Application of radiomics in predicting the preoperative risk stratification of gastric stromal tumors

PURPOSE: The stomach is the most common site of gastrointestinal stromal tumors. In this study, clinical model, radiomics models, and nomogram were constructed to compare and assess the clinical value of each model in predicting the preoperative risk stratification of gastric stromal tumors (GSTs). METHODS: In total, 180 patients with GSTs confirmed postoperatively pathologically were included. 70% of patients was randomly selected from each category as the training group (n= 126), and the remaining 30% was stratified as the testing group (n = 54). The image features and texture characteristics of each patient were analyzed, and predictive model was constructed. The image features and the rad-score of the optimal radiomics model were used to establish the nomogram. The clinical application value of these models was assessed by the receiver operating characteristic curve and decision curve analysis. The calibration of each model was evaluated by the calibration curve. RESULTS: The area under the curve (AUC) value of the nomogram was 0.930 (95% confidence interval [CI]: 0.886-0.973) in the training group and 0.931 (95% CI: 0.869-0.993) in the testing group. The AUC values of the training group and the testing group calculated by the radiomics model were 0.874 (95% CI: 0.814-0.935) and 0.863 (95% CI: 0.76 5-0.960), respectively; the AUC values calculated by the clinical model were 0.871 (95% CI: 0.811-0.931) and 0.854 (95% CI: 0.76 0-0.947). CONCLUSION: The proposed nomogram can accurately predict the malignant potential of GSTs and can be used as repeatable imaging markers for decision-making to predict the risk stratification of GSTs non-invasively and effectively before surgery.