{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T13:50:09Z","timestamp":1791467409877,"version":"4.3.4"},"reference-count":65,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Prostate"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This paper reports the development and application of an artificial intelligence (AI)\u2013led approach to large volume MRI data curation and image segmentation of periprostatic adipose tissue (PPAT) in prostate cancer (PCa)\u2013free men from the UK Biobank. We report how PPAT was defined, radiomics features of PPAT extracted, and explored their potential to predict a PCa diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>In total, MR images from 31,042 non\u2010PCa men were extracted and made available for autosegmentation. PPAT was defined using tools in 3D Slicer as anterior (Ant PPAT) or posterior (Post PPAT) in relation to Denonvilliers fascia. Dice\u2013S\u00f8rensen coefficients (DICE) were calculated to measure the agreement between the observer's manual segmentations used to train a 3D convolutional neural network with U\u2010Net architecture (nnU\u2010Net). Radiomics features were extracted using PyRadiomics and logistic regression analysis was performed on the Ant and Post PPAT features that were deemed significantly different between men without PCa and those who went on to develop PCa. Statistical analysis was performed in SPSS (version 29.0.0).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 107 features (14 shape, 18 first\u2010order texture and 75 second\u2010order texture) were extracted from auto\u2010segmentations of Ant PPAT and Post PPAT. A case\u2010control radiomics analysis was performed on 297 men who had an MRI scan when cancer\u2010free (but who subsequently went on to be diagnosed with PCa), age\u2010matched (within 1\u2009year) to participants without a PCa diagnosis during the follow\u2010up period. In Ant PPAT there were statistically significant differences in 31 features compared to 4 in Post PPAT between men without PCa and those with prevalent PCa. Stratifying the data by the prevalent PCa group time to diagnosis had a limited effect on these results. Predictive analysis results were not statistically significant for Ant PPAT or Post PPAT, however are comparable with the reported predictive value of other clinically established biomarkers of PCa.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An automated process for data curation and image segmentation was successfully demonstrated. Results suggest that Ant PPAT justifies further investigation as a predictive biomarker for prostate cancer diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pros.70226","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:52:15Z","timestamp":1791463935000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploratory Analysis of Peri\u2010Prostatic Adipose Tissue Radiomics Features and Their Potential to Predict Prostate Cancer Diagnosis: Results From a UK Biobank Study"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4781-1655","authenticated-orcid":false,"given":"Angela","family":"O'Neill","sequence":"first","affiliation":[{"name":"Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin Dublin Ireland"},{"name":"Trinity St. James's Cancer Institute Dublin 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Sciences Bristol Medical School, University of Bristol Bristol UK"},{"name":"MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol Bristol UK"},{"name":"NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol Bristol UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vilmundur","family":"Gu\u00f6nason","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, School of Health Sciences University of Iceland Reykjav\u00edk Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth C.","family":"Travis","sequence":"additional","affiliation":[{"name":"Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7992-7719","authenticated-orcid":false,"given":"Richard 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J. M.","year":"2017","journal-title":"Cancer Research"},{"key":"e_1_2_12_39_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106918","article-title":"Automatic Prostate and Peri\u2010Prostatic Fat Segmentation Based on Pyramid Mechanism Fusion Network for T2\u2010Weighted MRI","volume":"223","author":"Li Y.","year":"2022","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"3","key":"e_1_2_12_40_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e25030","article-title":"A Modified U\u2010Net Convolutional Neural Network for Segmenting Periprostatic Adipose Tissue Based on Contour Feature Learning","volume":"10","author":"Wang G.","year":"2024","journal-title":"Heliyon"},{"issue":"4","key":"e_1_2_12_41_1","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/42.363096","article-title":"Morphometric Analysis of White Matter Lesions in Mr Images: Method and Validation","volume":"13","author":"Zijdenbos A. 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