{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T08:36:57Z","timestamp":1792571817731,"version":"4.5.0"},"reference-count":41,"publisher":"American Diabetes Association","issue":"11","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.diabetesjournals.org\/journals\/pages\/license"}],"funder":[{"name":"Intramural Research Program, National Institutes of Health Clinical Center","award":["1Z01 CL040004"],"award-info":[{"award-number":["1Z01 CL040004"]}]}],"content-domain":{"domain":["diabetesjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,11,1]]},"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>Intrapancreatic fat deposition is a well-established biomarker for type 2 diabetes mellitus (T2DM), but little is known about the role of pancreatic perivascular adipose tissue (PVAT), fat surrounding blood vessels supplying the pancreas. The objectives of this study were to develop a fully automated pipeline to derive imaging biomarkers of pancreatic PVAT from abdominal computed tomography (CT) scans and evaluate their utility for diagnosing T2DM. Contrast-enhanced CT (CECT) scans (n = 1,350) from the public PANORAMA data set were used to train a three-dimensional deep neural network to segment pancreatic anatomy (parenchyma, vasculature, ducts) and extrapancreatic structures. The model was applied to an internal data set containing 615 CECT scans. Intrapancreatic, extrapancreatic, and PVAT biomarkers were derived from these segmentations. Logistic regression with feature selection identified optimal biomarker subsets for classifying T2DM. All PVAT biomarkers differed significantly between individuals with diabetes and those without. The best multivariate model for T2DM achieved an area under the curve (AUC) of 0.93, sensitivity of 0.73, and specificity of 0.91. Exploratory analyses for incident diabetes (developed within the following 4 years) showed similarly high discrimination (AUC 0.93). Automated CT-derived PVAT biomarkers are associated with existing and future T2DM and may enable opportunistic identification of undiagnosed or high-risk individuals undergoing routine abdominal CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Article Highlights<\/jats:title>\n                    <jats:p>Pancreatic perivascular adipose tissue (PVAT) is a potentially important but understudied fat depot surrounding vessels supplying the pancreas, and its role in type 2 diabetes mellitus (T2DM) remains unclear. We aimed to quantify pancreatic PVAT automatically on routine abdominal computed tomography (CT) scans and assess whether PVAT-derived imaging biomarkers are associated with T2DM and future T2DM risk. PVAT biomarkers were associated with T2DM and were informative predictors in multivariable models. Exploratory analyses showed similar findings for incident T2DM (disease onset within 4 years of follow-up). PVAT may represent a novel CT-based biomarker for early metabolic dysfunction and T2DM risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2337\/db25-1140","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:09:32Z","timestamp":1783606172000},"page":"1968-1979","update-policy":"https:\/\/doi.org\/10.2337\/ada-journal-policies","source":"Crossref","is-referenced-by-count":1,"title":["Utility of Pancreatic Perivascular Adipose Tissue as a CT Imaging Biomarker for Diagnosing Type 2 Diabetes"],"prefix":"10.2337","volume":"75","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1505-2079","authenticated-orcid":false,"given":"Anisa V.","family":"Prasad","sequence":"first","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"},{"name":"Frank H. Netter MD School of Medicine, Quinnipiac University 2 , North Haven, CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tejas Sudharshan","family":"Mathai","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Praveen T.S.","family":"Balamuralikrishna","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivek","family":"Batheja","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinav","family":"Suri","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pritam","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[{"name":"Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center 1 , Bethesda, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1167","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"2026102014520213700_B1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0140-6736(23)01301-6","article-title":"Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021","volume":"402","author":"GBD 2021 Diabetes Collaborators","year":"2023","journal-title":"Lancet"},{"key":"2026102014520213700_B2","unstructured":"Centers for Disease Control and Prevention.\n          \n          National Diabetes Statistics Report. 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