{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:29:57Z","timestamp":1778632197490,"version":"3.51.4"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec><jats:title>Aim<\/jats:title><jats:p>The current study aims to delineate subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the sacrospinalis muscle, and all abdominal musculature at the L3\u2013L5 vertebral level from non-contrast computed tomography (CT) imagery using deep learning algorithms. Subsequently, radiomic features are collected from these segmented images and subjected to medical interpretation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>This retrospective analysis includes a cohort of 315 patients diagnosed with acute necrotizing pancreatitis (ANP) who had undergone comprehensive whole-abdomen CT scans. The no new net (nnU-Net) architecture was adopted for the imagery segmentation, while Python scripts were employed to derive radiomic features from the segmented non-contrast CT images. In light of the intrinsic medical relevance of specific features, two categories were selected for analysis: first-order statistics and morphological characteristics. A correlation analysis was conducted, and statistically significant features were subjected to medical scrutiny.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>With respect to VAT, skewness (<jats:italic>p<\/jats:italic>\u202f=\u202f0.004) and uniformity (<jats:italic>p<\/jats:italic>\u202f=\u202f0.036) emerged as statistically significant; for SAT, significant features included skewness (<jats:italic>p<\/jats:italic>\u202f=\u202f0.023), maximum two-dimensional (2D) diameter slice (<jats:italic>p<\/jats:italic>\u202f=\u202f0.020), and maximum three-dimensional (3D) diameter (<jats:italic>p<\/jats:italic>\u202f=\u202f0.044); for the abdominal muscles, statistically significant metrics were the interquartile range (IQR; <jats:italic>p<\/jats:italic>\u202f=\u202f0.023), mean absolute deviation (<jats:italic>p<\/jats:italic>\u202f=\u202f0.039), robust mean absolute deviation (<jats:italic>p<\/jats:italic>\u202f=\u202f0.015), elongation (<jats:italic>p<\/jats:italic>\u202f=\u202f0.025), sphericity (<jats:italic>p<\/jats:italic>\u202f=\u202f0.010), and surface volume ratio (<jats:italic>p<\/jats:italic>\u202f=\u202f0.014); and for the sacrospinalis muscle, significant indices comprised the IQR (<jats:italic>p<\/jats:italic>\u202f=\u202f0.018), mean absolute deviation (<jats:italic>p<\/jats:italic>\u202f=\u202f0.049), robust mean absolute deviation (<jats:italic>p<\/jats:italic>\u202f=\u202f0.025), skewness (<jats:italic>p<\/jats:italic>\u202f=\u202f0.008), maximum 2D diameter slice (<jats:italic>p<\/jats:italic>\u202f=\u202f0.008), maximum 3D diameter (<jats:italic>p<\/jats:italic>\u202f=\u202f0.005), sphericity (<jats:italic>p<\/jats:italic>\u202f=\u202f0.011), and surface volume ratio (<jats:italic>p<\/jats:italic>\u202f=\u202f0.005).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Diminished localized deposition of VAT and SAT, homogeneity in the VAT and SAT density, augmented SAT volume, and a dispersed and heterogeneous distribution of abdominal muscle density are identified as risk factors for infectious pancreatic necrosis (IPN).<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmicb.2024.1509915","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T06:22:30Z","timestamp":1734070950000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning"],"prefix":"10.3389","volume":"15","author":[{"given":"Bingyao","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1097\/RCT.0000000000001175","article-title":"The effects of the fat distribution of body, skeletal muscle mass and muscle quality on acute pancreatitis severity: a retrospective cross-sectional study","volume":"45","author":"Akturk","year":"2021","journal-title":"J. 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