Development of an Individualized CT-based Radiomics Nomogram for Preoperative Differentiation Between Gastric Stromal Tumors and Gastric Leiomyomas

This protocol describes the development and validation of a computed tomography (CT)-based radiomics nomogram for the noninvasive preoperative differentiation of gastric stromal tumors (GISTs) and gastric leiomyomas (GLMs), two gastric submucosal lesions with distinct therapeutic strategies and prognostic implications. A retrospective cohort of 172 patients with pathologically confirmed GISTs or GLMs who underwent contrast-enhanced CT within 30 days before surgery was analyzed. Patients were randomly assigned to a training cohort (n = 120) and a validation cohort (n = 52). Demographic variables, CT morphological characteristics, and quantitative radiomic features extracted from manually delineated regions of interest were systematically evaluated. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression with cross-validation. The final predictive model incorporated age, tumor location, enhancement pattern, and the radiomic feature NGLDM_Busyness, which reflects intratumoral texture heterogeneity. These variables were integrated to construct an individualized nomogram for clinical use. Model performance was assessed using receiver operating characteristic analysis, calibration curves, and decision curve analysis. The nomogram demonstrated strong discriminative ability, good calibration, and favorable clinical utility in both the training and validation cohorts, supporting its potential value as a noninvasive tool for individualized preoperative diagnosis.