{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T13:56:52Z","timestamp":1790603812394,"version":"4.1.0"},"reference-count":46,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100020153","name":"Centene Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020153","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["UEG Journal"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high\u2010risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region\u2010of\u2010interest\u2013based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non\u2010HRI controls using an Artificial intelligence (AI)\u2010based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed and validated a deep\u2010learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1\u2010weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non\u2010HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement.\u00a0Linear models adjusting for matching variables were used to assess associations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Model\u2010expert agreement for whole\u2010pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98\u20130.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4];\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.32). PDFF increased with age (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.015), male sex (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), presence of diabetes (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.009), BMI (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001). IPF distribution patterns did not differ by risk group.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An AI\u2010based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ueg2.70279","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T06:35:06Z","timestamp":1787985306000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2010Estimated Intrapancreatic Fat and Its Relationship to Familial and Germline Pancreatic Cancer Risk"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-111X","authenticated-orcid":false,"given":"Tom","family":"Konikoff","sequence":"first","affiliation":[{"name":"Division of Gastroenterology and Hepatology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurens P.","family":"Janssens","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology Mayo Clinic  Rochester Minnesota 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