{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T06:48:55Z","timestamp":1747205335202,"version":"3.40.5"},"posted":{"date-parts":[[2024,9,2]]},"group-title":"In Review","reference-count":44,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,6,28]]},"abstract":"<title>Abstract<\/title>\n        <p>Background\n Subcutaneous (SAF) and visceral (VAF) abdominal fat have specific properties which the global body fat and total abdominal fat (TAF) size metrics do not capture. Beyond size, radiomics allows deep tissue phenotyping and may capture fat dysfunction. We aimed to characterize the computed tomography (CT) radiomics of SAF and VAF and assess their incremental value above fat size to detect coronary calcification.\nMethods\n SAF, VAF and TAF area, signal distribution and texture were extracted from non-contrast CT of 1001 subjects (57% male, 57\u2009\u00b1\u200910 years) with no established cardiovascular disease who underwent CT for coronary calcium score (CCS) with additional abdominal slice (L4\/5-S1). XGBoost machine learning models (ML) were used to identify the best features that discriminate SAF from VAF and to train\/test ML to detect any coronary calcification (CCS\u2009&gt;\u20090).\nResults\n SAF and VAF appearance in non-contrast CT differs: SAF displays brighter and finer texture than VAF. Compared with CCS\u2009=\u20090, SAF of CCS\u2009&gt;\u20090 has higher signal and homogeneous texture, while VAF of CCS\u2009&gt;\u20090 has lower signal and heterogeneous texture. SAF signal\/texture improved SAF area performance to detect CCS\u2009&gt;\u20090. A ML including SAF and VAF area performed better than TAF area to discriminate CCS\u2009&gt;\u20090 from CCS\u2009=\u20090, however, a combined ML of the best SAF and VAF features detected CCS\u2009&gt;\u20090 as the best TAF features.\nConclusion\n In non-contrast CT, SAF and VAF appearance differs and SAF radiomics improves the detection of CCS\u2009&gt;\u20090 when added to fat area; TAF radiomics (but not TAF area) spares the need for separate SAF and VAF segmentations.<\/p>","DOI":"10.21203\/rs.3.rs-4654020\/v1","type":"posted-content","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T10:43:38Z","timestamp":1725273818000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Computed Tomography Radiomics of Abdominal Adipose Tissue to Optimize Cardiovascular Risk Assessment"],"prefix":"10.21203","author":[{"given":"Jennifer","family":"Mancio","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University of Porto; Guys and St Thomas NHS Trust Foundation, London"}]},{"given":"Alice","family":"Lopes","sequence":"additional","affiliation":[]},{"given":"In\u00eas","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Fabio","family":"Nunes","sequence":"additional","affiliation":[]},{"given":"Sonia","family":"Xara","sequence":"additional","affiliation":[]},{"given":"M\u00f3nica","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Centro Hospitalar de Vila Nova de Gaia e Espinho"}]},{"given":"Wilson","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Centro Hospitalar de Vila Nova de Gaia e Espinho"}]},{"given":"Nuno","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Centro Hospitalar de Vila Nova de Gaia\/Espinho"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9103-5852","authenticated-orcid":false,"given":"Antonio","family":"Barros","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2306-8393","authenticated-orcid":false,"given":"Ricardo","family":"Fontes-Carvalho","sequence":"additional","affiliation":[{"name":"Faculdade de Medicina Universidade do Porto"}]},{"given":"Vasco Gama","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Centro Hospitalar de Vila Nova de Gaia"}]},{"given":"Nuno","family":"Bettencourt","sequence":"additional","affiliation":[]},{"given":"Joao","family":"Pedrosa","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"issue":"6","key":"ref1","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1161\/ATVBAHA.107.159228","article-title":"Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk","volume":"28","author":"Despr\u00e9s J-P","year":"2008","unstructured":"Despr\u00e9s J-P, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al. 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