{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T12:36:14Z","timestamp":1793018174866,"version":"4.5.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Abdom Radiol"],"DOI":"10.1007\/s00261-025-04887-y","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T13:44:53Z","timestamp":1743687893000},"page":"4615-4624","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based segmentation of gallbladder cancer on abdominal computed tomography scans: a multicenter study"],"prefix":"10.1007","volume":"50","author":[{"given":"Pankaj","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niharika","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajay","family":"Tomar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shravya","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonam","family":"Choudhary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandita","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vansha","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rishabh","family":"Sheth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divyashree","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salai","family":"Thanihai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Palki","family":"Singla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaurav","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thakur","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lileswar","family":"Kaman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santosh","family":"Irrinki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harjeet","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niket","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Choudhari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shraddha","family":"Patkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahesh","family":"Goel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajnikant","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Archana","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ishan","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kajal","family":"Seth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usha","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chetan","family":"Arora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"4887_CR1","doi-asserted-by":"crossref","unstructured":"Valle JW, Kelley RK, Nervi B, Oh DY, Zhu AX. Biliary tract cancer. Lancet. 2021;397:428\u201344. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36cancers in 185 countries.CA Cancer J Clin. 2021;71(3):209-249.","DOI":"10.3322\/caac.21660"},{"key":"4887_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.21037\/cco.2019.08.03","volume":"8","author":"U Dutta","year":"2019","unstructured":"Dutta U, Bush N, Kalsi D, Popli P, Kapoor VK. Epidemiology of gallbladder cancer in India. Chin Clin Oncol.2019;8:33.","journal-title":"Chin Clin Oncol"},{"issue":"43","key":"4887_CR3","doi-asserted-by":"publisher","first-page":"12211","DOI":"10.3748\/wjg.v21.i43.12211","volume":"21","author":"TO Goetze","year":"2015","unstructured":"Goetze TO. Gallbladder carcinoma: Prognostic factors and therapeutic options. World J Gastroenterol. 2015;21(43):12211\u20137.","journal-title":"World J Gastroenterol"},{"issue":"3","key":"4887_CR4","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1007\/s00261-020-02756-4","volume":"46","author":"P Gupta","year":"2021","unstructured":"Gupta P, Meghashyam K, Marodia Y, Gupta V, Basher R, Das CK, Yadav TD, Irrinki S, Nada R, Dutta U. Locally advanced gallbladder cancer: a review of the criteria and role of imaging. Abdom Radiol (NY). 2021;46(3):998\u20131007","journal-title":"Abdom Radiol (NY)"},{"issue":"6","key":"4887_CR5","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1148\/rg.2021210037","volume":"41","author":"JD Shur","year":"2021","unstructured":"Shur JD, Doran SJ, Kumar S, et al. Radiomics in Oncology: A Practical Guide. Radiographics. 2021;41(6):1717\u20131732. doi:https:\/\/doi.org\/10.1148\/rg.2021210037","journal-title":"Radiographics"},{"issue":"1","key":"4887_CR6","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1148\/radiol.2018181408","volume":"290","author":"GW Ji","year":"2019","unstructured":"Ji GW, Zhang YD, Zhang H, et al. Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes. Radiology. 2019;290(1):90\u20138.","journal-title":"Radiology"},{"key":"4887_CR7","doi-asserted-by":"publisher","first-page":"1045295","DOI":"10.3389\/fsurg.2022.1045295","volume":"9","author":"Y Ma","year":"2022","unstructured":"Ma Y, Lin Y, Lu J, et al. A meta-analysis of based radiomics for predicting lymph node metastasis in patients with biliary tract cancers. Front Surg. 2022;9:1045295.","journal-title":"Front Surg"},{"key":"4887_CR8","doi-asserted-by":"crossref","unstructured":"Gupta P, Basu S, Arora C. Applications of artificial intelligence in biliary tract cancers. Indian J Gastroenterol. 2024 Mar 1.","DOI":"10.1007\/s12664-024-01518-0"},{"key":"4887_CR9","doi-asserted-by":"publisher","first-page":"100279","DOI":"10.1016\/j.lansea.2023.100279","volume":"24","author":"P Gupta","year":"2023","unstructured":"Gupta P, Basu S, Rana P, Dutta U, Soundararajan R, Kalage D, Chhabra M, Singh S, Yadav TD, Gupta V, Kaman L, Das CK, Gupta P, Saikia UN, Srinivasan R, Sandhu MS, Arora C. Deep-learning enabled ultrasound based detection of gallbladder cancer in northern India: a prospective diagnostic study. Lancet Reg Health Southeast Asia. 2023;24:100279","journal-title":"Lancet Reg Health Southeast Asia"},{"key":"4887_CR10","doi-asserted-by":"publisher","first-page":"102676","DOI":"10.1016\/j.media.2022.102676","volume":"83","author":"S Basu","year":"2023","unstructured":"Basu S, Gupta M, Rana P, Gupta P, Arora C. RadFormer: Transformers with global-local attention for interpretable and accurate Gallbladder Cancer detection. Med Image Anal. 2023;83:102676","journal-title":"Med Image Anal"},{"issue":"4","key":"4887_CR11","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.hbpd.2023.04.001","volume":"23","author":"F Xiang","year":"2024","unstructured":"Xiang F, Meng QT, Deng JJ, Wang J, Liang XY, Liu XY, Yan S. A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma. Hepatobiliary Pancreat Dis Int. 2024;23(4):376\u2013384","journal-title":"Hepatobiliary Pancreat Dis Int"},{"issue":"4","key":"4887_CR12","doi-asserted-by":"publisher","first-page":"704","DOI":"10.3390\/diagnostics13040704","volume":"13","author":"Y Yin","year":"2023","unstructured":"Yin Y, Yakar D, Slangen JJG, Hoogwater FJH, Kwee TC, de Haas RJ. The Value of Deep Learning in Gallbladder Lesion Characterization. Diagnostics (Basel). 2023;13(4):704","journal-title":"Diagnostics (Basel)"},{"issue":"6","key":"4887_CR13","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1002\/ags3.12589","volume":"6","author":"H Fujita","year":"2022","unstructured":"Fujita H, Wakiya T, Ishido K, et al. Differential diagnoses of gallbladder tumors using CT-based deep learning. Ann Gastroenterol Surg. 2022;6(6):823\u2013832","journal-title":"Ann Gastroenterol Surg"},{"key":"4887_CR14","doi-asserted-by":"publisher","first-page":"107786","DOI":"10.1016\/j.compbiomed.2023.107786","volume":"168","author":"W Zhang","year":"2024","unstructured":"Zhang W, Wang Q, Liang K, et al. Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision. Comput Biol Med. 2024;168:107786","journal-title":"Comput Biol Med"},{"issue":"1","key":"4887_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00261-023-04029-2","volume":"49","author":"S Zhou","year":"2024","unstructured":"Zhou S, Han S, Chen W, et al. Radiomics-based machine learning and deep learning\u2002to predict serosal involvement in gallbladder cancer. Abdom Radiol (NY). 2024;49(1):3\u201310","journal-title":"Abdom Radiol (NY)"},{"key":"4887_CR16","doi-asserted-by":"publisher","first-page":"604288","DOI":"10.3389\/fonc.2020.604288","volume":"10","author":"Z Liu","year":"2020","unstructured":"Liu Z, Zhu G, Jiang X, et al. Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning. Front Oncol. 2020;10:604288","journal-title":"Front Oncol"},{"key":"4887_CR17","doi-asserted-by":"publisher","first-page":"633852","DOI":"10.3389\/fonc.2021.633852","volume":"11","author":"X Liu","year":"2021","unstructured":"Liu X, Liang X, Ruan L, Yan S. A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer. Front Oncol. 2021;11:633852","journal-title":"Front Oncol"},{"issue":"4","key":"4887_CR18","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.1007\/s00330-022-09281-6","volume":"33","author":"Y Yin","year":"2023","unstructured":"Yin Y, Yakar D, Slangen JJG, Hoogwater FJH, Kwee TC, de Haas RJ. Optimal radiological gallbladder lesion characterization by combining visual assessment with CT-based radiomics. Eur Radiol. 2023;33(4):2725\u20132734","journal-title":"Eur Radiol"},{"key":"4887_CR19","unstructured":"Meng FX, Zhang JX, Guo YR, Wang LJ, Zhang HZ, Shao WH, Xu J. Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer Postoperative. Acad Radiol. 2023 Dec 6:S1076-6332(23)00663-3"},{"issue":"4","key":"4887_CR20","doi-asserted-by":"publisher","first-page":"e230437","DOI":"10.1148\/ryai.230437","volume":"6","author":"N Horvat","year":"2024","unstructured":"Horvat N, Papanikolaou N, Koh DM. Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use. Radiol Artif Intell. 2024;6(4):e230437. doi:https:\/\/doi.org\/10.1148\/ryai.230437","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"4887_CR21","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1148\/radiol.220152","volume":"306","author":"PT Chen","year":"2023","unstructured":"Chen PT, Wu T, Wang P, et al. Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. Radiology. 2023;306(1):172\u2013182. doi:https:\/\/doi.org\/10.1148\/radiol.220152","journal-title":"Radiology"},{"issue":"17","key":"4887_CR22","doi-asserted-by":"publisher","first-page":"8650","DOI":"10.3390\/app12178650","volume":"12","author":"MW Sabir","year":"2022","unstructured":"Sabir MW, Khan Z, Saad NM, Khan DM, Al-Khasawneh MA, Perveen K, Qayyum A, Azhar Ali SS. Segmentation of Liver Tumor in CT Scan Using ResU-Net. Applied Sciences. 2022; 12(17):8650. https:\/\/doi.org\/10.3390\/app12178650","journal-title":"Applied Sciences"},{"key":"4887_CR23","doi-asserted-by":"publisher","unstructured":"Lim SH, Kim YJ, Park YH, Kim D, Kim KG, Lee DH. Automated pancreas segmentation and volumetry using deep neural network on computed tomography. Sci Rep. 2022;12(1):4075. Published 2022 Mar 8. doi:https:\/\/doi.org\/10.1038\/s41598-022-07848-3","DOI":"10.1038\/s41598-022-07848-3"},{"key":"4887_CR24","doi-asserted-by":"publisher","first-page":"1169922","DOI":"10.3389\/fonc.2023.1169922","volume":"13","author":"T Zhao","year":"2023","unstructured":"Zhao T, Sun Z, Guo Y, Sun Y, Zhang Y, Wang X. Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms. Front Oncol. 2023;13:1169922. Published 2023 May 18. doi:https:\/\/doi.org\/10.3389\/fonc.2023.1169922","journal-title":"Front Oncol"},{"issue":"4","key":"4887_CR25","doi-asserted-by":"publisher","first-page":"e230138","DOI":"10.1148\/ryai.230138","volume":"6","author":"MK Fassia","year":"2024","unstructured":"Fassia MK, Balasubramanian A, Woo S, et al. Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review. Radiol Artif Intell. 2024;6(4):e230138. doi:https:\/\/doi.org\/10.1148\/ryai.230138","journal-title":"Radiol Artif Intell"},{"issue":"3","key":"4887_CR26","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"A Paul","year":"2006","unstructured":"Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006;31(3):1116\u201328.","journal-title":"Neuroimage"},{"key":"4887_CR27","doi-asserted-by":"crossref","unstructured":"Chen T, Zhu L, Deng C, et al. Sam-adapter: Adapting segment anything in underperformed scenes. InProceedings of the IEEE\/CVF International Conference on Computer Vision 2023 (pp. 3367\u20133375).","DOI":"10.1109\/ICCVW60793.2023.00361"},{"issue":"1","key":"4887_CR28","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma J, He Y, Li F, Han L, You C, Wang B. Segment anything in medical images. Nat Commun. 2024;15(1):654. Published 2024 Jan 22. doi:https:\/\/doi.org\/10.1038\/s41467-024-44824-z","journal-title":"Nat Commun"},{"key":"4887_CR29","unstructured":"Chen J, Mei J, Li X, Lu Y, Yu Q, Wei Q, Luo X, Xie Y, Adeli E, Wang Y, Lungren M. 3d transunet: Advancing medical image segmentation through vision transformers. arXiv preprint arXiv:2310.07781. 2023 Oct 11."},{"key":"4887_CR30","unstructured":"Wang H, Guo S, Ye J, et al. Sam-med3d: towards general-purpose segmentation models for volumetric medical images. arXiv preprint arXiv:2310.15161. 2023 Oct 23."},{"issue":"2","key":"4887_CR31","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021;18(2):203\u2013211.","journal-title":"Nature methods"},{"key":"4887_CR32","doi-asserted-by":"crossref","unstructured":"Pan S, Lo SY, Huang M, Ma C, Wynne J, Wang T, Liu T, Yang X. Deep learning-based multi-organ CT segmentation with adversarial data augmentation. InMedical Imaging 2023: Image Processing 2023 Apr 3 (Vol. 12464, pp. 553\u2013560). SPIE.","DOI":"10.1117\/12.2653970"},{"key":"4887_CR33","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s12938-024-01238-8","volume":"23","author":"X Liu","year":"2024","unstructured":"Liu, X., Qu, L., Xie, Z. et al. Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation. BioMed Eng OnLine 2024; 23:52.","journal-title":"BioMed Eng OnLine"},{"issue":"2","key":"4887_CR34","first-page":"46766","volume":"55","author":"B Wu","year":"2024","unstructured":"Wu B, Liu Y, Wu MJ, Shaish H, Ma HY. Usage of Artificial Intelligence in Gallbladder Segmentation to Diagnose Acute Cholecystitis. Biomedical Journal of Scientific & Technical Research. 2024;55(2):46766\u201370.","journal-title":"Biomedical Journal of Scientific & Technical Research"},{"key":"4887_CR35","unstructured":"Lee HH, Gu Y, Zhao T, Xu Y, Yang J, Usuyama N, Wong C, Wei M, Landman BA, Huo Y, Santamaria-Pang A. Foundation models for biomedical image segmentation: A survey. arXiv preprint arXiv:2401.07654. 2024 Jan 15."},{"key":"4887_CR36","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo WY, Doll\u00e1r P. Segment anything. InProceedings of the IEEE\/CVF international conference on computer vision 2023 (pp. 4015\u20134026).","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"4887_CR37","doi-asserted-by":"crossref","unstructured":"He Y, Huang F, Jiang X, Nie Y, Wang M, Wang J, Chen H. Foundation model for advancing healthcare: challenges, opportunities and future directions. IEEE Reviews in Biomedical Engineering. 2024 Nov 12.","DOI":"10.1109\/RBME.2024.3496744"},{"key":"4887_CR38","doi-asserted-by":"publisher","first-page":"108238","DOI":"10.1016\/j.compbiomed.2024.108238","volume":"27","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Shen Z, Jiao R. Segment anything model for medical image segmentation: Current applications and future directions. Computers in Biology and Medicine. 2024 Feb 27:108238.","journal-title":"Computers in Biology and Medicine"}],"container-title":["Abdominal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-04887-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00261-025-04887-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-025-04887-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T12:04:01Z","timestamp":1758715441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00261-025-04887-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,1]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["4887"],"URL":"https:\/\/doi.org\/10.1007\/s00261-025-04887-y","relation":{},"ISSN":["2366-0058"],"issn-type":[{"value":"2366-0058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,1]]},"assertion":[{"value":"26 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}