, salma moalla, sara lakiss, joya hadchiti, emilie chouzenoux, corinne balleyguier, nathalie lassau, francois bidault. Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging. Frontiers in Oncology, Frontiers, 2021 (doi)
  153. LIFEx-texture: Xiaozhen Y, Chunwang Y, Yinghua Z, Zhenchang W. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellularcarcinomaA SQUIRE-compliant study; Medicine (2021) 100:19 (doi)
  154. LIFEx-texture: Roberto Cannella, Riccardo Sartoris, Jules Grégory, Lorenzo Garzelli, Valérie Vilgrain, Maxime Ronot and Marco Dioguardi Burgio. Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice. The British Journal of Radiology. Vol. 94, No. 1122 (doi)
  155. LIFEx-texture: Effect of Chemotherapy on Liver Metabolism as Measured by PET/CT scan. Shaimaa A. Ahmed, AIDA Salama, Mohamed Mohamed Houseni, Asmaa A A Elsheshiny. Egypt. J. Biophys. Biomed. Engng. Vol. 21, No.1,pp.75-85 (2020) (doi)
  156. LIFEx-texture: Xuehan Hu, Xun Sun, Fan Hu, Fang Liu, Weiwei Ruan, Tingfan Wu, Rui An, Xiaoli Lan. Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson’s disease and multiple system atrophy (doi)
  157. LIFEx-texture: Yuhan Yang, Xuelei Ma, Yixi Wang, Xinyan Ding. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest. Updates in surgery. 2021 May 18 (doi)

Publications 2020 - Journal papers (95)

  1. LIFEx-texture: Fornacon-Wood I, Mistry H, Ackermann CJ, Blackhall F, McPartlin A, Faivre-Finn C, Price GJ, O'Connor JPB. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol. 2020 Nov;30(11):6241-6250. https://doi.org/10.1007/s00330-020-06957-9. Epub 2020 Jun 1. PMID: 32483644; PMCID: PMC7553896.
  2. LIFEx-texture: Gruzdev, I S; Zamyatina, K A; Tikhonova, V S; Kondratyev, E V; Glotov, A V; Karmazanovsky, G G; Revishvili, A Sh. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol ; 133: 109371, 2020 Dec (doi)
  3. LIFEx-texture: Liu Zefan, Zhu Guannan, Jiang Xian, Zhao Yunuo, Zeng Hao, Jing Jing, Ma Xuelei. Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning. Frontiers in Oncology. November 2020, Vol 10, Article 604288 (doi)
  4. LIFEx-texture: Philipp Lohmann, Anna-Katharina Meißner, Martin Kocher, Elena K Bauer, Jan-Michael Werner, Gereon R Fink, Nadim J Shah, Karl-Josef Langen, Norbert Galldiks. Feature-based PET/MRI radiomics in patients with brain tumors. Neuro-Oncology Advances, Volume 2, Issue Supplement_4, December 2020, Pages iv15–iv21 (doi)
  5. LIFEx-MTV: 18F-FDG-PET dissemination features in diffuse large B cell lymphoma are predictive of outcome ; Anne-Ségolène Cottereau, Christophe Nioche, Anne-Sophie Dirand, Jérôme Clerc, Franck Morschhauser, Olivier Casasnovas, Michel Meignan, Irène Buvat ; Journal of Nuclear Medicine, published on June 14, 2019 (doi)
  6. LIFEx-texture: Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer ; Cucchiara Federico, Del Re Marzia, Valleggi Simona, Romei Chiara, Petrini Iacopo, Lucchesi Maurizio, Crucitta Stefania, Rofi Eleonora, De Liperi Annalisa, Chella Antonio, Russo Antonio, Danesi Romano ; Front. Oncol. 10:593831(doi)
  7. LIFEx-texture: Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers ; Santiago Cepeda, Ignacio Arrese, Sergio Garcia-Garcia, Maria Velasco-Casares, Trinidad Escudero-Caro, Tomas Zamora, Rosario Sarabia ;  World Neurosurgery ; Available online 28 November 2020 (doi)
  8. LIFEx-texture: Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Ren, C., Li, M., Zhang, Y. et al. Cancer Imaging 20, 86 (2020) (doi)
  9. LIFEx-texture: CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer ; Eriko KodaTsuneo Yamashiro, Rintaro Onoe, Hiroshi Handa, Shinya Azagami, Shoichiro Matsushita, Hayato Tomita, Takeo Inoue, Masamichi Mineshita ; PlosOne Dec 2, 2020 (doi)
  10. LIFEx-texture: Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma. Gill, A.B.; Rundo, L.; Wan, J.C.M.; Lau, D.; Zawaideh, J.P.; Woitek, R.; Zaccagna, F.; Beer, L.; Gale, D.; Sala, E.; Couturier, D.-L.; Corrie, P.G.; Rosenfeld, N.; Gallagher, F.A. Cancers 2020, 12, 3493 (doi)
  11. LIFEx-texture:Texture indices of 4′-[methyl-11C]-thiothymidine uptake predict p16 status in patients with newly diagnosed oropharyngeal squamous cell carcinoma: comparison with 18F-FDG uptake. Ihara-Nishishita, A., Norikane, T., Mitamura, K. et al.  European J Hybrid Imaging 4, 20 (2020) (doi)
  12. LIFEx-texture: Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer. Toyama, Y., Hotta, M., Motoi, F. et al. Sci Rep 10, 17024 (2020) (doi)
  13. LIFEx-Texture: Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells ; Sun R, Sundahl N, Hecht M, et al ; Journal for ImmunoTherapy of cancer 2020;8:e001429 (pdf)
  14. LIFEx-Texture: Saint Martin, MJ., Orlhac, F., Akl, P. et al. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. Magn Reson Mater Phy (2020) (doi)
  15. LIFEx-Texture: Intensity harmonization techniques influence radiomics features and radiomics‑based predictions in sarcoma patients ; Crombé, A., Kind, M., Fadli, D. et al. ; Sci Rep 10, 15496 (2020) (doi)
  16. LIFEx-Viewer: High-quality brain perfusion SPECT images may be achieved with a high-speed recording using 360° CZT camera. Bordonne, M., Chawki, M.B., Marie, P. et al. EJNMMI Phys 7, 65 (2020) (doi)
  17. LIFEx-Texture: Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer ; Lan Lei, Junqi Sun, Prateek Prasanna, Chunling Liu, Chuan Huang ; Academic Radiology ; online 5 November 2020 (doi)
  18. LIFEx-Texture: Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. Blanc-Durand, P., Jégou, S., Kanoun, S. et al. Eur J Nucl Med Mol Imaging (2020) (doi)
  19. LIFEx-Texture: Contrast-Enhanced CT-based Textural Parameters as Potential Prognostic Factors of Survival for Colorectal Cancer Patients Receiving Targeted Therapy. Zhao, Y., Yang, J., Luo, M. et al. Mol Imaging Biol 2020 (doi)
  20. LIFEx-Texture: Methodological Study to Investigate the Potential of Ultrasound-Based Elastography and Texture as Biomarkers to Monitor Liver Tumors ; Salma Moalla, Charly Girot, Stéphanie Franchi-Abella, Samy Ammari, Corinne Balleyguier, Nathalie Lassau and Stéphanie Pitre-Champagnat ; Diagnostics 2020, 10, 811 (doi)
  21. LIFEx-Texture: Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer. Toyama, Y., Hotta, M., Motoi, F. et al. Sci Rep 10, 17024 (2020) (doi)
  22. LIFEx-Texture: Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma. J.Zhong, R.Frood, P.Brown, H.Nelstrop, R.Prestwich, G.McDermott, S.Currie, S.Vaidyanathan, A.F.Scarsbrook ; Clinical Radiology, Oct 2020 (doi)
  23. LIFEx-Texture: Repeatability of 18F-FDG PET Radiomic Features in Cervical Cancer ; Crandall JP, Fraum TJ, Lee M, Jiang L, Grigsby PW, Wahl RL. J Nucl Med October 2, 2020 jnumed.120.247999 (doi)
  24. LIFEx-Texture: Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques. Kulkarni, A., Carrion-Martinez, I., Dhindsa, K. et al. ; Abdom Radiol (2020) (doi)
  25. LIFEx-Texture: Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients. Crombé, A., Kind, M., Fadli, D. et al. Sci Rep 10, 15496 (2020) (doi)
  26. LIFEx-Texture: How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F, Ayache N, Frouin F,  Duron L, Buvat I. Eur Radiol (2020) (doi)
  27. LIFEx-Texture: Value of volumetric and textural analysis in predicting the treatment response in patients with locally advanced rectal cancer. Karahan Şen, N.P., Aksu, A. & Kaya, G.Ç. Ann Nucl Med (2020) (doi)
  28. LIFEx-Viewer: Early Prediction of Tumor Response to Neoadjuvant Chemotherapy and Clinical Outcome in Breast Cancer Using a Novel FDG-PET Parameter for Cancer Stem Cell Metabolism ; Chanwoo Kim, Sang-Ah Han, Kyu Yeoun Won, Il Ki Hong and Deog Yoon Kim ; J. Pers. Med. 2020, 10, 132; doi:10.3390/jpm10030132 (doi)
  29. LIFEx-Viewer: Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS. Theranostics. 2020 Aug 29;10(23):10838-10848. doi: 10.7150/thno.50283. PMID: 32929383; PMCID: PMC7482798 (doi)
  30. LIFEx-Texture: Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation ; Palumbo B, Bianconi F, Palumbo I, Fravolini ML, Minestrini M, Nuvoli S, Stazza ML, Rondini M, Spanu A ; Diagnostics 2020, 10, 696 (doi)
  31. LIFEx-Texture: Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma ; Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS.  ; Theranostics 2020; 10(23):10838-10848 (doi)
  32. LIFEx-Texture: A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer; Xian Jiang, Xiuhe Zou, Jing Sun, Aiping Zheng, Chao Su,, Contrast Media ; Molecular Imaging, vol. 2020, Article ID 5418364, 10 pages, 2020 (doi)
  33. LIFEx-Texture: Association between immunotherapy biomarkers and glucose metabolism from F-18 FDG PET ; Kim BS, Kang J, Jun S, Im H, Pak K, Kim GH, Heo BJ, Kim YH ; European Review for Medical and Pharmacological Sciences ; 2020; 24: 8288-8295 (europeanreview)
  34. LIFEx-Viewer: Reciprocal change in Glucose metabolism of Cancer and Immune Cells mediated by different Glucose Transporters predicts Immunotherapy response ; Kwon Joong Na, Hongyoon Choi, Ho Rim Oh, Yoon Ho Kim, Sae Bom Lee, Yoo Jin Jung, Jaemoon Koh, Samina Park, Hyun Joo Lee, Yoon Kyung Jeon, Doo Hyun Chung, Jin Chul Paeng, In Kyu Park, Chang Hyun Kang, Gi Jeong Cheon, Keon Wook Kang, Dong Soo Lee, and Young Tae Kim ; Theranostics. 2020; 10(21): 9579–9590 (doi)
  35. LIFEx-Texture : A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer ; Xian Jiang, Xiuhe Zou, Jing Sun, Aiping Zheng, Chao Su ; Contrast Media ; Molecular Imaging, vol. 2020, Article ID 5418364, 10 pages, 2020 (doi)
  36. LIFEx-Texture: Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics. Aksu, A., Karahan Şen, N.P., Acar, E. et al.  Nucl Med Mol Imaging 2020 (doi)
  37. LIFEx-Texture: Improving the quantitative classification of Erlenmeyer flask deformities. Adusumilli, G., Kaggie, J.D., D’Amore, S. et al.  Skeletal Radiol 2020 (doi)
  38. LIFEX-Texture: Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia. Zeng Q-Q, Zheng KI, Chen J, et al.  MedComm. 2020;1–9 (doi)
  39. LIFEx-Texture: Radiomics in diffusion data: a test–retest, inter- and intra-reader DWI phantom study ; C. Dreher, T.A. Kuder, F. König, A. Mlynarska-Bujny, C. Tenconi, D. Paech, H.-P. Schlemmer, M.E. Ladd, S. Bickelhaupt ; Clinical Radiology July 25, 2020 (doi)
  40. LIFEX-Texture: Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics ; B Zheng, J Wu, Z Zhao, X Ou, P Cao, X Ma ; Contrast Media & Molecular Imaging, 2020, Article ID 3959236 (doi)
  41. LIFEx-MTV:A Case of Metastatic Hereditary Leiomyomatosis and Renal Cell Cancer Syndrome-Associated Renal Cell Carcinoma Treated with a Sequence of Axitinib and Nivolumab Following Cytoreductive Nephrectomy ; Ichiro Yonese, Masaya Ito, Kosuke Takemura, Takao Kamai, Fumitaka Koga ; Journal of Kidney Cancer and VHL 2020; 7(2): 6-10 9 (doi)
  42. LIFEx-MTV: Comparison of different automatic methods for the delineation of the total metabolic tumor volume in I–II stage Hodgkin Lymphoma. Martín-Saladich, Q., Reynés-Llompart, G., Sabaté-Llobera, A. et al.  Sci Rep 10, 12590 (2020) (doi)
  43. LIFEx-Texture: Radiomics in diffusion data: a test–retest, inter-and intra-reader DWI phantom study ; C.Dreher, T.A.Kuder, F.König, A.Mlynarska-Bujny, C.Tenconi, D.Paech, H. P. Schlemmer, M.E.Ladd, S. Bickelhaupt ; Clinical Radiology ; Available online 25 July 2020 (doi)
  44. LIFEx-Texture: Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features ; Yang Zhang, Chaoyue Chen, Zerong Tian & Jianguo Xu ; Jpn J Radiol (2020) (doi)
  45. LEFEx-Texture: MRI-based texture analysis to differentiate the most common parotid tumours; O.Sarioglu, F.C.Sarioglu, A.I. Akdogan, U.Kucuk, I.B.Arslan, I.Cukurova, Y.Pekcevik ; Clinical Radiology ; Available online 20 July 2020 (doi)
  46. LIFEx-Texture: Reinventing Radiation Therapy with Machine Learning and Imaging Bio-markers (Radiomics): state-of-the-art, challenges and perspectives ; Laurent Dercle, Theophraste Henry, Alexandre Carré, Nikos Paragios, Eric Deutsch, Charlotte Robert ; Methods ; Available online 19 July 2020 (doi)
  47. LIFEx-Texture: Prediction of survival outcome based on clinical features and pretreatment 18FDG-PET/CT for HNSCC patients ; Sayantani Ghosh, Shaurav Maulik, Sanjoy Chatterjee, Indranil Mallick, Nishant Chakravorty, JayantaMukherjee ; Computer Methods and Programs in Biomedicine ; Available online 18 July 2020, 105669 (doi)
  48. LIFEx-Texture: Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT ; Zheran Liu, Yuan Cao, Wei Diao, Yue Cheng, Zhiyun Jia, Xingchen Peng ; AGING 2020, Vol. 12, Advance (pdf)
  49. LIFEx-Texture: Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules ; Xiaonan Shao, Rong Niu, Xiaoliang Shao, Zhenxing Jiang and Yuetao Wang ; Shao et al. EJNMMI Research (2020) 10:80 (doi)
  50. LIFEx-Texture: Radiomics: A New Biomedical Workflow to Create a Predictive Model. Comelli A. et al. (2020) In: Papież B., Namburete A., Yaqub M., Noble J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham (doi)
  51. LIFEx-Texture: Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application ; JC Peeken, B Wiestler, SE Combs - Molecular Imaging in Oncology, 2020 (doi)
  52. LIFEx-Texture : Peeken J.C., Wiestler B., Combs S.E. (2020) Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application. In: Schober O., Kiessling F., Debus J. (eds) Molecular Imaging in Oncology. Recent Results in Cancer Research, vol 216. Springer, Cham (doi)
  53. LIFEx-Texture: Value of 18F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis. Yujing Hu & Xinming Zhao & Jianyuan Zhang & Jingya Han & Meng Dai ; Eur J Nucl Med Mol Imaging (2020) (doi)
  54. LIFEx-Texture, LIFEx-MTV: 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival. Polverari, G.; Ceci, F.; Bertaglia, V.; Reale, M.L.; Rampado, O.; Gallio, E.; Passera, R.; Liberini, V.; Scapoli, P.; Arena, V.; Racca, M.; Veltri, A.; Novello, S.; Deandreis, D.  Cancers 2020, 12, 1163 (doi)
  55. LIFEx-Texture: MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment ; Yang Zhang, Chaoyue Chen, Yangfan Cheng Danni Cheng Fumin Zhao and Jianguo Xu ; Contrast Media & Molecular Imaging ; Volume 2020, Article ID 2126768 (doi)
  56. LIFEx-Texture: Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions ; Giovanni Caruana, Lucas M. Pessini, Roberto Cannella, Giuseppe Salvaggio, Andréa de Barros, Annalaura Salerno, Cristina Auger & Alex Rovira ; Eur Radiol (2020) (doi)
  57. LIFEx-Texture: Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach. Girolamo Crisi Silvano Filice. Journal of Neuroimaging, May 2020 (doi)
  58. LIFEx-Texture: Current status and quality of radiomics studies in lymphoma: a systematic review. Wang, H., Zhou, Y., Li, L. et al. Eur Radiol (2020) (doi)
  59. LIFEx-Texture: Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake ; Alessandro Beleù, Giulio Rizzo, Riccardo De Robertis, Alessandro Drudi, Gregorio Aluffi, Chiara Longo, Alessandro Sarno, Sara Cingarlini, Paola Capelli, Luca Landoni, Aldo Scarpa, Claudio Bassi and Mirko D’Onofrio ; Cancers 2020, 12, 1486 (doi)
  60. LIFEx-Texture: Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base. Zhang Y, Shang L, Chen C, Ma X, Ou X, Wang J, Xia F and Xu J (2020) Front. Oncol. 10:752 (doi)
  61. LIFEx-Texture: Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels. Liyuan Fan  Qiang Cao  Xiuping Ding  Dongni Gao  Qiwei Yang  Baosheng Li ; Cancer Medicine, 27 may 2020 (doi)
  62. LIFEx-Texture: Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma. Wang, H., Zhao, S., Li, L. et al. Eur Radiol (2020) (doi)
  63. LIFEx-Texture: Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Kirienko, M., Ninatti, G., Cozzi, L. et al. Radiol med (2020) (doi)
  64. LIFEx-Texture: MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma. Sarioglu, F.C., Sarioglu, O., Guleryuz, H. et al.  Eur Radiol (2020) (doi)
  65. LIFEx (Texture+MTV): 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival ; Giulia Polverari, Francesco Ceci, Valentina Bertaglia, Maria Lucia Reale, Osvaldo Rampado, Elena Gallio, Roberto Passera, Virginia Liberini, Paola Scapoli, Vincenzo Arena, Manuela Racca, Andrea Veltri, Silvia Novello and Désirée Deandreis. Published: 5 May 2020; Cancers 2020, 12, 1163 (doi)
  66. LIFEx-Texture: Methodological framework for radiomics applications in Hodgkin Lymphoma. Martina Sollini, Margarita Kirienko, Lara Cavinato, Francesca Ricci, Matteo Biroli, Francesca Ieva, Letizia Calderoni, Elena Tabacchi, Cristina Nanni, Pier Luigi Zinzani, Stefano Fanti, Anna Guidetti, Alessandra Alessi, Paolo Corradini, Ettore Seregni, Carmelo Carlo Stella, Arturo Chiti ; Nuclear Medicine & Medical Imaging ; Hematology ; May 2020 (doi)
  67. LIFEx-Texture: Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Wang, W., Cao, K., Jin, S. et al. Eur Radiol (2020) (doi)
  68. LIFEx-Texture: A Non-invasive Method to Diagnose Lung Adenocarcinoma. Yan M and Wang W (2020) Front. Oncol. 10:602 (doi)
  69. LIFEx-Texture: Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis. Park, S., Hahm, M.H., Bae, B.K. et al. Radiat Oncol 15, 86 (2020) (doi)
  70. LIFEx-Texture: Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors ; I Basara Akin, H Ozgul, K Simsek, C Altay, M Secil, P Balci; Journal of Ultrasound in Medicine 2020 (doi)
  71. LIFEx-Texture: Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma. Chen, L., Wang, H., Zeng, H. et al. Cancer Imaging 20, 28 (2020) (doi)
  72. LIFEx-Texture: Delta-radiomics increases multicentre reproducibility: a phantom study. Nardone, V., Reginelli, A., Guida, C. et al. Med Oncol 37, 38 (2020)(doi)
  73. LIFEx-Texture: Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis. Yuri S.Velichko, Amirhossein Mozafarykhamseh, Tugce Agirlar Trabzonlu, Zhuoli Zhang, Alfred W. Rademaker, Vahid Yaghmai (doi)
  74. LIFEx-Texture: Treatment-related changes in neuroendocrine tumors as assessed by textural features derived from 68Ga-DOTATOC PET/MRI with simultaneous acquisition of apparent diffusion coefficient. Weber, M., Kessler, L., Schaarschmidt, B. et al.  BMC Cancer 20, 326 (2020) (doi)
  75. LIFEx-Texture: Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy. Aide, N., Fruchart, C., Nganoa, C. et al. ; Eur Radiol (2020) (doi)
  76. LIFEx-Texture: A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound ; Nobuyuki Kagiyama, Sirish Shrestha, Jung Sun Cho, Muhammad Khalil, Yashbir Singh, Abhiram Challa, Grace Casaclang-Verzosa, Partho P. Sengupta ; EBioMedicine 54 (2020) 102726 (doi)
  77. LIFEx-Texture: Immunotherapy in Metastatic Colorectal Cancer: Could the Latest Developments Hold the Key to Improving Patient Survival? Damilakis, E.; Mavroudis, D.; Sfakianaki, M.; Souglakos, J. ; Cancers 2020, 12, 889 (mdpi)
  78. LIFEx-Texture: Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation. Mosconi, C., Cucchetti, A., Bruno, A. et al.  Eur Radiol (2020) (doi)
  79. LIFEx-Texture: High-Dimensional Statistical Learning and Its Application to Oncological Diagnosis by Radiomics ; Bouveyron C. (2020) ;  In: Nordlinger B., Villani C., Rus D. (eds) Healthcare and Artificial Intelligence. Springer, Cham (doi)
  80. LIFEx-Texture: Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine? ; Nicolas Giraud, Paul Sargos, Nicolas Leduc, Olivier Saut, Te Vuong and Veronique Vendrely ; Appl. Sci. 2020, 10, 1988; (doi)
  81. LIFEx-Texture: Magnetic resonance imaging assessment of chemotherapy-related adipocytic maturation in myxoid/round cell liposarcomas: specificity and prognostic value ; Amandine Crombe, Maxime Sitbon, berhard Stoeckle, Antoine Italiano, Xavier Buy, François Le Loarer, Michèle Kind ; the British Institute of Radiology; February 27, 2020 (birpublications)
  82. LIFEx-Texture: Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience ; Gao Jing, Huang Xinyun, Meng Hongping, Zhang Miao, Zhang Xiaozhe, Lin Xiaozhu, Li Biao ; Front. Oncol., 25 February 2020 (frontiers)
  83. LIFEx-Texture: Integrated radiomic model for predicting the prognosis of esophageal squamous cell carcinoma patients undergoing neoadjuvant chemoradiation ; Tien-Chi Hou, Wen-Chien Huang, Hung-Chi Tai, Yu-Jen Chen ; Ther Radiol Oncol 2019;3:28 (tro)
  84. LIFEx-Texture: Radiomic Analysis of Craniopharyngioma and Meningioma in the Sellar/Parasellar Area with MR Images Features and Texture Features: A Feasible Study ; Zerong Tian, Chaoyue Chen, Yang Zhang, Yimeng Fan, Ridong Feng and Jianguo Xu ; Contrast Media & Molecular Imaging ; Volume 2020, Article ID 4837156 (doi)
  85. LIFEx-Texture: Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol (2020). Jin, X., Ai, Y., Zhang, J. et al. (doi)
  86. LIFEx-Texture: Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer. Blanc-Durand, P., Campedel, L., Mule, S. et al. Eur Radiol (2020). (doi)
  87. LIFEx-Texture: Radiogenomics predicts the expression of microRNA-1246 in the serum of esophageal cancer patients ; Hoshino, I., Yokota, H., Ishige, F. et al. Sci Rep 10, 2532 (2020) (nature)
  88. LIFEx-Texture: Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer. Lacroix Maxime, Frouin Frederique, Dirand Anne-Sophie, Nioche Christophe, Orlhac Fanny, Bernaudin Jean-François, Brillet Pierre-Yves, Buvat Irène ; Front. Oncol. 10:43. doi:10.3389/fonc.2020.00043 (frontiers)
  89. LIFEx-Texture: Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features ; Kulkarni, A., Carrion-Martinez, I., Jiang, N.N. et al. Eur Radiol (2020) (doi)
  90. LIFEx-Texture: Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas? Amandine Crombé, François Le Loarer, Maxime Sitbon, Antoine Italiano, Eberhard Stoeckle, Xavier Buy, Michèle Kind ; January 2020 ; European Radiology (springer)
  91. LIFEx-Texture: Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors. Ren, C., Wang, S. & Zhang, S  ; Cancer Imaging 20, 5 (2020) doi:10.1186/s40644-019-0284-7 (cancerimagingjournal)
  92. LIFEx-Texture: Projection-space implementation of deep learning-guided low-dose brain PET imaging improves performance over implementation in image-space ; Amirhossein Sanaat, Hossein Arabi, Ismini Mainta, Valentina Garibotto and Habib Zaidi ; Journal of Nuclear Medicine, published on January 10, 2020 (jnm)
  93. LIFEx-Texture: Predictive Role of Temporal Changes in Intratumoral Metabolic Heterogeneity During Palliative Chemotherapy in Patients with Advanced Pancreatic Cancer: A Prospective Cohort Study. Yoo SH1, Kang SY2, Cheon GJ2, Oh DY3,4, Bang YJ1,4. J Nucl Med. 2020 Jan;61(1):33-39. (pubmed)
  94. LIFEx-Texture: Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach ; Zhang Yang, Chen Chaoyue, Cheng Yangfan, Teng Yuen, Guo Wen, Xu Hui, Ou Xuejin, Wang Jian, Li Hui, Ma Xuelei, Xu Jianguo ; Frontiers in Oncology ; 2019, vol9 p1371 (frontiers)
  95. LIFEx-MTV: F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome ; Anne-Ségolène Cottereau, Christophe Nioche, Anne-Sophie Dirand, Jérome Clerc, Franck Morschhauser, Olivier Casasnovas, Michel Meignan and Irène Buvat ; J Nucl Med January 1, 2020 vol. 61 no. 1 40-45 (jnm)

Publications 2019 - Journal papers (50)

  1. LIFEx-texture: Nardone V, Reginelli A, Scala F, Carbone SF, Mazzei MA, Sebaste L, Carfagno T, Battaglia G, Pastina P, Correale P, Tini P, Pellino G, Cappabianca S, Pirtoli L. Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation. Gastroenterol Res Pract. 2019 Jan 17;2019:8505798. https://doi.org/10.1155/2019/8505798. PMID: 30847005; PMCID: PMC6360039.
  2. LIFEx-texture: Tian, Zerong; Chen, Chaoyue; Fan, Yimeng; Ou, Xuejin; Wang, Jian; Ma, Xuelei; Xu, Jianguo. Glioblastoma and Anaplastic Astrocytoma: Differentiation Using MRI Texture Analysis. Front Oncol ; 9: 876, 2019 (doi)
  3. LIFEx-texture: A downsampling strategy to assess the predictive value of radiomic features. Dirand, AS., Frouin, F. & Buvat, I. ; Sci Rep 9, 17869 (2019) (doi)
  4. LIFEx-Texture: An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases. Machiko Tateishi, Takeshi Nakaura, Mika Kitajima, Hiroyuki Uetani, Masataka Nakagawa, Taihei Inoue, Jun-ichiro Kuroda, Akitake Mukasa, Yasuyuki Yamashita ; Journal of the Neurological Sciences ; Volume 410, 15 March 2020 (doi)
  5. LIFEx-Texture: Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation ; Mykola Novikov ; Eur Radiol Exp 3, 48 (2019) (doi)
  6. LIFEx-Texture: Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab ; V Nardone, P Tini, P Pastina, C Botta, A Reginelli, Oncology Letters ; Dec 2019 (spandidos)
  7. LIFEx-Texture: Differential diagnosis of pancreatic serous cystadenoma and mucinous cystadenoma: utility of textural features in combination with morphological characteristics ; J Yang, X Guo, H Zhang, W Zhang, J Song, H Xu, X Ma - BMC Cancer, 2019 (bmccancer)
  8. LIFEx-Texture-MTV: Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis ;Jinyeong Choi, Jeong-An Gim, Chiwoo Oh, Seunggyun Ha, Howard Lee, Hongyoon Choi & Hyung-Jun Im ; EJNMMI Research volume 9, Article number: 97 (2019) (ejnmmi)
  9. LIFEx-Texture: The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study ; Chaoyue Chen, Xinyi Guo, Jian Wang, Wen Guo, Xuelei Ma and Jianguo Xu ; December 2019 ; Frontiers in Oncology (frontiers)
  10. LIFEx-Texture: Radiomics based on 18F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study ; Xuejin Ou, Jing Zhang, Jian Wang, Fuwen Pang, Yongsheng Wang, Xiawei Wei, Xuelei Ma ; Cancer Medicine. 2019;00:1–11 (onlinelibrary)
  11. LIFEx-Texure: Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: A multivariable radiomics model ; Heesoon Sheen, Wook Kim, Byung Hyun Byun, Chang-Bae Kong, Won Seok Song, Wan Hyeong Cho, Ilhan Lim, Sang Moo Lim, Sang-Keun WooID1 ; PLoS ONE 14(11): e0225242 (doi)
  12. LIFEx-Texture: Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool ; Marie Manon Krebs Krarup, Lotte Nygard, Ivan Richter Vogelius, Flemming Littrup Andersen, Gary Cook, Vicky Goh, Barbara Malene Fischer ; Volume 144, March 2020, Pages 72-78 (doi)
  13. LIFEx-Texture: Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer ; Jianyuan Zhang, Xinming Zhao, Yan Zhao, Jingmian Zhang, Zhaoqi Zhang, Jianfang Wang, Yingchen Wang, Meng Dai, Jingya Han ; European Journal of Nuclear Medicine and Molecular Imaging ; November 2019 ; pp 1–10 (springer)
  14. LIFEx-Texture: A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer ; Davide Franceschini, Luca Cozzi, Fiorenza De RosePierina Navarria, Antonella Fogliata, Ciro Franzese, Donato Pezzulla, Stefano TomatisGiacomo Reggiori, Marta Scorsetti ;Strahlentherapie und Onkologie ; November 2019 ; (springer)
  15. LIFEx-Texture: Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy ; Chaoyue Chen, Hongyu Zhuo, Xiawei Wei, Xuelei Ma ; Contrast Media & Molecular Imaging 2019(2):1-7 ; November 2019 (hindawi)
  16. LIFEx-Texture: Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status ; Burak Kocak, Emine Sebnem Durmaz, Ece Ates, Ipek Sel, Saime Turgut Gunes, Ozlem Korkmaz Kaya, Amalya Zeynalova, Ozgur Kilickesmez ; November 2019 ; European Radiology (springer)
  17. LIFEx-Texture: Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma ; Yimeng Fan Chaoyue Chen, Fumin Zhao, Zerong Tian3, Jian Wang, Xuelei Ma and Jianguo Xu ; November 2019 Frontiers in Oncology 9:1164 (frontiers)
  18. LIFEx-Texture: 11C-methionine-PET for diferentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifer ; Masatoshi Hotta, Ryogo Minamimoto & Kenta Miwa ; December 2019; Scientific Reports 9(1) (doi)
  19. LIFEx-Texture: The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: A Preliminary Study ; Yang Zhang, Chaoyue Chen, Zerong Tian, Ridong Feng, Yangfan Cheng, Jianguo Xu ; October 2019 Frontiers in Neuroscience 13:1113 (frontiers)
  20. LIFEx-Texture: Radiomics in stratification of pancreatic cystic lesions: Machine learning in action ; Vipin Dalal, Joseph Carmicheal, Amaninder Dhaliwal, Maneesh Jain, Sukhwinder Kaur, Surinder K.Batra ; Cancer Letters ; October 2019 (doi)
  21. LIFEx-Texture: Machine Learning-based MRI Texture Analysis Enables Differentiation between Glioblastoma and Anaplastic Oligodendroglioma ; Yimeng Fan, Xuelei Ma, Chaoyue Chen, Zerong Tian, Jian Wang and Jianguo Xu ; Front. Oncol. 2019.01164 (doi)
  22. LIFEx-Texture: A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma ; 
    Kaixuan Yanga, Jiangfang Tiana, Bin Zhang, Mei Lia, Wenji Xie, Yating Zou, Qiaoyue Tan, Lihui Liu, Jinbing Zhu, Arthur Shou, Guangjun Li ; Oral Oncology ; Volume 98, November 2019, Pages 85-91 ; (doi)
  23. LIFEx-Texture: Shape and Texture Analysis of Radiomic Data for Computer-Assisted Diagnosis and Prognostication: An Overview ; Francesco Bianconi, Mario Luca Fravolini, Isabella Palumbo, Barbara Palumbo ; Proceedings of the International Conference on Design Tools and Methods in Industrial Engineering, ADM 2019, September 9-10, 2019, Modena, Italy pp 3-14 (springer)
  24. LIFEx-Texture: MRI derived radiomics: Methodology and clinical applications in the field of pelvic oncology ; Ulrike Schick, François Lucia, Gurvan Dissaux, Dimitris Visvikis, Bogdan Badic, Ingrid Masson, Olivier Pradier, Vincent Bourbonne and Mathieu Hatt ; the British Institute of Radiology ; 2019, 12 september (doi)
  25. LIFEx-Texture: Radiomics with artificial intelligence: a practical guide for beginners ; Burak Koçak, Emine Sebnem Durmaz, Ece Ates, Özgür Kiliçkesmez ;  Diagn Interv Radiol ; 4 september 2019 (doi)
  26. LIFEx-texture: Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT ; PJ Brown, J Zhong, R Frood, S Currie, A Gilbert, AL Appelt, D Sebag-Montefiore, A Scarsbrook ;  04 September 2019 ; EJNMMI pp 1-10 (doi)
  27. LIFEx-Texture: Conventional MRI radiomics in patients with suspected early- or pseudo-progression ; Alexandre Bani-Sadr, Omer Faruk Eker, Lise-Prune Berner, Roxana Ameli, Marc Hermier, Marc Barritault, David Meyronet, Jacques Guyotat, Emmanuel Jouanneau, Jerome Honnorat, François Ducray, Yves Berthezene ; Neuro-Oncology Advances ; 01 September 2019 (doi)
  28. LIFEx-Texture: CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma ; Mungai F, Verrone GB, Pietragalla M, Berti V, Addeo G, Desideri I, Bonasera L, Miele V. Radiol Med. 2019 Mar 25. (pubmed)
  29. LIFEx-Texture: Glioblastoma Multiforme and Anaplastic Astrocytoma: Differentiation using MRI Texture Analysis ; J Xu, X Ma, Z Tian, C Chen, Y Fan, X Ou, J Wang - Frontiers in Oncology, 2019 ; (doi)
  30. LIFEx-Texture: Contrast-Enhanced CT Texture Analysis: a New Set of Predictive Factors for Small Cell Lung Cancer ; Chaoyue Chen, Xuejin Ou, Hui Li, Yanjie Zhao, Fengnian Zhao, Shengliang Zhou, Xuelei Ma ; Molecular Imaging and Biology ; August 2019 ; pp 1-7 (springer)
  31. LIFEx-MTV: Time to prepare for risk adaptation in lymphoma by standardising measurement of metabolic tumour burden. Sally F Barrington, Michel Meignan ; Apr 2019 ; Journal of Nuclear Medicine ; (jnm)
  32. LIFEx-Texture: Prognostic Value of Functional Parameters of 18F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma ; M Wang, H Xu, L Xiao, W Song, S Zhu, X Ma ; Contrast Media & Molecular Imaging, Volume 2019, Article ID 2641627 (doi, cm&mi)
  33. LIFEx-Texture: Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study. Emine Acar, Asim Leblebici, Berat Ender Ellidokuz, Yasemin Basbinar and Gamze Çapa Kaya. British Institute of Radiology. Published Online: July 10, 2019 (doi)
  34. LIFEx-Texture: AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Isabella Castiglioni, Francesca GallivanonePaolo Soda, Michele AvanzoJoseph StancanelloMarco AielloMatteo InterlenghiMarco Salvatore. European Journal of Nuclear Medicine and Molecular Imaging. First Online: 11 July 2019 ; (springer)
  35. LIFEx-Texture: CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach ; N Taguchi, S Oda, Y Yokota, S Yamamura, M Imuta ;European Journal of Radiology ; Volume 118, September 2019, Pages 38-43 (sciencedirect)
  36. LIFEx-Texture: Radiomics in nuclear medicine: robustness, reproducibility, standardization, and howto avoid data analysis traps and replication crisis ; Alex Zwanenburg ; European Journal of Nuclear Medicine and Molecular Imaging ; 25 June 2019 (doi)
  37. LIFEx-Texture: Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics ; Luca Cozzi, Ciro Franzese, Antonella Fogliata, Davide Franceschini, Pierina Navarria, Stefano Tomatis, Marta Scorsetti ; Strahlentherapie und Onkologie, pp 1-14 (doi)
  38. LIFEx-Texture: Discrimination of pancreatic serous cystadenomas from mucinous cystadenomas with CT textural features: based on machine learning ; Jing Yang, Xinli Guo, Xuejin Ou, Weiwei Zhang, Xuelei Ma ; Front. Oncol., 12 June 2019 (doi, link)
  39. LIFEx-Texture: The Future of Medical Imaging ; Luigi Landini ; Current Pharmaceutical Design, 2018, Vol. 24, No. 46 (eurekaselect)
  40. LIFEx-MTV: Time to prepare for risk adaptation in lymphoma by standardising measurement of metabolic tumour burden ; Sally F Barrington and Michel Meignan ; J Nucl Med April 6, 2019 jnumed.119.227249 (abstract)
  41. LIFEx-Texture: Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET/CT in head and neck cancer ; Catherine Guezennec, David Bourhis, Fanny Orlhac, Philippe Robin, Jean-Baptiste Corre, Olivier Delcroix, Yves Gobel, Ulrike Schick, Pierre-Yves Salaun, Ronan Abgral ; PLOSone March 28, 2019 ; (doi, plosone)
  42. LIFEx-Texture: PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy ; Lidija Antunovic, Rita De Sanctis, Luca Cozzi, Margarita Kirienko, Andrea Sagona, Rosalba Torrisi, Corrado Tinterri, Armando Santoro, Arturo Chiti, Renata Zelic, Martina Sollini ; 26 March 2019 ; European Journal of Nuclear Medicine and Molecular Imaging ; (doi)
  43. LIFEx-Texture: Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers ; Paul Giraud, Philippe Giraud, Anne Gasnier, Radouane El Ayachy, Sarah Kreps, Jean-Philippe Foy, Catherine Durdux, Florence Huguet, Anita Burgun and Jean-Emmanuel Bibault ; Front. Oncol., 27 March 2019 ; (doi)
  44. LIFEx-Texture: Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types ; Francesco Bianconi, Isabella Palumbo, Mario Luca Fravolini, Rita Chiari, Matteo Minestrini, Luca Brunese, Barbara Palumbo ; March 2019 ; Molecular Imaging & Biology ; (doi)
  45. LIFEx-Texture: Tumor heterogeneity in oral and oropharyngeal squamous cell carcinoma assessed by texture analysis of CT and conventional MRI: a potential marker of overall survival ; Jiliang Ren, Ying Yuan, Yiqian Shi, Xiaofeng Tao ;Acta Radiologica ; First Published February 28, 2019 (doi)
  46. LIFEx-Texture: Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma - Xuejin Ou, Jian Wang, Ruofan Zhou, Sha Zhu, Fuwen Pang, Yi Zhou, Rong Tian and Xuelei Ma ; Contrast Media & Molecular Imaging ; Volume 2019, Article ID 4507694, Published 25 February 2019, 9 pages (doi)
  47. LIFEx-Texture: Postmortem Changes in Skeletal Muscle Can Be Expressed by Hounsfield Unit Measurements in Postmortem Computed Tomography—A Murine Model Study ;  Yamada, Tsuyoshi; Takeuchi, Tamaki; Ito, Morihiro ;  Journal of Medical Imaging and Health Informatics, Volume 9, Number 2 February 2019, pp. 261-266(6) (doi)
  48. LIFEx-Texture: Validation of a method to compensate multicenter effects affecting CT radiomics. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Radiology 2019 (doi) (hal)
  49. LIFEx-Texture: Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. Cozzi L, Comito T, Fogliata A, Franzese C, Franceschini D, Bonifacio C, Tozzi A, Di Brina L, Clerici E, Tomatis S, Reggiori G, Lobefalo F, Stravato A, Mancosu P, Zerbi A, Sollini M, Kirienko M, Chiti A, Scorsetti M. PlosOne Jan 2019 (plosone) (doi)
  50. LIFEx-Texture: Radiomics in Oncological PET/CT: a Methodological Overview. Seunggyun Ha, Hongyoon Choi, Jin Chul Paeng, Gi Jeong Cheon. Nuclear Medicine and Molecular Imaging Jan 2019 (springer)

Publications 2018 - Journal papers (26)

  1. LIFEx-Texture: Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics. Aide N, Salomon T, Blanc-Fournier C, Grellard JM, Levy C, Lasnon C. EJNMMI Research, Dec 2018 (springer)
  2. LIFEx-Texture: Prognostic value of textural indices extracted from pretherapeutic 18-F FDG-PET/CT in head and neck squamous cell carcinoma. Guezennec C, Robin P, Orlhac F, Bourhis D, Delcroix O, Gobel Y, Rousset J, Schick U, Salaün PY, Abgral R. Head & Neck, Dec 2018 (doi)
  3. LIFEx-Texture: The value of MR textural analysis in prostate cancer. Patel N, Henry A, Scarsbrook A. Clinical Radiology ; Available online 17 December 2018
    (sciencedirect)(doi)
  4. LIFEx-Texture: Effects of CT FOV displacement and acquisition parameters variation on texture analysis features. Biondi M, Vanzi E, De Otto G, Carbone SF, Nardone V, Banci Buonamici F. Physics in Medicine and Biology, 2018 Nov, 1361-6560 (link)
  5. LIFEx-Texture: Machine-learning integration of CT histogram analysis to evaluate the composition of atherosclerotic plaques: Validation with IB-IVUS. Masudaae T, Nakaura T, Funamad Y, Okimoto T, Satob T, Higakie T, Noda N, Imadaa N, Babae Y, Awai K ; Journal of Cardiovascular Computed Tomography ; Oct 2018 (link)
  6. LIFEx-Texture: Meignan M and Cottereau AS. FDG-PET in PMBCL: which heterogeneity? Blood 2018 132:117-118 (link)
  7. LIFEx-MTV: Chantepie S, Hovhannisyan N, Guillouet S, Pelage JP, Ibazizene M, Bodet-Milin C, Carlier T, Gac AC, Réboursière E, Vilque JP, Kraeber-Bodéré F, Manrique A, Damaj G, Leporrier M, Barré L. 18F-Fludarabine PET for Lymphoma Imaging: First-in-Humans Study on DLBCL and CLL Patients. J Nucl Med. 2018 Sep;59(9):1380-1385 (link)
  8. LIFEx-Texture: Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, Oda S, Utsunomiya D, Makino K, Nakamura H, Mukasa A, Hirai T, Yamashita Y. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. European Journal of Radiology. 2018 Sep (link)
  9. LIFEx-Texture: Vendrami CL, Velichko YS, Miller FH, Chatterjee A, Villavicencio CP, Yaghmai V, McCarthy RJ. Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis. AJR Am J Roentgenol. 2018 Sep 21:1-12 (link)
  10. LIFEx-Texture: Lohmann P, Lerche C, Bauer EK, Steger J, Stoffels G, Blau T, Dunkl V, Kocher M, Viswanathan S, Filss CP, Stegmayr C, Ruge MI, Neumaier B, Shah NJ, Fink GR, Langen KJ & Galldiks N. Predicting IDH genotype in gliomas using FET PET radiomics. Scientific Reports 8, Article number: 13328 (2018) (link)
  11. LIFEx-Texture: Liu C, Ding J, Spuhler K, Gao Y, Serrano Sosa M, Moriary M, Hussain S, He X, Liang C, Huang C. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging, 2018 Sep (link)
  12. LIFEx-Texture: R Sun, EJ Limkin, M Vakalopoulou, L Dercle, S Champiat, S Rong Han, L Verlingue, D Brandao, A Lancia, S Ammari, A Hollebecque, JY Scoazec, A Marabelle, C Massard, JC Soria, C Robert, N Paragios, E Deutsch, C Ferté. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study; The Lancet Oncology ; Published:August 14, 2018 (link)
  13. LIFEx-Texture: P Lohmann, M Kocher, G Ceccon, EK Bauer, G Stoffels, S Viswanathan, MI Ruge, B Neumaier, NJ Shah, GR Fink, KJ Langen, N Galldiks. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clinical. 2018, 20:537-542 (link).
  14. LIFEx-Texture: C Nioche, F Orlhac, S Boughdad, S Reuzé, J Goya-Outi, C Robert, C Pellot-Barakat, M Soussan, F Frouin, and I Buvat. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Research 2018; 78(16):4786-4789 (link).
  15. LIFEx-Texture: S Boughdad, C Nioche, F Orlhac, L Jehl, L Champion, I Buvat. Influence of age on radiomic features in 18F-FDG PET in normal breast tissue and in breast cancer tumors. Oncotarget 2018; 9:30855-30868 (link).
  16. LIFEx-Texture: A Parvez, N Tau, D Hussey, M Maganti, U Metser. 18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin’s lymphoma as predictors of treatment outcome and survival. Ann Nucl Med (2018). https://doi.org/10.1007/s12149-018-1260-1 (link)
  17. LIFEx-Texture: T Tsujikawa, H Tsuyoshi, M Kanno, S Yamada, M Kobayashi, N Narita, H Kimura, S Fujieda, Y Yoshida and H Okazawa. Selected PET radiomic features remain the same. Oncotarget. 2018; 9:20734-20746. https://doi.org/10.18632/oncotarget.25070. (link)
  18. LIFEx-MTV: P Blanc-Durand, A Van Der Gucht, N Schaefer, E Itti, J O. Prior. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study. Plos One April 13, 2018 (link)
  19. LIFEx-Texture: M Kirienko M, L Cozzi, A Rossi, E Voulaz, L Antunovic, A Fogliata, A Chiti, M Sollini. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018 Apr 6. doi: 10.1007/s00259-018-3987-2. (link)
  20. LIFEx-Texture: V Nardone, P Tini, S Croci, SF Carbone, L Sebaste, T Carfagno, G Battaglia, P Pastina, G Rubino, MA Mazzei, L Pirtoli. 3D bone texture analysis as a potential predictor of radiationinduced insufficiency fractures. Quant Imaging Med Surg 2018;8(1):14-24 (link)
  21. LIFEx-Texture: C Caramella, A Allorant, F Orlhac, F Bidault, B Asselain, S Ammari, P Jaranowski, A Moussier, C Balleyguier, N Lassau, S Pitre-Champagnat. Can we trust the calculation of texture indices of CT images? A phantom study. Med Phys. 2018 Feb 14. doi: 10.1002/mp.12809 (link)
  22. LIFEx-Texture: V Nardone, P Tini, C Nioche, MA Mazzei, T Carfagno, G Battaglia, P Pastina, R Grassi, L Sebaste, L Pirtoli. Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med. 2018 Jan 24. doi: 10.1007/s11547-017-0850-7 (link)
  23. LIFEx-Texture: F Orlhac, S Boughdad, C Philippe, H Stalla-Bourdillon, C Nioche, L Champion, M Soussan, F Frouin, V Frouin, I Buvat. A post-reconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018  doi: 10.2967/jnumed.117.199935. [Epub ahead of print] (link)
  24. LIFEx-Texture: M Kirienko, L Cozzi, L Antunovic, L Lozza, A Fogliata, E Voulaz, A Rossi, A Chiti, M Sollini ; Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207-217, 2018 (link)
  25. LIFEx-Texture: N Aide, M Talbot, C Fruchart, G Damaj, C Lasnon ; Diagnostic and prognostic value of baseline FDG PET/CT skeletal textural features in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging. 2018;45(5):699-711 (link)
  26. LIFEx-Texture: A Schernberg, S Reuze, F Orlhac, I Buvat, L Dercle, R Sun, E Limkin, A Escande, C Haie-Meder, E Deutsch, C Chargari, C Robert ; A score combining baseline neutrophilia and primary tumor SUVpeak measured from FDG PET is associated with outcome in locally advanced cervical cancer ; Eur J Nucl Med Mol Imaging. 2018;45(2):187-195. doi: 10.1007/s00259-017-3824-z (link)

Publications 2017 - Journal papers (5)

  1. LIFEx-Texture: L Cozzi, N Dinapoli, A Fogliata, WC Hsu, G Reggiori, F Lobefalo, M Kirienko, M Sollini, D Franceschini, T Comito, C Franzese, Ma Scorsetti and PM Wang ; Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer 2017 17:829 (link)
  2. LIFEx-Texture: F Orlhac, C Nioche, M Soussan, I Buvat ; Understanding changes in tumor textural indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med 2017; 58:387–392: (link)
  3. LIFEx-Texture: S Reuzé, F Orlhac, C Chargari, C Nioche, E Limkin, F Riet, A Escande, C Haie-Meder, L Dercle, S Gouy, I Buvat, E Deutsch, C Robert ; Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget. 2017; 8(26):43169-43179 (link)
  4. LIFEx-Texture: M Sollini, L Cozzi, G Pepe, L Antunovic, A Lania, L Di Tommaso, P Magnoni, PA Erba,M Kirienko ; [18F]FDG-PET/CT texture analysis in thyroid incidentalomas: preliminary results. European Journal of Hybrid Imaging, December 2017, 1:3 (link)
  5. LIFEx-Texture: V Nardone, P Tini, C Nioche, M Biondi, L Sebaste, MA Mazzei, F Banci Buonamici, L Pirtoli ; Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: A subset analysis. Radiother Oncol. 2017 Feb;122(2):321. doi: 10.1016/j.radonc.2016.09.004 (link)

Publications 2016 - Journal papers (2)

  1. LIFEx-Texture: F Orlhac, B Thézé, M Soussan, R Boisgard, I Buvat ; Multiscale texture analysis: from 18F-FDG PET images to pathological slides. J Nucl Med 57: 1823-1828, 2016 (link)
  2. LIFEx-Texture: O Diop, EAL Bathily, B Ndong, G Mbaye, RS Senghor, W Sow-Diop, M Soumboundou, LAD Diouf, AR Djiboune, PM Sy, M Diarra, O Ndoye, M Mbodj, S Seck-Gassama ; Etude de la robustesse des statistiques de premier ordre dans la discrimination des ganglions malins et benins dans le cancer du col de l'utérus. Journal des Sciences, I.S.S.N 0851 – 4631 (link)

Publications 2015 - Journal papers (2)

  1. LIFEx-Texture: F Orlhac, M Soussan, K Chouahnia, E Martinod, I Buvat ; 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non small cell lung cancer. Plos One 10(12):e0145063, 2015 (link)
  2. LIFEx-Texture: I Buvat, F Orlhac, M Soussan ; Tumor texture analysis in PET: where do we stand? J Nucl Med 56: 1642-1644, 2015 (link)

Publications 2014 - Journal papers (2)

  1. LIFEx-Texture: M Soussan, F Orlhac, M Boubaya, L Zelek, M Ziol, V Eder, I Buvat ; Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. Plos One 9: e94017, 2014 (link)
  2. LIFEx-Texture: F Orlhac, M Soussan, JA Maisonobe, CA Garcia, B Vanderlinden, I Buvat ; Tumor texture analysis in 18F-FDG-PET: relationships between texture parameters, histogram indices, SUVs, metabolic volumes and total lesion glycolysis. J Nucl Med 55: 414-422, 2014 (link)