Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors

Abstract

PURPOSE: To build radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict malignant potential and mitotic count of gastrointestinal stromal tumors (GISTs). PATIENTS AND METHODS: A total of 333 GISTs patients were retrospectively included in our study. Radiomic features were extracted from the preoperative CE-CT images. According to postoperative pathology, patients were categorized by malignant potential and mitotic count, respectively. The most valuable radiomic features were chosen to build a logistic regression model to predict the malignant potential and a random forest classifier model to predict the mitotic count. The performance of radiomic models was assessed with the receiver operating characteristics curve. Our study further developed a radiomic nomogram to preoperatively predict malignant potential in a personalized way for patients with GISTs. RESULTS: The predictive model was built to discriminate high– from low–malignant potential GISTs with an area under the curve (AUC) of 0.882 (95% CI 0.823-0.942) in the training set and 0.920 (95% CI 0.870-0.971) in the validation set. Moreover, the other radiomic model was built to differentiate high– from low–mitotic count GISTs with an AUC of 0.820 (95% CI 0.753-0.887) in the training set and 0.769 (95% CI 0.654-0.883) in the validation set. CONCLUSION: The radiomic models using CE-CT showed a good predictive performance for preoperative risk stratification of GISTs and hold great potential for personalized clinical decision making.