{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:48:49Z","timestamp":1791938929149,"version":"4.3.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"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":["Eur Radiol"],"DOI":"10.1007\/s00330-026-12683-5","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T22:39:37Z","timestamp":1782859177000},"page":"8212-8224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["APEX-NET: automated pancreatic evaluation network using early non-contrast CT"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5344-6933","authenticated-orcid":false,"given":"Fang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjie","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ShiZe","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"12683_CR1","doi-asserted-by":"publisher","first-page":"2894","DOI":"10.1007\/s10620-017-4720-x","volume":"62","author":"DX Jin","year":"2017","unstructured":"Jin DX, McNabb-Baltar JY, Suleiman SL et al (2017) Early abdominal imaging remains over-utilized in acute pancreatitis. Dig Dis Sci 62:2894\u20132899. https:\/\/doi.org\/10.1007\/s10620-017-4720-x","journal-title":"Dig Dis Sci"},{"key":"12683_CR2","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1136\/gutjnl-2016-313595","volume":"66","author":"SM van Dijk","year":"2017","unstructured":"van Dijk SM, Hallensleben NDL, van Santvoort HC et al (2017) Acute pancreatitis: recent advances through randomised trials. Gut 66:2024\u20132032. https:\/\/doi.org\/10.1136\/gutjnl-2016-313595","journal-title":"Gut"},{"key":"12683_CR3","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1136\/gutjnl-2012-302779","volume":"62","author":"PA Banks","year":"2013","unstructured":"Banks PA, Bollen TL, Dervenis C et al (2013) Classification of acute pancreatitis-2012: revision of the Atlanta classification and definitions by international consensus. Gut 62:102\u2013111. https:\/\/doi.org\/10.1136\/gutjnl-2012-302779","journal-title":"Gut"},{"key":"12683_CR4","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1016\/j.cgh.2011.04.026","volume":"9","author":"BU Wu","year":"2011","unstructured":"Wu BU, Hwang JQ, Gardner TH et al (2011) Lactated Ringer\u2019s solution reduces systemic inflammation compared with saline in patients with acute pancreatitis. Clin Gastroenterol Hepatol 9:710\u2013717. https:\/\/doi.org\/10.1016\/j.cgh.2011.04.026","journal-title":"Clin Gastroenterol Hepatol"},{"key":"12683_CR5","first-page":"69","volume":"139","author":"JH Ranson","year":"1974","unstructured":"Ranson JH, Rifkind KM, Roses DF, Fink SD, Eng K, Spencer FC (1974) Prognostic signs and the role of operative management in acute pancreatitis. Surg Gynecol Obstet 139:69\u201381","journal-title":"Surg Gynecol Obstet"},{"key":"12683_CR6","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1097\/00003246-198510000-00009","volume":"13","author":"WA Knaus","year":"1985","unstructured":"Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13:818\u2013829","journal-title":"Crit Care Med"},{"key":"12683_CR7","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1136\/gut.2008.152702","volume":"57","author":"BU Wu","year":"2008","unstructured":"Wu BU, Johannes RS, Sun X, Tabak Y, Conwell DL, Banks PA (2008) The early prediction of mortality in acute pancreatitis: a large population-based study. Gut 57:1698\u20131703. https:\/\/doi.org\/10.1136\/gut.2008.152702","journal-title":"Gut"},{"key":"12683_CR8","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1148\/radiology.174.2.2296641","volume":"174","author":"EJ Balthazar","year":"1990","unstructured":"Balthazar EJ, Robinson DL, Megibow AJ, Ranson JH (1990) Acute pancreatitis: value of CT in establishing prognosis. Radiology 174:331\u2013336. https:\/\/doi.org\/10.1148\/radiology.174.2.2296641","journal-title":"Radiology"},{"key":"12683_CR9","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.2214\/ajr.183.5.1831261","volume":"183","author":"KJ Mortele","year":"2004","unstructured":"Mortele KJ, Wiesner W, Intriere L et al (2004) A modified CT severity index for evaluating acute pancreatitis: improved correlation with patient outcome. AJR Am J Roentgenol 183:1261\u20131265. https:\/\/doi.org\/10.2214\/ajr.183.5.1831261","journal-title":"AJR Am J Roentgenol"},{"key":"12683_CR10","doi-asserted-by":"publisher","unstructured":"Leuthauser A, McVane B (2016) Abdominal pain in the geriatric patient. Emerg Med Clin North Am. https:\/\/doi.org\/10.1016\/j.emc.2015.12.009","DOI":"10.1016\/j.emc.2015.12.009"},{"key":"12683_CR11","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1186\/s13046-024-03166-w","volume":"43","author":"KL Reese","year":"2024","unstructured":"Reese KL, Pantel K, Smit DJ (2024) Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer\u2014a comprehensive review. J Exp Clin Cancer Res 43:250. https:\/\/doi.org\/10.1186\/s13046-024-03166-w","journal-title":"J Exp Clin Cancer Res"},{"key":"12683_CR12","doi-asserted-by":"publisher","first-page":"e192","DOI":"10.1016\/S2589-7500(20)30025-X","volume":"2","author":"PJ Pickhardt","year":"2020","unstructured":"Pickhardt PJ, Graffy PM, Zea R et al (2020) Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Lancet Digit Health 2:e192\u2013e200. https:\/\/doi.org\/10.1016\/S2589-7500(20)30025-X","journal-title":"Lancet Digit Health"},{"key":"12683_CR13","doi-asserted-by":"publisher","first-page":"e222044","DOI":"10.1148\/radiol.222044","volume":"307","author":"PJ Pickhardt","year":"2023","unstructured":"Pickhardt PJ, Summers RM, Garrett JW et al (2023) Opportunistic screening: radiology scientific expert panel. Radiology 307:e222044. https:\/\/doi.org\/10.1148\/radiol.222044","journal-title":"Radiology"},{"key":"12683_CR14","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1148\/radiol.2021210607","volume":"302","author":"T Zver","year":"2022","unstructured":"Zver T, Calame P, Koch S, Aubry S, Vuitton L, Delabrousse E (2022) Early prediction of acute biliary pancreatitis using clinical and abdominal CT features. Radiology 302:118\u2013126. https:\/\/doi.org\/10.1148\/radiol.2021210607","journal-title":"Radiology"},{"key":"12683_CR15","doi-asserted-by":"publisher","first-page":"e45","DOI":"10.1097\/MPA.0000000000002216","volume":"52","author":"Z Chen","year":"2023","unstructured":"Chen Z, Wang Y, Zhang H et al (2023) Deep learning models for severity prediction of acute pancreatitis in the early phase from abdominal nonenhanced computed tomography images. Pancreas 52:e45\u2013e53. https:\/\/doi.org\/10.1097\/MPA.0000000000002216","journal-title":"Pancreas"},{"key":"12683_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.24976\/Discov.Med.202537192.7","volume":"37","author":"K Xiang","year":"2025","unstructured":"Xiang K, Shang D (2025) Construction of a multi-view deep learning model for the severity classification of acute pancreatitis. Discov Med 37:73\u201392. https:\/\/doi.org\/10.24976\/Discov.Med.202537192.7","journal-title":"Discov Med"},{"key":"12683_CR17","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02414-5","volume":"24","author":"R Zhang","year":"2024","unstructured":"Zhang R, Yin M, Jiang A, Zhang S, Xu X, Liu L (2024) Automated machine learning for early prediction of acute kidney injury in acute pancreatitis. BMC Med Inform Decis Mak 24:16. https:\/\/doi.org\/10.1186\/s12911-024-02414-5","journal-title":"BMC Med Inform Decis Mak"},{"key":"12683_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-34257-x","volume":"13","author":"F Shi","year":"2022","unstructured":"Shi F, Hu W, Wu J et al (2022) Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy. Nat Commun 13:6566. https:\/\/doi.org\/10.1038\/s41467-022-34257-x","journal-title":"Nat Commun"},{"key":"12683_CR19","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1111\/1751-2980.12961","volume":"22","author":"P Alberti","year":"2021","unstructured":"Alberti P, Pando E, Mata R et al (2021) Evaluation of the modified computed tomography severity index (MCTSI) and computed tomography severity index (CTSI) in predicting severity and clinical outcomes in acute pancreatitis. J Dig Dis 22:41\u201348. https:\/\/doi.org\/10.1111\/1751-2980.12961","journal-title":"J Dig Dis"},{"key":"12683_CR20","doi-asserted-by":"publisher","first-page":"1760036","DOI":"10.1080\/00325481.2022.2099193","volume":"16","author":"J Cao","year":"2022","unstructured":"Cao J, Long S, Liu H et al (2022) Prediction of the severity of acute pancreatitis using machine learning models. Postgrad Med 16:1760036. https:\/\/doi.org\/10.1080\/00325481.2022.2099193","journal-title":"Postgrad Med"},{"key":"12683_CR21","doi-asserted-by":"publisher","first-page":"e240478","DOI":"10.1148\/ryai.240478","volume":"7","author":"DH Shiraishi","year":"2025","unstructured":"Shiraishi DH, Saha S, Adanyeguh IM et al (2025) Automated deep learning-based segmentation of the dentate nucleus using quantitative susceptibility mapping MRI. Radiol Artif Intell 7:e240478. https:\/\/doi.org\/10.1148\/ryai.240478","journal-title":"Radiol Artif Intell"},{"key":"12683_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01903-9","volume":"8","author":"W Wang","year":"2025","unstructured":"Wang W, Yang G, Liu Y et al (2025) Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study. npj Digit Med 8:503. https:\/\/doi.org\/10.1038\/s41746-025-01903-9","journal-title":"npj Digit Med"},{"key":"12683_CR23","doi-asserted-by":"publisher","first-page":"e240300","DOI":"10.1148\/ryai.240300","volume":"6","author":"AS Tejani","year":"2024","unstructured":"Tejani AS, Klontzas ME, Gatti AA et al (2024) Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell 6:e240300. https:\/\/doi.org\/10.1148\/ryai.240300","journal-title":"Radiol Artif Intell"},{"key":"12683_CR24","doi-asserted-by":"publisher","first-page":"200029","DOI":"10.1148\/ryai.2020200029","volume":"2","author":"J Mongan","year":"2020","unstructured":"Mongan J, Moy L, Kahn CE Jr (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:200029. https:\/\/doi.org\/10.1148\/ryai.2020200029","journal-title":"Radiol Artif Intell"},{"key":"12683_CR25","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1038\/s44172-024-00287-0","volume":"3","author":"X Ouyang","year":"2024","unstructured":"Ouyang X, Gu D, Li X et al (2024) Towards a general computed tomography image segmentation model for anatomical structures and lesions. Commun Eng 3:143. https:\/\/doi.org\/10.1038\/s44172-024-00287-0","journal-title":"Commun Eng"},{"key":"12683_CR26","doi-asserted-by":"publisher","first-page":"106141","DOI":"10.1016\/j.cmpb.2021.106141","volume":"207","author":"RO Dogan","year":"2021","unstructured":"Dogan RO, Dogan H, Bayrak C, Kayikcioglu T (2021) A two-phase approach using mask R-CNN and 3D U-Net for high-accuracy automatic segmentation of pancreas in CT imaging. Comput Methods Programs Biomed 207:106141. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106141","journal-title":"Comput Methods Programs Biomed"},{"key":"12683_CR27","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-02066-3","volume":"22","author":"L Yuan","year":"2022","unstructured":"Yuan L, Ji M, Wang S et al (2022) Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study. BMC Med Inform Decis Mak 22:312. https:\/\/doi.org\/10.1186\/s12911-022-02066-3","journal-title":"BMC Med Inform Decis Mak"},{"key":"12683_CR28","doi-asserted-by":"publisher","first-page":"104430","DOI":"10.1016\/j.bspc.2022.104430","volume":"81","author":"Y Deng","year":"2023","unstructured":"Deng Y, Lan L, You L et al (2023) Automated CT pancreas segmentation for acute pancreatitis patients by combining a novel object detection approach and U-Net. Biomed Signal Process Control 81:104430. https:\/\/doi.org\/10.1016\/j.bspc.2022.104430","journal-title":"Biomed Signal Process Control"},{"key":"12683_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01509-9","volume":"24","author":"M Qi","year":"2024","unstructured":"Qi M, Lu C, Dai R, Zhang J, Hu H, Shan X (2024) Prediction of acute pancreatitis severity based on early CT radiomics. BMC Med Imaging 24:321. https:\/\/doi.org\/10.1186\/s12880-024-01509-9","journal-title":"BMC Med Imaging"},{"key":"12683_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01339-9","volume":"24","author":"C Zhang","year":"2024","unstructured":"Zhang C, Peng J, Wang L et al (2024) A deep learning-powered diagnostic model for acute pancreatitis. BMC Med Imaging 24:154. https:\/\/doi.org\/10.1186\/s12880-024-01339-9","journal-title":"BMC Med Imaging"},{"key":"12683_CR31","doi-asserted-by":"publisher","first-page":"718","DOI":"10.3390\/diagnostics14070718","volume":"14","author":"S Bette","year":"2024","unstructured":"Bette S, Canalini L, Feitelson LM et al (2024) Radiomics-based machine learning model for diagnosis of acute pancreatitis using computed tomography. Diagnostics (Basel) 14:718. https:\/\/doi.org\/10.3390\/diagnostics14070718","journal-title":"Diagnostics (Basel)"},{"key":"12683_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-021-00588-2","volume":"21","author":"D Gu","year":"2021","unstructured":"Gu D, Chen L, Shan F et al (2021) Computing infection distributions and longitudinal evolution patterns in lung CT images. BMC Med Imaging 21:57. https:\/\/doi.org\/10.1186\/s12880-021-00588-2","journal-title":"BMC Med Imaging"},{"key":"12683_CR33","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely Connected Convolutional Networks. IEEE CVPR 4700\u20134708. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"12683_CR34","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. IEEE CVPR 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"12683_CR35","doi-asserted-by":"publisher","first-page":"102608","DOI":"10.1016\/j.media.2022.102608","volume":"82","author":"C Playout","year":"2022","unstructured":"Playout C, Duval R, Boucher MC, Cheriet F (2022) Focused attention in transformers for interpretable classification of retinal images. Med Image Anal 82:102608. https:\/\/doi.org\/10.1016\/j.media.2022.102608","journal-title":"Med Image Anal"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12683-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12683-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12683-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:02:37Z","timestamp":1791936157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-026-12683-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":35,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,11]]}},"alternative-id":["12683"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12683-5","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"9 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The scientific guarantor of this publication is Dinggang Shen.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare the following potential conflicts of interest: Fang Wang, Qing Zhou, Zhong Xue, and Dinggang Shen are employees of Shanghai United Imaging Intelligence Co., Ltd. Among them, Fang Wang and Qing Zhou are regular employees without any decision-making authority in the company. Zhong Xue holds a departmental leadership position, and Dinggang Shen is the Co-CEO; both have decision-making authority regarding company products and operations. However, it is important to note that this research did not utilize any company products and was conducted solely using open-source code and publicly available methodologies.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No complex statistical methods were necessary for this paper.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was waived for this retrospective study because the research involved only the analysis of existing, anonymized patient data, posed no risk to participants, and received approval from the Institutional Review Board\/Ethics Committee.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We affirm that this manuscript has not been published previously, either in whole or in part, and is not currently under consideration for publication elsewhere.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}