{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:29:58Z","timestamp":1790886598679,"version":"4.1.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01607-2","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T14:03:05Z","timestamp":1752847385000},"page":"1407-1421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis"],"prefix":"10.1007","volume":"39","author":[{"given":"Hamid","family":"Harandi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Gouravani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sogol","family":"Alikarami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Shahrabi Farahani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Ghavam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soheil","family":"Mohammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Amin","family":"Salehi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Reynolds","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Dehghani Firouzabadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-2711","authenticated-orcid":false,"given":"Fahimul","family":"Huda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"1607_CR1","doi-asserted-by":"crossref","unstructured":"Ushio J, Kanno A, Ikeda E, et al.: Pancreatic ductal adenocarcinoma: epidemiology and risk factors. Diagnostics (Basel) 11:562, 2021.","DOI":"10.3390\/diagnostics11030562"},{"key":"1607_CR2","doi-asserted-by":"crossref","unstructured":"Bortesi L, Pesci A, Bogina G, Castelli P, Zamboni G: Ductal adenocarcinoma of the pancreas. Surg Pathol Clin 4:487\u2013521, 2011.","DOI":"10.1016\/j.path.2011.03.007"},{"key":"1607_CR3","doi-asserted-by":"crossref","unstructured":"Latenstein AEJ, van der Geest LGM, Bonsing BA, et al: Nationwide trends in incidence, treatment and survival of pancreatic ductal adenocarcinoma. Eur J Cancer 125:83\u201393, 2020.","DOI":"10.1016\/j.ejca.2019.11.002"},{"key":"1607_CR4","doi-asserted-by":"crossref","unstructured":"Conroy T, Pfeiffer P, Vilgrain V, et al: Pancreatic cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 34:987\u20131002, 2023.","DOI":"10.1016\/j.annonc.2023.08.009"},{"key":"1607_CR5","doi-asserted-by":"crossref","unstructured":"Toft J, Hadden WJ, Laurence JM, et al: Imaging modalities in the diagnosis of pancreatic adenocarcinoma: a systematic review and meta-analysis of sensitivity, specificity and diagnostic accuracy. Eur J Radiol 92:17\u201323, 2017.","DOI":"10.1016\/j.ejrad.2017.04.009"},{"key":"1607_CR6","doi-asserted-by":"crossref","unstructured":"Toshima F, Watanabe R, Inoue D, 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 217:1353\u20131364, 2021.","DOI":"10.2214\/AJR.21.26014"},{"key":"1607_CR7","doi-asserted-by":"crossref","unstructured":"Elsherif SB, Virarkar M, Javadi S, Ibarra-Rovira JJ, Tamm EP, Bhosale PR: Pancreatitis and PDAC: association and differentiation. Abdom Radiol (NY) 45:1324\u20131337, 2020.","DOI":"10.1007\/s00261-019-02292-w"},{"key":"1607_CR8","doi-asserted-by":"crossref","unstructured":"Park S, Chu LC, Hruban RH, et al: Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging 101:555\u2013564, 2020.","DOI":"10.1016\/j.diii.2020.03.002"},{"key":"1607_CR9","doi-asserted-by":"crossref","unstructured":"Lv P, Mahyoub R, Lin X, Chen K, Chai W, Xie J: Differentiating pancreatic ductal adenocarcinoma from pancreatic serous cystadenoma, mucinous cystadenoma, and a pseudocyst with detailed analysis of cystic features on CT scans: a preliminary study. Korean J Radiol 12:187\u2013195, 2011.","DOI":"10.3348\/kjr.2011.12.2.187"},{"key":"1607_CR10","doi-asserted-by":"crossref","unstructured":"Gandhi NS, Feldman MK, Le O, Morris-Stiff G: Imaging mimics of pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 43:273\u2013284, 2018.","DOI":"10.1007\/s00261-017-1330-1"},{"key":"1607_CR11","doi-asserted-by":"crossref","unstructured":"Kang JD, Clarke SE, Costa AF: Factors associated with missed and misinterpreted cases of pancreatic ductal adenocarcinoma. Eur Radiol 31:2422\u20132432, 2021.","DOI":"10.1007\/s00330-020-07307-5"},{"key":"1607_CR12","doi-asserted-by":"crossref","unstructured":"Kang J, Clarke SE, Abdolell M, Ramjeesingh R, Payne J, Costa AF: The implications of missed or misinterpreted cases of pancreatic ductal adenocarcinoma on imaging: a multi-centered population-based study. Eur Radiol 31:212\u2013221, 2021.","DOI":"10.1007\/s00330-020-07120-0"},{"key":"1607_CR13","doi-asserted-by":"crossref","unstructured":"Huang B, Huang H, Zhang S, et al: Artificial intelligence in pancreatic cancer. Theranostics 12:6931\u20136954, 2022.","DOI":"10.7150\/thno.77949"},{"key":"1607_CR14","doi-asserted-by":"crossref","unstructured":"Mukherjee S, Patra A, Khasawneh H, 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.e3, 2022.","DOI":"10.1053\/j.gastro.2022.06.066"},{"key":"1607_CR15","doi-asserted-by":"crossref","unstructured":"Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221\u201348.","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"1607_CR16","doi-asserted-by":"crossref","unstructured":"Kooi T, Litjens G, van Ginneken B, et al: Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303\u2013312, 2017.","DOI":"10.1016\/j.media.2016.07.007"},{"key":"1607_CR17","doi-asserted-by":"crossref","unstructured":"Shi Y, Tang H, Baine MJ, et al: 3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer. Cancers (Basel) 15:5496, 2023.","DOI":"10.3390\/cancers15235496"},{"key":"1607_CR18","doi-asserted-by":"crossref","unstructured":"Zhang SM, Wang YJ, Zhang ST: Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: a systematic review and meta-analysis. J Dig Dis 22:318\u2013328, 2021.","DOI":"10.1111\/1751-2980.12992"},{"key":"1607_CR19","doi-asserted-by":"crossref","unstructured":"Guidozzi N, Menon N, Chidambaram S, Markar SR: The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis. Dis Esophagus 36:1\u201312, 2023.","DOI":"10.1093\/dote\/doad048"},{"key":"1607_CR20","doi-asserted-by":"crossref","unstructured":"Salehi MA, Harandi H, Mohammadi S, Shahrabi Farahani M, Shojaei S, Saleh RR: Diagnostic performance of artificial intelligence in detection of hepatocellular carcinoma: a meta-analysis. J Imaging Inform Med 37:1297\u20131311, 2024.","DOI":"10.1007\/s10278-024-01058-1"},{"key":"1607_CR21","doi-asserted-by":"crossref","unstructured":"Thong LT, Chou HS, Chew HSJ, Lau Y: Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: a systematic review and meta-analysis. Lung Cancer 176:4\u201313, 2023.","DOI":"10.1016\/j.lungcan.2022.12.002"},{"key":"1607_CR22","doi-asserted-by":"crossref","unstructured":"Liu J, Lei J, Ou Y, et al: Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis. Clin Exp Med 23:2341\u20132356, 2022.","DOI":"10.1007\/s10238-022-00895-0"},{"key":"1607_CR23","doi-asserted-by":"crossref","unstructured":"Rokhshad R, Salehi SN, Yavari A, et al: Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis. Oral Radiol 40:1\u201320, 2024.","DOI":"10.1007\/s11282-023-00715-5"},{"key":"1607_CR24","doi-asserted-by":"crossref","unstructured":"Dhali A, Kipkorir V, Srichawla BS, et al: Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space occupying lesion: a systematic review and meta-analysis. Int J Surg 2023.","DOI":"10.1097\/JS9.0000000000000717"},{"key":"1607_CR25","doi-asserted-by":"crossref","unstructured":"Liberati A, Altman DG, Tetzlaff J, et al: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 6:e1000100, 2009.","DOI":"10.1371\/journal.pmed.1000100"},{"key":"1607_CR26","doi-asserted-by":"crossref","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KGM: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med 13:1\u201310, 2015.","DOI":"10.1186\/s12916-014-0241-z"},{"key":"1607_CR27","doi-asserted-by":"crossref","unstructured":"Wolff RF, Moons KGM, Riley RD, et al: PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170:51\u201358, 2019.","DOI":"10.7326\/M18-1376"},{"key":"1607_CR28","doi-asserted-by":"crossref","unstructured":"Nyaga VN, Arbyn M: Metadta: a Stata command for meta-analysis and meta-regression of diagnostic test accuracy data \u2013 a tutorial. Arch Public Health 80:1\u201315, 2022.","DOI":"10.1186\/s13690-021-00747-5"},{"key":"1607_CR29","doi-asserted-by":"crossref","unstructured":"Ren S, Zhao R, Zhang J, et al: Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 45:1524\u20131533, 2020.","DOI":"10.1007\/s00261-020-02506-6"},{"key":"1607_CR30","doi-asserted-by":"crossref","unstructured":"Wei W, Jia G, Wu Z, et al. A multidomain fusion model of radiomics and deep learning to discriminate between pancreatic ductal adenocarcinoma and autoimmune pancreatitis based on 18F-FDG PET\/CT images. Jpn J Radiol 41:417\u2013427, 2023.","DOI":"10.1007\/s11604-022-01363-1"},{"key":"1607_CR31","doi-asserted-by":"crossref","unstructured":"Chu LC, Solmaz B, Park S, et al. Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls. Abdom Radiol (NY) 45:2469\u20132475, 2020.","DOI":"10.1007\/s00261-020-02556-w"},{"key":"1607_CR32","doi-asserted-by":"crossref","unstructured":"Chu LC, Park S, Kawamoto S, et al. Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue. AJR Am J Roentgenol 213:349\u2013357, 2019.","DOI":"10.2214\/AJR.18.20901"},{"key":"1607_CR33","doi-asserted-by":"crossref","unstructured":"Ma H, Liu ZX, Zhang JJ, et al. Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis. World J Gastroenterol 26:5156\u20135168, 2020.","DOI":"10.3748\/wjg.v26.i34.5156"},{"key":"1607_CR34","doi-asserted-by":"crossref","unstructured":"Li J, Liu F, Fang X, et al. CT radiomics features in differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma: a propensity score analysis. Acad Radiol 29:358\u2013366, 2022.","DOI":"10.1016\/j.acra.2021.04.014"},{"key":"1607_CR35","doi-asserted-by":"crossref","unstructured":"Ziegelmayer S, Kaissis G, Harder F, et al. Deep convolutional neural network-assisted feature extraction for diagnostic discrimination and feature visualization in pancreatic ductal adenocarcinoma versus autoimmune pancreatitis. J Clin Med 9:1\u20138, 2020.","DOI":"10.3390\/jcm9124013"},{"key":"1607_CR36","doi-asserted-by":"crossref","unstructured":"He M, Liu Z, Lin Y, et al. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT-based radiomics. Eur J Radiol 117:102\u2013111, 2019.","DOI":"10.1016\/j.ejrad.2019.05.024"},{"key":"1607_CR37","doi-asserted-by":"crossref","unstructured":"Chu LC, Park S, Kawamoto S, et al. Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience. J Am Coll Radiol 16:1338\u20131342, 2019.","DOI":"10.1016\/j.jacr.2019.05.034"},{"key":"1607_CR38","doi-asserted-by":"crossref","unstructured":"Qiu JJ, Yin J, Qian W, et al. A novel multiresolution-statistical texture analysis architecture: radiomics-aided diagnosis of pancreatic ductal adenocarcinoma based on plain CT images. IEEE Trans Med Imaging 40:12\u201325, 2021.","DOI":"10.1109\/TMI.2020.3021254"},{"key":"1607_CR39","doi-asserted-by":"crossref","unstructured":"Alves N, Schuurmans M, Litjens G, Bosma JS, Hermans J, Huisman H: Fully automatic deep learning framework for pancreatic ductal adenocarcinoma detection on computed tomography. Cancers (Basel) 14:1\u201314, 2022.","DOI":"10.3390\/cancers14020376"},{"key":"1607_CR40","doi-asserted-by":"crossref","unstructured":"Korfiatis P, Suman G, Patnam NG, et al. Automated artificial intelligence model trained on a large data set can detect pancreas cancer on diagnostic computed tomography scans as well as visually occult preinvasive cancer on prediagnostic computed tomography scans. Gastroenterology 165:1533\u20131546.e4, 2023.","DOI":"10.1053\/j.gastro.2023.08.034"},{"key":"1607_CR41","doi-asserted-by":"crossref","unstructured":"Anai K, Hayashida Y, Ueda I, et al. The effect of CT texture-based analysis using machine learning approaches on radiologists\u2019 performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol 40:1156\u20131165, 2022.","DOI":"10.1007\/s11604-022-01298-7"},{"key":"1607_CR42","doi-asserted-by":"crossref","unstructured":"Linning E, Xu Y, Wu Z, et al. Differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma using radiomics based on multiphasic computed tomography. J Comput Assist Tomogr 44:511\u2013518, 2020.","DOI":"10.1097\/RCT.0000000000001049"},{"key":"1607_CR43","doi-asserted-by":"crossref","unstructured":"Si K, Xue Y, Yu X, et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors. Theranostics 11:1982\u20131990, 2021.","DOI":"10.7150\/thno.52508"},{"key":"1607_CR44","unstructured":"Liu F, Xie L, Xia Y, Fishman E, Yuille A. Joint shape representation and classification for detecting pancreatic ductal adenocarcinoma. In: Proceedings of the 2019 International Conference on Medical Image Computing and Computer-Assisted Intervention. p. 212\u2013220."},{"key":"1607_CR45","doi-asserted-by":"crossref","unstructured":"Zhang T, Xiang Y, Wang H, et al. Radiomics combined with multiple machine learning algorithms in differentiating pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumor: more hands produce a stronger flame. J Clin Med 11, 2022.","DOI":"10.3390\/jcm11226789"},{"key":"1607_CR46","doi-asserted-by":"crossref","unstructured":"Cao K, Xia Y, Yao J, et al. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med 29:3033\u20133043, 2023.","DOI":"10.1038\/s41591-023-02640-w"},{"key":"1607_CR47","doi-asserted-by":"crossref","unstructured":"Chen PT, Wu T, Wang P, et al. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study. Radiology 306:172\u2013182, 2023.","DOI":"10.1148\/radiol.220152"},{"key":"1607_CR48","doi-asserted-by":"crossref","unstructured":"Chen PT, Chang D, Yen H, et al. Radiomic features at CT can distinguish pancreatic cancer from noncancerous pancreas. Radiol Imaging Cancer 3:e210010, 2021.","DOI":"10.1148\/rycan.2021210010"},{"key":"1607_CR49","doi-asserted-by":"crossref","unstructured":"Zavals\u0131z MT, Alhajj S, Sailunaz K, Ozyer T, Alhajj R. A comparative study of different pre-trained deep learning models and custom CNN for pancreatic tumor detection. Int Arab J Inf Technol 20(3A):515\u2013526, 2023.","DOI":"10.34028\/iajit\/20\/3A\/9"},{"key":"1607_CR50","doi-asserted-by":"crossref","unstructured":"Ye Y, Zhang J, Song P, et al. Clinical features and computed tomography radiomics-based model for predicting pancreatic ductal adenocarcinoma and focal mass-forming pancreatitis. Technol Cancer Res Treat 22, 2023;22:15330338231180792.","DOI":"10.1177\/15330338231180792"},{"key":"1607_CR51","doi-asserted-by":"crossref","unstructured":"Lu J, Jiang N, Zhang Y, Li D. A CT-based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Front Oncol 13, 2023.","DOI":"10.3389\/fonc.2023.979437"},{"key":"1607_CR52","doi-asserted-by":"crossref","unstructured":"Grossberg AJ, Chu LC, Deig CR, et al. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin 70:375\u2013403, 2020.","DOI":"10.3322\/caac.21626"},{"key":"1607_CR53","doi-asserted-by":"crossref","unstructured":"Yan Q, Chen Y, Liu C, et al. Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis. Front Oncol 14, 2024.","DOI":"10.3389\/fonc.2024.1332387"},{"key":"1607_CR54","doi-asserted-by":"crossref","unstructured":"Kenner B, Chari ST, Kelsen D, et al. Artificial intelligence and early detection of pancreatic cancer. Pancreas 50:251\u2013279, 2021.","DOI":"10.1097\/MPA.0000000000001762"},{"key":"1607_CR55","doi-asserted-by":"crossref","unstructured":"Liao WC. Early detection of pancreatic cancer: opportunities provided by cancer-induced paraneoplastic phenomena and artificial intelligence. J Cancer Res Pract 10:129\u2013133, 2023.","DOI":"10.4103\/ejcrp.eJCRP-D-23-00002"},{"key":"1607_CR56","doi-asserted-by":"crossref","unstructured":"Qureshi TA, Gaddam S, Wachsman AM, et al. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images. Cancer Biomark 33:211\u2013217, 2022.","DOI":"10.3233\/CBM-210273"},{"key":"1607_CR57","doi-asserted-by":"publisher","unstructured":"Li J, Shi Z, Liu F, et al. XGBoost classifier based on computed tomography radiomics for prediction of tumor-infiltrating CD8+ T-cells in patients with pancreatic ductal adenocarcinoma. Front Oncol 2021. https:\/\/doi.org\/10.3389\/fonc.2021.671333","DOI":"10.3389\/fonc.2021.671333"},{"key":"1607_CR58","doi-asserted-by":"crossref","unstructured":"Javed S, Qureshi TA, Gaddam S, et al (2022) Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images. Front Oncol 12:1007990","DOI":"10.3389\/fonc.2022.1007990"},{"key":"1607_CR59","doi-asserted-by":"crossref","unstructured":"Hameed BS, Krishnan UM. Artificial intelligence-driven diagnosis of pancreatic cancer. Cancers (Basel) 14:5382, 2022.","DOI":"10.3390\/cancers14215382"},{"key":"1607_CR60","doi-asserted-by":"crossref","unstructured":"Abi Nader C, Vetil R, Wood LK, et al. Automatic detection of pancreatic lesions and main pancreatic duct dilatation on portal venous CT scans using deep learning. Invest Radiol, 2023.","DOI":"10.1097\/RLI.0000000000000992"},{"key":"1607_CR61","doi-asserted-by":"crossref","unstructured":"Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 20:405, 2019.","DOI":"10.3348\/kjr.2019.0025"},{"key":"1607_CR62","doi-asserted-by":"crossref","unstructured":"Vargas R, Nino-Murcia M, Trueblood W, Jeffrey RB (2004) MDCT in pancreatic adenocarcinoma: prediction of vascular invasion and resectability using a multiphasic technique with curved planar reformations. AJR Am J Roentgenol 182:419\u2013425","DOI":"10.2214\/ajr.182.2.1820419"},{"key":"1607_CR63","doi-asserted-by":"crossref","unstructured":"Pickhardt PJ. Value-added opportunistic CT screening: state of the art. Radiology 303:241\u2013254, 2022.","DOI":"10.1148\/radiol.211561"},{"key":"1607_CR64","doi-asserted-by":"crossref","unstructured":"Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A: A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 65:545-563, 2021.","DOI":"10.1111\/1754-9485.13261"},{"key":"1607_CR65","doi-asserted-by":"crossref","unstructured":"Suman G, Patra A, Mukherjee S, Korfiatis P, Goenka AH: Radiomics for Detection of Pancreas Adenocarcinoma on CT Scans: Impact of Biliary Stents. Radiol Imaging Cancer 4:e210081, 2022.","DOI":"10.1148\/rycan.210081"},{"key":"1607_CR66","doi-asserted-by":"crossref","unstructured":"Suman G, Patra A, Korfiatis P, Majumder S, Chari ST, Truty MJ, Fletcher JG, Goenka AH: Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications. Pancreatology 21:1001-1008, 2021.","DOI":"10.1016\/j.pan.2021.03.016"},{"key":"1607_CR67","doi-asserted-by":"crossref","unstructured":"Mohammadi S, Salehi MA, Jahanshahi A, Shahrabi Farahani M, Zakavi SS, Behrouzieh S, Gouravani M, Guermazi A: Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis. Osteoarthritis Cartilage 32:241-253, 2024.","DOI":"10.1016\/j.joca.2023.09.011"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01607-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01607-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01607-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:22:13Z","timestamp":1776874933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01607-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":67,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["1607"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01607-2","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,18]]},"assertion":[{"value":"21 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}