Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning

Aim The current study aims to delineate subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the sacrospinalis muscle, and all abdominal musculature at the L3–L5 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. Materials and methods 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. Results With respect to VAT, skewness ( p = 0.004) and uniformity ( p = 0.036) emerged as statistically significant; for SAT, significant features included skewness ( p = 0.023), maximum two-dimensional (2D) diameter slice ( p = 0.020), and maximum three-dimensional (3D) diameter ( p = 0.044); for the abdominal muscles, statistically significant metrics were the interquartile range (IQR; p = 0.023), mean absolute deviation ( p = 0.039), robust mean absolute deviation ( p = 0.015), elongation ( p = 0.025), sphericity ( p = 0.010), and surface volume ratio ( p = 0.014); and for the sacrospinalis muscle, significant indices comprised the IQR ( p = 0.018), mean absolute deviation ( p = 0.049), robust mean absolute deviation ( p = 0.025), skewness ( p = 0.008), maximum 2D diameter slice ( p = 0.008), maximum 3D diameter ( p = 0.005), sphericity ( p = 0.011), and surface volume ratio ( p = 0.005). Conclusion 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).