{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:30:55Z","timestamp":1772166655983,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"DOI":"10.1186\/s40644-025-00951-4","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T13:33:54Z","timestamp":1762781634000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Association between induced organ atrophy assessed by artificial intelligence-generated automatic segmentation and efficacy of bevacizumab in combination with chemotherapy in metastatic colorectal cancer"],"prefix":"10.1186","volume":"25","author":[{"given":"Malik","family":"Laich","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathias","family":"Brugel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre Henri","family":"Conze","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marwan","family":"Abbas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Ben","family":"Abdelghanie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faiza","family":"Khemissa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvie","family":"Kircher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karine","family":"Le Malicot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00f4me","family":"Lepage","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Aparicio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Hoeffel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Bouch\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claire","family":"Carlier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"issue":"3","key":"951_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin Mai. 2021;71(3):209\u201349.","journal-title":"CA Cancer J Clin Mai"},{"key":"951_CR2","doi-asserted-by":"crossref","unstructured":"Chiorean EG, Nandakumar G, Fadelu T, Temin S, Alarcon-Rozas AE, Bejarano S et al. Treatment of patients with late-stage colorectal cancer: ASCO resource-stratified guideline. JCO Glob Oncol. 9 mars 2020;6:JGO.19.00367.","DOI":"10.1200\/JGO.19.00367"},{"issue":"1469","key":"951_CR3","first-page":"51","volume":"131","author":"A Gunawardene","year":"2018","unstructured":"Gunawardene A, Desmond B, Shekouh A, Larsen P, Dennett E. Disease recurrence following surgery for colorectal cancer: five-year follow-up. N Z Med J 2 f\u00e9vr. 2018;131(1469):51\u20138.","journal-title":"N Z Med J 2 f\u00e9vr"},{"issue":"1","key":"951_CR4","first-page":"e53","volume":"20","author":"S Malakorn","year":"2021","unstructured":"Malakorn S, Ouchi A, Hu CY, Sandhu L, Dasari A, You YQN, et al. Tumor Sidedness, Recurrence, and survival after curative resection of localized colon cancer. Clin Colorectal Cancer 1 Mars. 2021;20(1):e53\u201360.","journal-title":"Clin Colorectal Cancer 1 Mars"},{"issue":"1","key":"951_CR5","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1001\/jamaoncol.2023.5098","volume":"10","author":"J Nors","year":"2024","unstructured":"Nors J, Iversen LH, Erichsen R, Gotschalck KA, Andersen CL. Incidence of recurrence and time to recurrence in stage I to III colorectal cancer: A nationwide Danish cohort study. JAMA Oncol 1 Janv. 2024;10(1):54\u201362.","journal-title":"JAMA Oncol 1 Janv"},{"key":"951_CR6","doi-asserted-by":"crossref","unstructured":"Van Cutsem E, K\u00f6hne CH, L\u00e1ng I, Folprecht G, Nowacki MP, Cascinu S et al. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status. JCO. 20 mai. 2011;29(15):2011\u20139.","DOI":"10.1200\/JCO.2010.33.5091"},{"issue":"7","key":"951_CR7","doi-asserted-by":"publisher","first-page":"e170278","DOI":"10.1001\/jamaoncol.2017.0278","volume":"3","author":"G Tomasello","year":"2017","unstructured":"Tomasello G, Petrelli F, Ghidini M, Russo A, Passalacqua R, Barni S. FOLFOXIRI plus bevacizumab as conversion therapy for patients with initially unresectable metastatic colorectal cancer. JAMA Oncol Juill. 2017;3(7):e170278.","journal-title":"JAMA Oncol Juill"},{"issue":"1","key":"951_CR8","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1186\/s12885-016-2734-y","volume":"16","author":"TEA Botrel","year":"2016","unstructured":"Botrel TEA, Clark LG, de Paladini O, Clark L. Efficacy and safety of bevacizumab plus chemotherapy compared to chemotherapy alone in previously untreated advanced or metastatic colorectal cancer: a systematic review and meta-analysis. BMC Cancer 24 ao\u00fbt. 2016;16(1):677.","journal-title":"BMC Cancer 24 ao\u00fbt"},{"issue":"5","key":"951_CR9","doi-asserted-by":"publisher","first-page":"e0232353","DOI":"10.1371\/journal.pone.0232353","volume":"15","author":"VA Eng","year":"2020","unstructured":"Eng VA, Rayess N, Nguyen HV, Leng T. Complete RPE and outer retinal atrophy in patients receiving anti-VEGF treatment for neovascular age-related macular degeneration. PLoS One 5 Mai. 2020;15(5):e0232353.","journal-title":"PLoS One 5 Mai"},{"issue":"1","key":"951_CR10","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.ophtha.2013.08.015","volume":"121","author":"JE Grunwald","year":"2014","unstructured":"Grunwald JE, Daniel E, Huang J, Ying G, shuang, Maguire MG, Toth CA, et al. Risk of geographic atrophy in the comparison of Age-related macular degeneration treatments trials. Ophthalmol Janv. 2014;121(1):150\u201361.","journal-title":"Ophthalmol Janv"},{"issue":"3","key":"951_CR11","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s11060-015-1751-z","volume":"122","author":"AK Bag","year":"2015","unstructured":"Bag AK, Kim H, Gao Y, Bolding M, Warren PP, Fathallah-Shaykh HM, et al. Prolonged treatment with bevacizumab is associated with brain atrophy: a pilot study in patients with high-grade gliomas. J Neurooncol 1 Mai. 2015;122(3):585\u201393.","journal-title":"J Neurooncol 1 Mai"},{"key":"951_CR12","doi-asserted-by":"crossref","unstructured":"Xu Q, Li J, Wu Y, Zhou W, Xu Z. Colorectal cancer chemotherapy drug bevacizumab may induce muscle atrophy through CDKN1A and TIMP4. Front Oncol [Internet]. 2022 [cit\u00e9 14 juill 2024];12. Disponible sur: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9283830\/.","DOI":"10.3389\/fonc.2022.897495"},{"key":"951_CR13","doi-asserted-by":"publisher","unstructured":"Oshiro Y, Nishida K, Shimazaki J, Shimoda M, Suzuki S. Investigation of morphological and functional changes in the liver and pancreas during bevacizumab treatment. Scandinavian J Gastroenterol [Internet] 2 juin 2020 [cit\u00e9 7 ao\u00fbt 2024]; Disponible sur: https:\/\/doi.org\/10.1080\/00365521.2020.1766556https:\/\/www.tandfonline.com\/doi\/abs\/.","DOI":"10.1080\/00365521.2020.1766556"},{"issue":"6","key":"951_CR14","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1093\/annonc\/mdp533","volume":"21","author":"S Welch","year":"2010","unstructured":"Welch S, Spithoff K, Rumble RB, Maroun J. Bevacizumab combined with chemotherapy for patients with advanced colorectal cancer: a systematic review. Annals Oncol 1 Juin. 2010;21(6):1152\u201362.","journal-title":"Annals Oncol 1 Juin"},{"issue":"4","key":"951_CR15","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.dld.2010.10.010","volume":"43","author":"E Galfrascoli","year":"2011","unstructured":"Galfrascoli E, Piva S, Cinquini M, Rossi A, La Verde N, Bramati A, et al. Risk\/benefit profile of bevacizumab in metastatic colon cancer: A systematic review and meta-analysis. Dig Liver Disease 1 Avr. 2011;43(4):286\u201394.","journal-title":"Dig Liver Disease 1 Avr"},{"key":"951_CR16","doi-asserted-by":"crossref","unstructured":"Chen Yxian, Yang Q, Kuang Jjie, Chen S, yu, Wei Y, Jiang Z et al. min,. Efficacy of adding bevacizumab in the first-line chemotherapy of metastatic colorectal cancer: evidence from seven randomized clinical trials. Gastroenterol Res Pract. 2014;2014:594930.","DOI":"10.1155\/2014\/594930"},{"key":"951_CR17","doi-asserted-by":"crossref","unstructured":"Niogret J, Limagne E, Thibaudin M, Blanc J, Bertaut A, Le Malicot K et al. Baseline splenic volume as a prognostic biomarker of FOLFIRI efficacy and a surrogate marker of MDSC accumulation in metastatic colorectal carcinoma. Cancers (Basel). 31 mai. 2020;12(6):1429.","DOI":"10.3390\/cancers12061429"},{"issue":"3","key":"951_CR18","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1136\/gutjnl-2018-316407","volume":"69","author":"A Dohan","year":"2020","unstructured":"Dohan A, Gallix B, Guiu B, Le Malicot K, Reinhold C, Soyer P, et al. Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut Mars. 2020;69(3):531\u20139.","journal-title":"Gut Mars"},{"issue":"6","key":"951_CR19","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TRPMS.2023.3265863","volume":"7","author":"PH Conze","year":"2023","unstructured":"Conze PH, Andrade-Miranda G, Singh VK, Jaouen V, Visvikis D. Current and emerging trends in medical image segmentation with deep learning. IEEE Trans Radiation Plasma Med Sci Juill. 2023;7(6):545\u201369.","journal-title":"IEEE Trans Radiation Plasma Med Sci Juill"},{"issue":"7","key":"951_CR20","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1200\/JCO.2017.75.2931","volume":"36","author":"T Aparicio","year":"2018","unstructured":"Aparicio T, Ghiringhelli F, Boige V, Le Malicot K, Taieb J, Bouch\u00e9 O, et al. Bevacizumab maintenance versus no maintenance during Chemotherapy-Free intervals in metastatic colorectal cancer: A randomized phase III trial (PRODIGE 9). JCO Mars. 2018;36(7):674\u201381.","journal-title":"JCO Mars"},{"issue":"1","key":"951_CR21","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1093\/annonc\/mdx529","volume":"29","author":"T Aparicio","year":"2018","unstructured":"Aparicio T, Bouch\u00e9 O, Taieb J, Maillard E, Kirscher S, Etienne PL, et al. Bevacizumab\u2009+\u2009chemotherapy versus chemotherapy alone in elderly patients with untreated metastatic colorectal cancer: a randomized phase II trial\u2014PRODIGE 20 study results. Ann Oncol Janv. 2018;29(1):133\u20138.","journal-title":"Ann Oncol Janv"},{"issue":"3","key":"951_CR22","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage Juill. 2006;31(3):1116\u201328.","journal-title":"NeuroImage Juill"},{"key":"951_CR23","doi-asserted-by":"crossref","unstructured":"Abbas M, Andrade-Miranda G, Bourbonne V, Visvikis D, Badic B, Conze PH. Learning transferability in deep segmentation of liver metastases. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) [Internet]. 2024 [cit\u00e9 10 oct 2024]. pp. 1\u20135. Disponible sur: https:\/\/ieeexplore.ieee.org\/abstract\/document\/10635479.","DOI":"10.1109\/ISBI56570.2024.10635479"},{"key":"951_CR24","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/978-3-031-08999-2_22","volume-title":"\u00e9diteurs. brainlesion: Glioma, multiple Sclerosis, stroke and traumatic brain injuries","author":"A Hatamizadeh","year":"2022","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D, Swin UNETR. Swin Transformers for semantic segmentation of brain tumors in MRI images. In: Crimi A, Bakas S, editors. \u00e9diteurs. brainlesion: Glioma, multiple Sclerosis, stroke and traumatic brain injuries. Cham: Springer International Publishing; 2022. pp. 272\u201384."},{"key":"951_CR25","doi-asserted-by":"publisher","first-page":"102680","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, et al. The liver tumor segmentation benchmark (LiTS). Med Image Anal f\u00e9vr. 2023;84:102680.","journal-title":"Med Image Anal f\u00e9vr"},{"issue":"5","key":"951_CR26","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell 5 Juill. 2023;5(5):e230024.","journal-title":"Radiol Artif Intell 5 Juill"},{"issue":"6","key":"951_CR27","doi-asserted-by":"publisher","first-page":"555","DOI":"10.4111\/icu.20200086","volume":"61","author":"TY Shin","year":"2020","unstructured":"Shin TY, Kim H, Lee JH, Choi JS, Min HS, Cho H, et al. Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver. Investig Clin Urol Nov. 2020;61(6):555\u201364.","journal-title":"Investig Clin Urol Nov"},{"key":"951_CR28","unstructured":"Automated segmentation of. liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease - PMC [Internet]. [cit\u00e9 7 ao\u00fbt 2024]. Disponible sur: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5244890\/."},{"key":"951_CR29","unstructured":"Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease - PMC [Internet]. [cit\u00e9 7 ao\u00fbt 2024]. Disponible sur: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5435691\/."},{"issue":"1","key":"951_CR30","doi-asserted-by":"publisher","first-page":"152","DOI":"10.2214\/AJR.13.10812","volume":"202","author":"HT Huynh","year":"2014","unstructured":"Huynh HT, Karademir I, Oto A, Suzuki K. Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation. AJR Am J Roentgenol Janv. 2014;202(1):152\u20139.","journal-title":"AJR Am J Roentgenol Janv"},{"key":"951_CR31","doi-asserted-by":"publisher","first-page":"557","DOI":"10.12659\/PJR.898936","volume":"81","author":"H Takahashi","year":"2016","unstructured":"Takahashi H, Nasu K, Minami M, Kojima T, Nishiyama H, Ishiguro T, et al. Organ atrophy induced by Sorafenib and Sunitinib \u2013 Quantitative computed tomography (CT) evaluation of the Pancreas, thyroid gland and spleen. Pol J Radiol 22 Nov. 2016;81:557\u201365.","journal-title":"Pol J Radiol 22 Nov"},{"issue":"2","key":"951_CR32","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1089\/thy.2010.0234","volume":"21","author":"L van Doorn","year":"2011","unstructured":"van Doorn L, Eskens FALM, Visser TJ, van der Lugt A, Mathijssen RHJ, Peeters RP. Sorafenib induced thyroiditis in two patients with hepatocellular carcinoma. Thyroid\u00ae f\u00e9vr. 2011;21(2):197\u2013202.","journal-title":"Thyroid\u00ae f\u00e9vr"},{"key":"951_CR33","doi-asserted-by":"publisher","unstructured":"Association of Skeletal Muscle Wasting With Treatment With Sorafenib in Patients With Advanced Renal Cell Carcinoma. : results from a placebo-controlled study. J Clin Oncol [Internet]. [cit\u00e9 17 juill 2024]. Disponible sur: https:\/\/ascopubs.org\/doi\/88-2003;_id=ori:rid:crossref.org_dat=cr_pub%20%200pubmed. https:\/\/doi.org\/10.1200\/JCO.2009.24.9730?url_ver=Z39.","DOI":"10.1200\/JCO.2009.24.9730?url_ver=Z39"},{"key":"951_CR34","unstructured":"Pancreatic Atrophy \u2014 A New late toxic effect of sorafenib. New England J Med [Internet]. [cit\u00e9 17 juill 2024]. Disponible sur: https:\/\/doi.org\/10.1056\/NEJMc1305302?url_ver=Z39.88-2003_id=ori:rid:crossref.org_dat=cr_pub%20%200pubmed."},{"key":"951_CR35","doi-asserted-by":"crossref","unstructured":"Garcia J, Hurwitz HI, Sandler AB, Miles D, Coleman RL, Deurloo R et al. Bevacizumab (Avastin\u00ae) in cancer treatment: a review of 15 years of clinical experience and future outlook. Cancer Treatment Reviews [Internet]. 1 juin 2020 [cit\u00e9 14 juill 2024];86. Disponible sur: https:\/\/www.cancertreatmentreviews.com\/article\/S0305-7372(20)30055-4\/fulltext.","DOI":"10.1016\/j.ctrv.2020.102017"},{"issue":"6","key":"951_CR36","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1007\/s00259-020-05142-w","volume":"48","author":"NJ Wesdorp","year":"2021","unstructured":"Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JHTM, Boellaard R, Punt CJA, et al. Advanced analytics and artificial intelligence in Gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 1 Juin. 2021;48(6):1785\u201394.","journal-title":"Eur J Nucl Med Mol Imaging 1 Juin"},{"key":"951_CR37","doi-asserted-by":"crossref","unstructured":"Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J et al. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Military Medical Research. 16 mai. 2023;10(1):22.","DOI":"10.1186\/s40779-023-00458-8"},{"issue":"3","key":"951_CR38","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/tomography11030020","volume":"11","author":"N Gennaro","year":"2025","unstructured":"Gennaro N, Soliman M, Borhani AA, Kelahan L, Savas H, Avery R, et al. Delta radiomics and tumor size: A new predictive radiomics model for chemotherapy response in liver metastases from breast and colorectal cancer. Tomography Mars. 2025;11(3):20.","journal-title":"Tomography Mars"},{"key":"951_CR39","doi-asserted-by":"crossref","unstructured":"Yoon S, Kim YJ, Jeon JS, Ahn SJ, Choi SJ. Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases. Korean J Clin Oncol. 30 juin. 2024;20(1):27\u201335.","DOI":"10.14216\/kjco.24005"},{"key":"951_CR40","unstructured":"(PDF) Radiomics-based artificial intelligence (AI) models in colorectal cancer (CRC) diagnosis, metastasis detection, prognosis, and treatment response. ResearchGate [Internet]. 9 ao\u00fbt 2025 [cit\u00e9 25 ao\u00fbt 2025]; Disponible sur: https:\/\/www.researchgate.net\/publication\/381672454_Radiomics-based_artificial_intelligence_AI_models_in_colorectal_cancer_CRC_diagnosis_metastasis_detection_prognosis_and_treatment_response."},{"issue":"4","key":"951_CR41","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer Mars. 2012;48(4):441\u20136.","journal-title":"Eur J Cancer Mars"},{"issue":"19","key":"951_CR42","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.3748\/wjg.v29.i19.2888","volume":"29","author":"R Inchingolo","year":"2023","unstructured":"Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, et al. Radiomics in colorectal cancer patients. World J Gastroenterol 21 Mai. 2023;29(19):2888\u2013904.","journal-title":"World J Gastroenterol 21 Mai"},{"issue":"15","key":"951_CR43","doi-asserted-by":"publisher","first-page":"3841","DOI":"10.3390\/cancers15153841","volume":"15","author":"H Lee","year":"2023","unstructured":"Lee H, Moon SH, Hong JY, Lee J, Hyun SH. A machine learning approach using FDG PET-Based radiomics for prediction of tumor mutational burden and prognosis in stage IV colorectal cancer. Cancers Janv. 2023;15(15):3841.","journal-title":"Cancers Janv"}],"container-title":["Cancer Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00951-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40644-025-00951-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00951-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T18:13:55Z","timestamp":1762884835000},"score":1,"resource":{"primary":{"URL":"https:\/\/cancerimagingjournal.biomedcentral.com\/articles\/10.1186\/s40644-025-00951-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["951"],"URL":"https:\/\/doi.org\/10.1186\/s40644-025-00951-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6600859\/v1","asserted-by":"object"}]},"ISSN":["1470-7330"],"issn-type":[{"value":"1470-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"6 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All patients included in this study were enrolled in the PRODIGE 9 and PRODIGE 20 cohorts of the F\u00e9d\u00e9ration Francophone de Canc\u00e9rologie Digestive (FFCD). Written informed consent was obtained from all participants at the time of inclusion in these trials. Both studies were approved by the FFCD. Therefore, no additional approval from an independent ethics committee was required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"129"}}