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Publications 2025 - Journal papers (88)

(68)

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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.
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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 
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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.
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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 (5):

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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 (15):

  1. 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
  2. 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
  3. 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
  4. 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
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