{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T14:13:46Z","timestamp":1793110426623,"version":"4.5.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developing new-onset PC at two and a half to three year time frame\n                    <jats:bold>.<\/jats:bold>\n                    We used the Electronic Health Records (EHR) of a large medical system from 2000 to 2021 (N\u2009=\u2009537,410). The EHR data analyzed in this work consists of patients\u2019 demographic information, diagnosis records, and lab values, which are used to identify patients who were diagnosed with pancreatic cancer and the risk factors used in the machine learning algorithm for prediction. We identified 73 risk factors of pancreatic cancer with the Phenome-wide Association Study (PheWAS) on a matched case\u2013control cohort. Based on them, we built a large-scale machine learning algorithm based on EHR. A temporally stratified validation based on patients not included in any stage of the training of the model was performed. This model showed an AUROC at 0.742 [0.727, 0.757] which was similar in both the general population and in a subset of the population who has had prior cross-sectional imaging. The rate of diagnosis of pancreatic cancer in those in the top 1 percentile of the risk score was 6 folds higher than the general population. Our model leverages data extracted from a 6-month window of time in the electronic health record to identify patients at nearly sixfold higher than baseline risk of developing pancreatic cancer 2.5\u20133\u00a0years from evaluation. This approach offers an opportunity to define an enriched population entirely based on static data, where current screening may be recommended.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-89607-8","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:52:36Z","timestamp":1743828756000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms"],"prefix":"10.1038","volume":"15","author":[{"given":"Weicheng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yindalon","family":"Aphinyanaphongs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fay","family":"Kastrinos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diane M.","family":"Simeone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Pochapin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cody","family":"Stender","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narges","family":"Razavian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamas A.","family":"Gonda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"key":"89607_CR1","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1158\/0008-5472.CAN-14-0155","volume":"74","author":"L Rahib","year":"2014","unstructured":"Rahib, L. et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 74, 2913\u20132921 (2014).","journal-title":"Cancer Res."},{"key":"89607_CR2","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s11605-019-04230-z","volume":"24","author":"MI Canto","year":"2020","unstructured":"Canto, M. I. et al. Surgical outcomes after pancreatic resection of screening-detected lesions in individuals at high risk for developing pancreatic cancer. J. Gastrointest. Surg. 24, 1101\u20131110 (2020).","journal-title":"J. Gastrointest. Surg."},{"key":"89607_CR3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1136\/gutjnl-2019-319352","volume":"69","author":"M Goggins","year":"2020","unstructured":"Goggins, M. et al. Management of patients with increased risk for familial pancreatic cancer: updated recommendations from the International Cancer of the Pancreas Screening (CAPS) Consortium. Gut. 69, 7\u201317 (2020).","journal-title":"Gut."},{"key":"89607_CR4","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1053\/j.gastro.2021.08.036","volume":"161","author":"TA Gonda","year":"2021","unstructured":"Gonda, T. A., Everett, J. N., Wallace, M., Simeone, D. M., PRECEDE Consortium. Recommendations for a more organized and effective approach to the early detection of pancreatic cancer from the PRECEDE (Pancreatic Cancer Early Detection) consortium. Gastroenterology. 161, 1751\u20131757 (2021).","journal-title":"Gastroenterology."},{"key":"89607_CR5","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1038\/nature09515","volume":"467","author":"S Yachida","year":"2010","unstructured":"Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature. 467, 1114\u20131117 (2010).","journal-title":"Nature."},{"key":"89607_CR6","doi-asserted-by":"crossref","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 (2015).","journal-title":"Gut."},{"key":"89607_CR7","doi-asserted-by":"crossref","first-page":"4085","DOI":"10.1038\/s41467-020-17917-8","volume":"11","author":"M No\u00eb","year":"2020","unstructured":"No\u00eb, M. et al. Genomic characterization of malignant progression in neoplastic pancreatic cysts. Nat. Commun. 11, 4085 (2020).","journal-title":"Nat. Commun."},{"key":"89607_CR8","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1093\/bioinformatics\/btq126","volume":"26","author":"JC Denny","year":"2010","unstructured":"Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 26, 1205\u20131210 (2010).","journal-title":"Bioinformatics."},{"key":"89607_CR9","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1002\/gepi.20589","volume":"35","author":"SA Pendergrass","year":"2011","unstructured":"Pendergrass, S. A. et al. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet. Epidemiol. 35, 410\u2013422 (2011).","journal-title":"Genet. Epidemiol."},{"key":"89607_CR10","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301\u2013320 (2005).","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"89607_CR11","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41575-021-00457-x","volume":"18","author":"AP Klein","year":"2021","unstructured":"Klein, A. P. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat. Rev. Gastroenterol. Hepatol. 18, 493\u2013502 (2021).","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"89607_CR12","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.canlet.2016.07.022","volume":"381","author":"S Midha","year":"2016","unstructured":"Midha, S., Chawla, S. & Garg, P. K. Modifiable and non-modifiable risk factors for pancreatic cancer: A review. Cancer Lett. 381, 269\u2013277 (2016).","journal-title":"Cancer Lett."},{"key":"89607_CR13","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1001\/jama.2018.6228","volume":"319","author":"C Hu","year":"2018","unstructured":"Hu, C. et al. Association between inherited germline mutations in cancer predisposition genes and risk of pancreatic cancer. JAMA. 319, 2401\u20132409 (2018).","journal-title":"JAMA."},{"key":"89607_CR14","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/gim.2017.85","volume":"20","author":"KG Chaffee","year":"2018","unstructured":"Chaffee, K. G. et al. Prevalence of germ-line mutations in cancer genes among pancreatic cancer patients with a positive family history. Genet. Med. 20, 119\u2013127 (2018).","journal-title":"Genet. Med."},{"key":"89607_CR15","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1001\/jama.2019.10232","volume":"322","author":"DK Owens","year":"2019","unstructured":"US Preventive Services Task Force et al. Screening for pancreatic cancer: US preventive services task force reaffirmation recommendation statement. JAMA. 322, 438\u2013444 (2019).","journal-title":"JAMA."},{"key":"89607_CR16","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.pan.2018.07.009","volume":"18","author":"MLB Peters","year":"2018","unstructured":"Peters, M. L. B. et al. Progression to pancreatic ductal adenocarcinoma from pancreatic intraepithelial neoplasia: Results of a simulation model. Pancreatology. 18, 928\u2013934 (2018).","journal-title":"Pancreatology."},{"key":"89607_CR17","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1016\/j.cgh.2019.01.026","volume":"17","author":"ND Shah","year":"2019","unstructured":"Shah, N. D. et al. Alcohol-related liver disease is rarely detected at early stages compared with liver diseases of other etiologies worldwide. Clin. Gastroenterol. Hepatol. 17, 2320-2329.e12 (2019).","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"89607_CR18","doi-asserted-by":"crossref","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, 730-739.e3 (2018).","journal-title":"Gastroenterology."},{"key":"89607_CR19","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1053\/j.gastro.2020.11.052","volume":"160","author":"JF Fahrmann","year":"2021","unstructured":"Fahrmann, J. F. et al. Lead-time trajectory of CA19-9 as an anchor marker for pancreatic cancer early detection. Gastroenterology. 160, 1373-1383.e6 (2021).","journal-title":"Gastroenterology."},{"key":"89607_CR20","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1056\/NEJMoa1311194","volume":"370","author":"TF Imperiale","year":"2014","unstructured":"Imperiale, T. F. et al. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 370, 1287\u20131297 (2014).","journal-title":"N. Engl. J. Med."},{"key":"89607_CR21","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1001\/jamainternmed.2015.0344","volume":"175","author":"PF Pinsky","year":"2015","unstructured":"Pinsky, P. F. & Schoen, R. E. Colorectal cancer incidence by age among patients undergoing surveillance colonoscopy. JAMA Intern. Med. 175, 858\u2013860 (2015).","journal-title":"JAMA Intern. Med."},{"key":"89607_CR22","doi-asserted-by":"crossref","first-page":"3015","DOI":"10.1007\/s11606-020-05951-7","volume":"35","author":"RM Hoffman","year":"2020","unstructured":"Hoffman, R. M., Atallah, R. P., Struble, R. D. & Badgett, R. G. Lung cancer screening with low-dose CT: a meta-analysis. J. Gen. Intern. Med. 35, 3015\u20133025 (2020).","journal-title":"J. Gen. Intern. Med."},{"issue":"3","key":"89607_CR23","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(3), 730-739.e3. https:\/\/doi.org\/10.1053\/j.gastro.2018.05.023 (2018).","journal-title":"Gastroenterology."},{"issue":"10","key":"89607_CR24","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2020.2948","volume":"6","author":"C Yuan","year":"2020","unstructured":"Yuan, C. et al. Diabetes, weight change, and pancreatic cancer risk. JAMA Oncol. 6(10), e202948. https:\/\/doi.org\/10.1001\/jamaoncol.2020.2948 (2020).","journal-title":"JAMA Oncol."},{"issue":"7","key":"89607_CR25","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41575-021-00457-x","volume":"18","author":"AP Klein","year":"2021","unstructured":"Klein, A. P. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat. Rev. Gastroenterol. Hepatol. 18(7), 493\u2013502. https:\/\/doi.org\/10.1038\/s41575-021-00457-x (2021).","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"issue":"9","key":"89607_CR26","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1038\/ajg.2017.218","volume":"112","author":"J Kirkeg\u00e5rd","year":"2017","unstructured":"Kirkeg\u00e5rd, J., Mortensen, F. V. & Cronin-Fenton, D. Chronic pancreatitis and pancreatic cancer risk: a systematic review and meta-analysis. Am. J. Gastroenterol. 112(9), 1366\u20131372. https:\/\/doi.org\/10.1038\/ajg.2017.218 (2017).","journal-title":"Am. J. Gastroenterol."},{"issue":"1","key":"89607_CR27","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.canlet.2016.07.022","volume":"381","author":"S Midha","year":"2016","unstructured":"Midha, S., Chawla, S. & Garg, P. K. Modifiable and non-modifiable risk factors for pancreatic cancer: A review. Cancer Lett. 381(1), 269\u2013277. https:\/\/doi.org\/10.1016\/j.canlet.2016.07.022 (2016).","journal-title":"Cancer Lett."},{"issue":"5","key":"89607_CR28","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/s41591-023-02332-5","volume":"29","author":"D Placido","year":"2023","unstructured":"Placido, D. et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat. Med. 29(5), 1113\u20131122. https:\/\/doi.org\/10.1038\/s41591-023-02332-5 (2023).","journal-title":"Nat. Med."},{"key":"89607_CR29","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ejca.2020.10.019","volume":"143","author":"L Appelbaum","year":"2021","unstructured":"Appelbaum, L. et al. Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study. Eur. J. Cancer. 143, 19\u201330. https:\/\/doi.org\/10.1016\/j.ejca.2020.10.019 (2021).","journal-title":"Eur. J. Cancer."},{"key":"89607_CR30","first-page":"358","volume":"14","author":"X Li","year":"2020","unstructured":"Li, X. et al. A deep-learning based prediction of pancreatic adenocarcinoma with electronic health records from the state of Maine. Int. J. Med. Health Sci. 14, 358\u2013365 (2020).","journal-title":"Int. J. Med. Health Sci."},{"issue":"6","key":"89607_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0251876","volume":"16","author":"A Malhotra","year":"2021","unstructured":"Malhotra, A., Rachet, B., Bonaventure, A., Pereira, S. P. & Woods, L. M. Can we screen for pancreatic cancer? Identifying a sub-population of patients at high risk of subsequent diagnosis using machine learning techniques applied to primary care data. PLoS One. 16(6), e0251876. https:\/\/doi.org\/10.1371\/journal.pone.0251876 (2021).","journal-title":"PLoS One."},{"issue":"520","key":"89607_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.canlet.2021.06.027","volume":"1","author":"J Cai","year":"2021","unstructured":"Cai, J. et al. Advances in the epidemiology of pancreatic cancer: Trends, risk factors, screening, and prognosis. Cancer Lett. 1(520), 1\u201311. https:\/\/doi.org\/10.1016\/j.canlet.2021.06.027 (2021).","journal-title":"Cancer Lett."},{"issue":"9","key":"89607_CR33","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1001\/archinternmed.2010.63","volume":"170","author":"AA Arslan","year":"2010","unstructured":"Arslan, A. A. et al. Anthropometric measures, body mass index, and pancreatic cancer: a pooled analysis from the Pancreatic Cancer Cohort Consortium (PanScan). Arch. Intern. Med. 170(9), 791\u2013802. https:\/\/doi.org\/10.1001\/archinternmed.2010.63 (2010).","journal-title":"Arch. Intern. Med."},{"issue":"7","key":"89607_CR34","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1053\/j.gastro.2023.02.032","volume":"164","author":"DCF Klatte","year":"2023","unstructured":"Klatte, D. C. F. et al. Surveillance for pancreatic cancer in high-risk individuals leads to improved outcomes: a propensity score-matched analysis. Gastroenterology. 164(7), 1223\u201312314. https:\/\/doi.org\/10.1053\/j.gastro.2023.02.032 (2023).","journal-title":"Gastroenterology."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-89607-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-89607-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-89607-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:41:00Z","timestamp":1744202460000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-89607-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,5]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["89607"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-89607-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.08.03.23293555","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-4999645\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,5]]},"assertion":[{"value":"29 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 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"}},{"value":"IRB exempt.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"11697"}}