Value of CT-Based Texture Analysis in Preoperative Prediction of the Grade of Gastrointestinal Stromal Tumors Compared to Conventional CT Imaging

Background: Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are classified as very low, low, intermediate and high risk. The treatment and prognosis of GISTs vary according to the grade. Objectives: To investigate the capability of computed tomography (CT) -based texture analysis for predicting the grade of GISTs, and compare the findings with a combination model consisting of CT signs and texture parameters. Patients and Methods: For this retrospective study, a total of 168 patients (training group: n = 117; validation group: n = 51) with pathologically proven GISTs were analyzed. Patients were randomly divided into the potential malignant and malignant group. Radiomics signature based on texture features and the combination model consisting of selected CT signs and texture parameters were developed with the least absolute shrinkage and selection operator (Lasso) regression. The prediction performance of models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Calibration was evaluated with the Hosmer- Lemeshow goodness-of-fit test. Results: Totally, 29 texture features and seven CT signs were extracted. Texture features of sphericity, compacity, contrast, and dissimilarity, and CT signs of size, and location were selected to develop the predictive models. The rad- and pre-scores were calculated for the radiomics signature and combination model based on the validation group. Both two models hold great prediction performance with AUCs of 0.897 and 0.959 (P < 0.05), sensitivities of 76.20% and 90.50%, specificities of 90.0% and 93.30%, accuracies of 84.30% and 90.20%, respectively. The combination model performed better. Calibration curves showed no statistically significant differences between the two models (P > 0.05). Conclusion: The prediction models were validated to be valuable for risk grade of GISTs and may provide non-invasive and practical biomarkers for optimizing the treatment strategy and improving the prognosis. In addition, the combination model had more advantages than texture analysis alone.