A deep learning-based radiomic nomogram derived from visceral fat for early prediction of gastrointestinal stromal tumor risk grade

Background Gastrointestinal stromal tumors (GISTs) show substantial heterogeneity and are classified into distinct risk categories requiring different treatment strategies. Therefore, reliable preoperative risk stratification is crucial for treatment planning. Due to its inherent contrast, visceral adipose tissue can be reliably segmented and its volume of interest (VOI) obtained even on non-contrast CT scans. Therefore, this study aimed to develop a deep-learning-based radiomics nomogram (DLRN)