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Publications 2024 - Journal papers

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7 hours 56 minutes ago #16 by christophe.nioche
Publications 2024 - Journal papers was created by christophe.nioche
Articles (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. 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).  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.  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). 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).  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. 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,  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).  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,  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,  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.  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, 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.  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.  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).  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).  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).  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). 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.  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).  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.  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.  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.  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.  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. 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).  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.  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).  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.  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;  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  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
    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.  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.  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.  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.  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.  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.  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.  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.  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.  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).  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).  doi.org/10.1186/s40658-024-00689-1
  44. LIFEx-MTV: Hong, Sp., Lee, S.M., Yoo, I.D. et al. Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer. Cancer Imaging 24, 136 (2024). doi.org/10.1186/s40644-024-00787-4
  45. LIFEx-MTV: Seban, RD., Champion, L., De Moura, A. et al. Pre-treatment [18F]FDG PET/CT biomarkers for the prediction of antibody-drug conjugates efficacy in metastatic breast cancer. Eur J Nucl Med Mol Imaging (2024). doi.org/10.1007/s00259-024-06929-x
  46. LIFEx-MTV: F. Kleiburg, L.F. de Geus-Oei, S.A.C. Luelmo, R. Spijkerman, J.J. Goeman, F.A.J. Toonen, F. Smit, T. van der Hulle, L. Heijmen, PSMA PET/CT for treatment response evaluation at predefined time points is superior to PSA response for predicting survival in metastatic castration-resistant prostate cancer patients, European Journal of Radiology (2024), doi: doi.org/10.1016/j.ejrad.2024.111774
  47. LIFEx-texture: Ogün BülBül, Demet Nak, Sibel Göksel. Prediction of lesion-based treatment response after two cycles of Lu-177 PSMA treatment in metastatic castration-resistant prostate cancer using machine learning. Urol Int 1–12.  doi.org/10.1159/000541628
  48. LIFEx-texture: Li, Jiatong; Cui, Nan; Wang, Yanmei; Li, Wei; Jiang, Zhiyun; Liu, Wei; Guo, Chenxu; Wang, Kezheng. Prediction of preoperative lymph-vascular space invasion and survival outcomes of cervical squamous cell carcinoma by utilizing 18F-FDG PET/CT imaging at early stage. Nuclear Medicine Communications ():10.1097/MNM.0000000000001909, October 02, 2024.  doi.org/10.1097/MNM.0000000000001909
  49. LIFEx-texture: Yang, F., Wang, C., Shen, J. et al. End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer. Abdom Radiol (2024). doi.org/10.1007/s00261-024-04601-4
  50. LIFEx-texture: Zhang, Y., Huang, W., Jiao, H. et al. PET radiomics in lung cancer: advances and translational challenges. EJNMMI Phys 11, 81 (2024).  doi.org/10.1186/s40658-024-00685-5
  51. LIFEx-texture: Pizzuto, D.A., Guerreri, M., Zamboglou, C. et al. The clinical predictive value of radiomic features from [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET in patients with prostate cancer: a preliminary comparative study. Clin Transl Imaging (2024).  doi.org/10.1007/s40336-024-00659-8
  52. LIFEx-texture: Toshinari Horie, Motohiro Fujiwara, Yuma Waseda, Hajime Tanaka, Soichiro Yoshida and Yasuhisa Fujii. Radiomics analysis using non-contrast computed tomography for predicting high-dependency unit admission in patients with acute pyelonephritis. International Journal of Urology 2024.  doi.org/10.1111/iju.15591
  53. LIFEx-texture: Khangembam B C, Jaleel J, Roy A, et al. (September 16, 2024) A Novel Approach to Identifying Hibernating Myocardium Using Radiomics-Based Machine Learning. Cureus 16(9): e69532.  doi.org/10.7759/cureus.69532
  54. LIFEx-texture: Wang, N., Dai, M., Jing, F., Liu, Y., Zhao, Y., Zhang, Z., … Zhao, X. (2024). Value of 18F-FDG PET/CT-based radiomics features for differentiating primary lung cancer and solitary lung metastasis in patients with colorectal adenocarcinoma. International Journal of Radiation Biology, 1–9.  doi.org/10.1080/09553002.2024.2404465
  55. LIFEx-texture: Li, C., Lu, X., Zhang, F. et al. Neuroblastoma with high ASPM reveals pronounced heterogeneity and poor prognosis. BMC Cancer 24, 1151 (2024). doi.org/10.1186/s12885-024-12912-4
  56. LIFEx-MTV: Ali Abdulhasan Kadhim, Peyman Sheikhzadeh, Mehrshad Abbasi, Saeed Afshar, Nasim Vahidfar, Shirin Asidkar, Mehrnoosh Karimipourfard, Zahra Valibeiglou, Mohammad Reza. A Investigating Patient-Specific Absorbed Dose Assessment for Copper-64 PET/CT. Frontiers in Biomedical Technologies. Vol. 12, No. 4.  fbt.tums.ac.ir/index.php/fbt/article/download/1058/436
  57. LIFEx-texture: Nakajo, M., Hirahara, D., Jinguji, M. et al. Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases. Jpn J Radiol (2024). doi.org/10.1007/s11604-024-01649-6
  58. LIFEx-texture: Lee, J.W.; Won, Y.K.; Ahn, H.; Lee, J.E.; Han, S.W.; Kim, S.Y.; Jo, I.Y.; Lee, S.M. Peritumoral Adipose Tissue Features Derived from [18F]fluoro-2-deoxy-2-D-glucose Positron Emission Tomography/ Computed Tomography as Predictors for Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. J. Pers. Med. 2024, 14, 952.  doi.org/10.3390/jpm14090952
  59. LIFEx-texture: Fenglian Jing, Xinchao Zhang, Yunuan Liu, & al. Baseline 18F-FDG PET Radiomics Predicting Therapeutic Efficacy of Diffuse Large B-Cell Lymphoma after R-CHOP (-Like) Therapy. Cancer Biotherapy and Radiopharmaceuticals. 4 September 2024. doi.org/10.1089/cbr.2024.0115
  60. LIFEx-texture: Chen, Yu-Hung; Lue, Kun-Han; Chu, Sung-Chao; Lin, Chih-Bin; Liu, Shu-Hsin. The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer. Tzu Chi Medical Journal ():10.4103/tcmj.tcmj_124_24, September 03, 2024. |  doi.org/10.4103/tcmj.tcmj_124_24
  61. LIFEx-texture: Fereshteh Yousefirizi, Annudesh Liyanage, Ivan S. Klyuzhin, Arman Rahmim. From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing. Journal of Medical Imaging and Radiation Sciences, Volume 55, Issue 4, 2024, 101745, ISSN 1939-8654,  doi.org/10.1016/j.jmir.2024.101745
  62. LIFEx-texture: C. Masson-Grehaigne, M. Lafon, J. Palussiere et al., Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma, Diagnostic and Interventional  Imaging (2024), doi.org/10.1016/j.diii.2024.07.005 Diagnostic and Interventional Imaging 000 (2024) 1−14
  63. LIFEx-texture: Toniolo, A.; Agostini, E.; Ceccato, F.; Tizianel, I.; Cabrelle, G.; Lupi, A.; Pepe, A.; Campi, C.; Quaia, E.; Crimì, F. Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation? Curr. Oncol. 2024, 31, 4917–4926.  doi.org/10.3390/curroncol31090364
  64. LIFEx-texture: Seyed Ali Mirshahvalad, Adriano B. Dias, Sangeet Ghai, Claudia Ortega, Nathan Perlis, Alejandro Berlin, Lisa Avery, Theodorus van der Kwast, Ur Metser, Patrick Veit-Haibach. Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study, Academic Radiology, 2024, ISSN 1076-6332.  doi.org/10.1016/j.acra.2024.08.004
  65. LIFEx-texture: Yusuke Kawashima, Aya Hagimoto, Hiroshi Abe, Masaaki Miyakoshi, Yoshihiro Kawabata, Hiroko Indo, Tatsuro Tanaka. Using texture analysis of ultrasonography images of neck lymph nodes to differentiate metastasis to non-metastasis in oral maxillary gingival squamous cell carcinoma. Journal of Oral and Maxillofacial Surgery, Medicine, and Pathology, 2024. ISSN 2212-5558. doi.org/10.1016/j.ajoms.2024.07.013
  66. LIFEx-texture: Kote, Rutuja; Ravina, Mudalsha; Goyal, Harish; Mohanty, Debajyoti; Gupta, Rakesh; Shukla, Arvind Kumar; Reddy, Moulish; Prasanth, Pratheek N. Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients. Nuclear Medicine Communications.  doi.org/10.1097/MNM.0000000000001885 , August 08, 2024
  67. LIFEx-texture: Seda Gülbahar Ateş, Bedriye Büşra Demirel, Esra Kekilli, Erdem Öztürk, Gülin 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 (English Edition), 2024, 500032, ISSN 2253-8089,  doi.org/10.1016/j.remnie.2024.500032
  68. LIFEx-texture: Jing, F., Zhang, X., Liu, Y. et al. Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement. Clin Transl Oncol (2024).  doi.org/10.1007/s12094-024-03633-y
  69. LIFEx-texture: Zhi, H., Xiang, Y., Chen, C. et al. Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival. Cancer Imaging 24, 99 (2024).  doi.org/10.1186/s40644-024-00741-4
  70. LIFEx-MTV: Ronald Boellaard, Irène Buvat, Christophe Nioche, Luca Ceriani, Anne-Ségolène Cottereau, Luca Guerra, Rodney J. Hicks, Salim Kanoun, Carsten Kobe, Annika Loft, Heiko Schöder, Annibale Versari, Conrad-Amadeus Voltin, Gerben J.C. Zwezerijnen, Josée M. Zijlstra, N. George Mikhaeel, Andrea Gallamini, Tarec C. El-Galaly, Christine Hanoun, Stephane Chauvie, Romain Ricci, Emanuele Zucca, Michel Meignan, Sally F. Barrington. International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation. Journal of Nuclear Medicine Aug 2024, jnumed.124.267789; doi.org/ 10.2967/jnumed.124.267789
  71. LIFEx-MTV: Madeleine J Karpinski, Johannes Hüsing, Kevin Claassen & al. Combining PSMA-PET and PROMISE to re-define disease stage and risk in patients with prostate cancer: a multicentre retrospective study. The lancet Oncology :July 29, 2024 doi.org/10.1016/S1470-2045(24)00326-7
  72. LIFEx-texture: Pellegrino, S., Origlia, D., Di Donna, E. et al. Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma. Ann Hematol (2024).  doi.org/10.1007/s00277-024-05905-7
  73. LIFEx-MTV: Cui, S., Xin, W., Wang, F. et al. Metabolic tumour area: a novel prognostic indicator based on 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma in the R-CHOP era. BMC Cancer 24, 895 (2024).  doi.org/10.1186/s12885-024-12668-x
  74. LIFEx-MTV: Lukas Muller, Daniel Bender, Simon J. Gairing & al. Amount of ascites impacts survival in patients with hepatocellular carcinoma undergoing transarterial chemoembolization advocating for volumetric assessment. Scientific Reports | (2024) 14:16550,  doi.org/10.1038/s41598-024-67312-2
  75. LIFEx-texture: N. Agüloğlu, A. Aksu, D.S. Unat, Ö. Selim Unat. The value of PET/CT radiomic texture analysis of primary mass and mediastinal lymph node on survival in patients with non-small cell lung cancer. Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), 2024, 500027, ISSN 2253-8089,  doi.org/10.1016/j.remnie.2024.500027
  76. LIFEx-texture: Lafon, M., Cousin, S., Alamé, M. et al. Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity. J Digit Imaging. Inform. med. (2024).  doi.org/10.1007/s10278-024-01163-1
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  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. 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). 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 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. 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: 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. 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. 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. 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. 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). 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. 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. 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. 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. 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. 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. kups.ub.uni-koeln.de/74094/1/DissertationsschriftJanSteger.pdf
  2. LIFEx-texture: Louis Rebaud. Whole-body / total-body biomarkers in PET imaging.  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. doi.org/10.18725/OPARU-53062

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