Performance of the Combined Model Based on Both Clinicopathological and CT Texture Features in Predicting Liver Metastasis of High-risk Gastrointestinal Stromal Tumors

Abstract

Objective To investigate the performance of the combined model based on both clinicopathological features and CT texture features in predicting liver metastasis of high-risk gastrointestinal stromal tumors(GISTs). Methods The high-risk GISTs confirmed by pathology from January 2015 to December 2020 were analyzed retrospectively,including 153 cases from the Cancer Hospital of the University of Chinese Academy of Sciences and 51 cases from the Shaoxing Central Hospital.The cases were randomly assigned into a training set(n=142)and a test set(n=62)at a ratio of 7∶3.According to the results of operation or puncture,they were classified into a liver metastasis group(76 cases)and a non-metastasis group(128 cases).ITK-SNAP was employed to delineate the volume of interest of the stromal tumors.Least absolute shrinkage and selection operator(LASSO)was employed to screen out the effective features.Multivariate logistic regression was adopted to construct the models based on clinicopathological features,texture features extracted from CT scans,and the both(combined model),respectively.Receiver operating characteristic(ROC)curve and calibration curve were established to evaluate the predictive performance of the models.The area under the curve(AUC)was compared by Delong test. Results Body mass index(BMI),tumor size,Ki-67,tumor occurrence site,abdominal mass,gastrointestinal bleeding,and CA125 level showed statistical differences between groups(all P<0.05).A total of 107 texture features were extracted from CT images,from which 13 and 7 texture features were selected by LASSO from CT plain scans and CT enhanced scans,respectively.The AUC of the prediction with the training set and the test set respectively was 0.870 and 0.855 for the model based on clinicopathological features,0.918 and 0.836 for the model based on texture features extracted from CT plain scans,0.920 and 0.846 for the model based on texture features extracted from CT enhanced scans,and 0.930 and 0.889 for the combined model based on both clinicopathological features and texture features extracted from CT plain scans.Delong test demonstrated no significant difference in AUC between the models based on the texture features extracted from CT plain scans and CT enhanced scans(P=0.762),whereas the AUC of the combined model was significantly different from that of the clinicopathological feature-based model and texture feature-based model(P=0.001 and P=0.023,respectively). Conclusion Texture features extracted from CT plain scans can predict the liver metastasis of high-risk GISTs,and the model established with clinicopathological features combined with CT texture features has best prediction performance.