{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T12:43:45Z","timestamp":1793105025944,"version":"4.5.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA256969"],"award-info":[{"award-number":["R01CA256969"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Mayo Clinic Comprehensive Cancer Center","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}]},{"name":"Champions for Hope Pancreatic Cancer Research Program of the Funk Zitiello Foundation","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}]},{"name":"Centene Charitable Foundation","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}]},{"name":"Hoveida Family Foundation","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-01802-9","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T12:30:19Z","timestamp":1747398619000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection"],"prefix":"10.1038","volume":"15","author":[{"given":"Sovanlal","family":"Mukherjee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajith","family":"Antony","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandakumar G.","family":"Patnam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamaxi H.","family":"Trivedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aashna","family":"Karbhari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhu","family":"Nagaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murlidhar","family":"Murlidhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajit H.","family":"Goenka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"1802_CR1","unstructured":"Five-Year Pancreatic Cancer Survival Rate Increases to 12% https:\/\/pancan.org\/news\/five-year-pancreatic-cancer-survival-rate-increases-to-12\/."},{"key":"1802_CR2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3322\/caac.21820","volume":"74","author":"RL Siegel","year":"2024","unstructured":"Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 74, 12\u201349. https:\/\/doi.org\/10.3322\/caac.21820 (2024).","journal-title":"CA Cancer J. Clin."},{"key":"1802_CR3","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/s1542-3565(04)00171-5","volume":"2","author":"EA Holly","year":"2004","unstructured":"Holly, E. A., Chaliha, I., Bracci, P. M. & Gautam, M. Signs and symptoms of pancreatic cancer: A population-based case-control study in the San Francisco Bay area. Clin. Gastroenterol. Hepatol. 2, 510\u2013517. https:\/\/doi.org\/10.1016\/s1542-3565(04)00171-5 (2004).","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"1802_CR4","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1053\/j.gastro.2019.01.259","volume":"156","author":"AD Singhi","year":"2019","unstructured":"Singhi, A. D., Koay, E. J., Chari, S. T. & Maitra, A. Early detection of pancreatic cancer: Opportunities and challenges. Gastroenterology 156, 2024\u20132040. https:\/\/doi.org\/10.1053\/j.gastro.2019.01.259 (2019).","journal-title":"Gastroenterology"},{"key":"1802_CR5","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1097\/MPA.0000000000001762","volume":"50","author":"B Kenner","year":"2021","unstructured":"Kenner, B. et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review. Pancreas 50, 251\u2013279. https:\/\/doi.org\/10.1097\/MPA.0000000000001762 (2021).","journal-title":"Pancreas"},{"key":"1802_CR6","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.2020190020","volume":"2","author":"AC Morani","year":"2020","unstructured":"Morani, A. C. et al. Hereditary and sporadic pancreatic ductal adenocarcinoma: Current update on genetics and imaging. Radiol. Imaging Cancer 2, e190020. https:\/\/doi.org\/10.1148\/rycan.2020190020 (2020).","journal-title":"Radiol. Imaging Cancer"},{"key":"1802_CR7","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.gastro.2005.05.007","volume":"129","author":"ST Chari","year":"2005","unstructured":"Chari, S. T. et al. Probability of pancreatic cancer following diabetes: A population-based study. Gastroenterology 129, 504\u2013511. https:\/\/doi.org\/10.1016\/j.gastro.2005.05.007 (2005).","journal-title":"Gastroenterology"},{"key":"1802_CR8","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1053\/j.gastro.2018.05.023","volume":"155","author":"A Sharma","year":"2018","unstructured":"Sharma, A. et al. Model to determine risk of pancreatic cancer in patients with new-onset diabetes. Gastroenterology 155, 730\u2013739. https:\/\/doi.org\/10.1053\/j.gastro.2018.05.023 (2018).","journal-title":"Gastroenterology"},{"key":"1802_CR9","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1053\/j.gastro.2021.10.014","volume":"162","author":"KA Overbeek","year":"2022","unstructured":"Overbeek, K. A. et al. Timeline of development of pancreatic cancer and implications for successful early detection in high-risk individuals. Gastroenterology 162, 772\u2013785. https:\/\/doi.org\/10.1053\/j.gastro.2021.10.014 (2022).","journal-title":"Gastroenterology"},{"key":"1802_CR10","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1053\/j.gastro.2021.11.021","volume":"162","author":"A Chhoda","year":"2022","unstructured":"Chhoda, A. et al. Late-stage pancreatic cancer detected during high-risk individual surveillance: A systematic review and meta-analysis. Gastroenterology 162, 786\u2013798. https:\/\/doi.org\/10.1053\/j.gastro.2021.11.021 (2022).","journal-title":"Gastroenterology"},{"key":"1802_CR11","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1136\/gutjnl-2014-308653","volume":"64","author":"J Yu","year":"2015","unstructured":"Yu, J., Blackford, A. L., Dal Molin, M., Wolfgang, C. L. & Goggins, M. Time to progression of pancreatic ductal adenocarcinoma from low-to-high tumour stages. Gut 64, 1783\u20131789. https:\/\/doi.org\/10.1136\/gutjnl-2014-308653 (2015).","journal-title":"Gut"},{"key":"1802_CR12","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1053\/j.gastro.2022.06.066","volume":"163","author":"S Mukherjee","year":"2022","unstructured":"Mukherjee, S. et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical diagnosis. Gastroenterology 163, 1435\u20131446. https:\/\/doi.org\/10.1053\/j.gastro.2022.06.066 (2022).","journal-title":"Gastroenterology"},{"key":"1802_CR13","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1007\/s00261-023-04127-1","volume":"49","author":"S Mukherjee","year":"2024","unstructured":"Mukherjee, S. et al. Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): Evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods. Abdom. Radiol. (NY) 49, 964\u2013974. https:\/\/doi.org\/10.1007\/s00261-023-04127-1 (2024).","journal-title":"Abdom. Radiol. (NY)"},{"key":"1802_CR14","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.2021210010","volume":"3","author":"PT Chen","year":"2021","unstructured":"Chen, P. T. et al. Radiomic features at CT can distinguish pancreatic cancer from noncancerous pancreas. Radiol. Imaging Cancer 3, e210010. https:\/\/doi.org\/10.1148\/rycan.2021210010 (2021).","journal-title":"Radiol. Imaging Cancer"},{"key":"1802_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.3233\/CBM-210273","volume":"33","author":"TA Qureshi","year":"2022","unstructured":"Qureshi, T. A. et al. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images. Cancer Biomark. 33, 211\u2013217. https:\/\/doi.org\/10.3233\/CBM-210273 (2022).","journal-title":"Cancer Biomark."},{"key":"1802_CR16","doi-asserted-by":"publisher","first-page":"1007990","DOI":"10.3389\/fonc.2022.1007990","volume":"12","author":"S Javed","year":"2022","unstructured":"Javed, S. et al. Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images. Front. Oncol. 12, 1007990. https:\/\/doi.org\/10.3389\/fonc.2022.1007990 (2022).","journal-title":"Front. Oncol."},{"key":"1802_CR17","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1080\/17474124.2018.1496015","volume":"12","author":"SV DeSouza","year":"2018","unstructured":"DeSouza, S. V. et al. Pancreas volume in health and disease: A systematic review and meta-analysis. Expert. Rev. Gastroenterol. Hepatol. 12, 757\u2013766. https:\/\/doi.org\/10.1080\/17474124.2018.1496015 (2018).","journal-title":"Expert. Rev. Gastroenterol. Hepatol."},{"key":"1802_CR18","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1007\/s00592-014-0581-3","volume":"51","author":"S Lim","year":"2014","unstructured":"Lim, S. et al. Differences in pancreatic volume, fat content, and fat density measured by multidetector-row computed tomography according to the duration of diabetes. Acta Diabetol. 51, 739\u2013748. https:\/\/doi.org\/10.1007\/s00592-014-0581-3 (2014).","journal-title":"Acta Diabetol."},{"key":"1802_CR19","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1111\/joim.12745","volume":"283","author":"JM Lohr","year":"2018","unstructured":"Lohr, J. M., Panic, N., Vujasinovic, M. & Verbeke, C. S. The ageing pancreas: A systematic review of the evidence and analysis of the consequences. J. Intern. Med. 283, 446\u2013460. https:\/\/doi.org\/10.1111\/joim.12745 (2018).","journal-title":"J. Intern. Med."},{"key":"1802_CR20","doi-asserted-by":"publisher","first-page":"3806","DOI":"10.1007\/s00261-022-03668-1","volume":"47","author":"DE Wright","year":"2022","unstructured":"Wright, D. E. et al. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: A proof-of-concept study. Abdom. Radiol. (NY) 47, 3806\u20133816. https:\/\/doi.org\/10.1007\/s00261-022-03668-1 (2022).","journal-title":"Abdom. Radiol. (NY)"},{"key":"1802_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/dmrr.3025","volume":"34","author":"A Lautenbach","year":"2018","unstructured":"Lautenbach, A. et al. Adaptive changes in pancreas post Roux-en-Y gastric bypass induced weight loss. Diabetes Metab. Res. Rev. 34, e3025. https:\/\/doi.org\/10.1002\/dmrr.3025 (2018).","journal-title":"Diabetes Metab. Res. Rev."},{"key":"1802_CR22","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/S2213-8587(20)30303-X","volume":"8","author":"A Al-Mrabeh","year":"2020","unstructured":"Al-Mrabeh, A. et al. 2-year remission of type 2 diabetes and pancreas morphology: A post-hoc analysis of the DiRECT open-label, cluster-randomised trial. Lancet Diabetes Endocrinol. 8, 939\u2013948. https:\/\/doi.org\/10.1016\/S2213-8587(20)30303-X (2020).","journal-title":"Lancet Diabetes Endocrinol."},{"key":"1802_CR23","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1148\/radiol.2016152547","volume":"281","author":"AB Shinagare","year":"2016","unstructured":"Shinagare, A. B., Steele, E., Braschi-Amirfarzan, M., Tirumani, S. H. & Ramaiya, N. H. Sunitinib-associated pancreatic atrophy in patients with gastrointestinal stromal tumor: A toxicity with prognostic implications detected at imaging. Radiology 281, 140\u2013149. https:\/\/doi.org\/10.1148\/radiol.2016152547 (2016).","journal-title":"Radiology"},{"key":"1802_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, Y. et al. A Fixed-Point Model for pancreas segmentation in abdominal CT Scans. 693\u2013701 (2017). https:\/\/arxiv.org\/abs\/1612.08230.","DOI":"10.1007\/978-3-319-66182-7_79"},{"key":"1802_CR25","doi-asserted-by":"crossref","unstructured":"Moglia, A., Cavicchioli, M., Mainardi, L. & Cerveri, P. Deep Learning for Pancreas Segmentation: a Systematic Review. https:\/\/arxiv.org\/abs\/2407.16313. (2024).","DOI":"10.1007\/s10462-024-11050-4"},{"key":"1802_CR26","doi-asserted-by":"publisher","first-page":"2468","DOI":"10.1002\/mp.14782","volume":"48","author":"A Panda","year":"2021","unstructured":"Panda, A. et al. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset. Med. Phys. 48, 2468\u20132481. https:\/\/doi.org\/10.1002\/mp.14782 (2021).","journal-title":"Med. Phys."},{"key":"1802_CR27","doi-asserted-by":"publisher","first-page":"730","DOI":"10.2214\/AJR.20.24567","volume":"217","author":"A Panda","year":"2021","unstructured":"Panda, A. et al. Borderline resectable and locally advanced pancreatic cancer: FDG PET\/MRI and CT tumor metrics for assessment of pathologic response to neoadjuvant therapy and prediction of survival. AJR Am. J. Roentgenol. 217, 730\u2013740. https:\/\/doi.org\/10.2214\/AJR.20.24567 (2021).","journal-title":"AJR Am. J. Roentgenol."},{"key":"1802_CR28","unstructured":"MIRC CTP http:\/\/mircwiki.rsna.org\/index.php?title=CTP-The_RSNA_Clinical_Trial_Processor."},{"key":"1802_CR29","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1097\/RCT.0000000000001374","volume":"46","author":"H Khasawneh","year":"2022","unstructured":"Khasawneh, H. et al. Volumetric pancreas segmentation on computed tomography: Accuracy and efficiency of a convolutional neural network versus manual segmentation in 3D slicer in the context of interreader variability of expert radiologists. J. Comput. Assist. Tomogr. 46, 841\u2013847. https:\/\/doi.org\/10.1097\/RCT.0000000000001374 (2022).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"1802_CR30","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, P. F., Kohl, S. A. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211. https:\/\/doi.org\/10.1038\/s41592-020-01008-z (2021).","journal-title":"Nat. Methods"},{"key":"1802_CR31","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation, 234\u2013241 (2015).https:\/\/arxiv.org\/abs\/1505.04597.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1802_CR32","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J. et al. TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5, e230024. https:\/\/doi.org\/10.1148\/ryai.230024 (2023).","journal-title":"Radiol. Artif. Intell."},{"key":"1802_CR33","doi-asserted-by":"publisher","first-page":"6695","DOI":"10.1109\/TPAMI.2021.3100536","volume":"44","author":"J Ma","year":"2022","unstructured":"Ma, J. et al. AbdomenCT-1K: Is abdominal organ segmentation a solved problem?. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6695\u20136714. https:\/\/doi.org\/10.1109\/TPAMI.2021.3100536 (2022).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1802_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P. et al. The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680. https:\/\/doi.org\/10.1016\/j.media.2022.102680 (2023).","journal-title":"Med. Image Anal."},{"key":"1802_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N. et al. The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge. Med. Image Anal. 67, 101821. https:\/\/doi.org\/10.1016\/j.media.2020.101821 (2021).","journal-title":"Med. Image Anal."},{"key":"1802_CR36","doi-asserted-by":"publisher","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli, M. et al. The Medical Segmentation Decathlon. Nat. Commun. 13, 4128. https:\/\/doi.org\/10.1038\/s41467-022-30695-9 (2022).","journal-title":"Nat. Commun."},{"key":"1802_CR37","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K. et al. The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digit Imaging 26, 1045\u20131057. https:\/\/doi.org\/10.1007\/s10278-013-9622-7 (2013).","journal-title":"J. Digit Imaging"},{"key":"1802_CR38","doi-asserted-by":"publisher","first-page":"e27096","DOI":"10.1097\/MD.0000000000027096","volume":"100","author":"L Li","year":"2021","unstructured":"Li, L. et al. Normal pancreatic volume assessment using abdominal computed tomography volumetry. Medicine 100, e27096. https:\/\/doi.org\/10.1097\/MD.0000000000027096 (2021).","journal-title":"Medicine"},{"key":"1802_CR39","doi-asserted-by":"publisher","first-page":"6371231","DOI":"10.1155\/2019\/6371231","volume":"2019","author":"J Lu","year":"2019","unstructured":"Lu, J. et al. Association between pancreatic atrophy and loss of insulin secretory capacity in patients with Type 2 diabetes mellitus. J. Diabetes Res. 2019, 6371231. https:\/\/doi.org\/10.1155\/2019\/6371231 (2019).","journal-title":"J. Diabetes Res."},{"key":"1802_CR40","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1016\/j.pan.2021.03.016","volume":"21","author":"G Suman","year":"2021","unstructured":"Suman, G. et al. Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications. Pancreatology 21, 1001\u20131008. https:\/\/doi.org\/10.1016\/j.pan.2021.03.016 (2021).","journal-title":"Pancreatology"},{"key":"1802_CR41","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.21.26014","author":"F Toshima","year":"2021","unstructured":"Toshima, F. et al. CT abnormalities of the pancreas associated with the subsequent diagnosis of clinical stage I pancreatic ductal adenocarcinoma more than 1 year later: A case-control study. AJR Am. J. Roentgenol. https:\/\/doi.org\/10.2214\/AJR.21.26014 (2021).","journal-title":"AJR Am. J. Roentgenol."},{"key":"1802_CR42","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1097\/MPA.0000000000001155","volume":"47","author":"MS Petrov","year":"2018","unstructured":"Petrov, M. S. Harnessing analytic morphomics for early detection of pancreatic cancer. Pancreas 47, 1051\u20131054. https:\/\/doi.org\/10.1097\/MPA.0000000000001155 (2018).","journal-title":"Pancreas"},{"key":"1802_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-024-04775-x","author":"A Antony","year":"2024","unstructured":"Antony, A. et al. AI-Driven insights in pancreatic cancer imaging: From pre-diagnostic detection to prognostication. Abdom. Radiol. (NY) https:\/\/doi.org\/10.1007\/s00261-024-04775-x (2024).","journal-title":"Abdom. Radiol. (NY)"},{"key":"1802_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-024-04644-7","author":"C Huang","year":"2024","unstructured":"Huang, C. et al. Advancements in early detection of pancreatic cancer: The role of artificial intelligence and novel imaging techniques. Abdom. Radiol. (NY) https:\/\/doi.org\/10.1007\/s00261-024-04644-7 (2024).","journal-title":"Abdom. Radiol. (NY)"},{"key":"1802_CR45","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.24.31151","volume":"223","author":"C Huang","year":"2024","unstructured":"Huang, C. et al. Imaging for early detection of pancreatic ductal adenocarcinoma: Updates and challenges in the implementation of screening and surveillance programs. AJR Am. J. Roentgenol. 223, e2431151. https:\/\/doi.org\/10.2214\/AJR.24.31151 (2024).","journal-title":"AJR Am. J. Roentgenol."},{"key":"1802_CR46","doi-asserted-by":"publisher","first-page":"73","DOI":"10.2214\/AJR.22.27806","volume":"220","author":"A Inoue","year":"2023","unstructured":"Inoue, A. et al. Diagnostic performance in low- and high-contrast tasks of an image-based denoising algorithm applied to radiation dose-reduced multiphase abdominal CT examinations. AJR Am. J. Roentgenol. 220, 73\u201385. https:\/\/doi.org\/10.2214\/AJR.22.27806 (2023).","journal-title":"AJR Am. J. Roentgenol."},{"key":"1802_CR47","doi-asserted-by":"publisher","first-page":"52","DOI":"10.2214\/AJR.17.18665","volume":"211","author":"RR Almeida","year":"2018","unstructured":"Almeida, R. R. et al. Advances in pancreatic CT imaging. AJR Am. J. Roentgenol. 211, 52\u201366. https:\/\/doi.org\/10.2214\/AJR.17.18665 (2018).","journal-title":"AJR Am. J. Roentgenol."},{"key":"1802_CR48","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2020.7798","author":"NRM Schwartz","year":"2021","unstructured":"Schwartz, N. R. M. et al. Potential cost-effectiveness of risk-based pancreatic cancer screening in patients with new-onset diabetes. J. Natl. Compr. Canc. Netw. https:\/\/doi.org\/10.6004\/jnccn.2020.7798 (2021).","journal-title":"J. Natl. Compr. Canc. Netw."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01802-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01802-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01802-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T12:30:26Z","timestamp":1747398626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01802-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,16]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1802"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-01802-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,16]]},"assertion":[{"value":"25 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17096"}}