A Preliminary Study of Preoperative Prediction of Ki-67 Expression in Gastrointestinal Stromal Tumors Using a Machine Learning Interpretable Model Integrating Enhanced CT Radiomics and Semantic Features
Objective To construct and validate a machine learning SHAP model based on enhanced CT radiomics fea-tures combined with traditional CT features for preoperative prediction of Ki-67 expression status in GIST patients.Meth-ods A retrospective collection of clinical,imaging,and pathological data was performed on 149 GIST patients in our hos-pital.Patients were divided into low expression and high expression groups based on postoperative pathology.Traditional CT features were analyzed from preoperative enhanced CT images,and radiomics features were extracted from the venous phase images.ICCs,MRMR,and LASSO methods were employed to select radiomics features and construct radiomics la-bels.Subsequently,the SVM machine learning algorithm was used to build a model incorporating radiomics features and statistically significant traditional CT features.The predictive performance of the machine learning model for Ki-67 expres-sion in GIST patients was evaluated using ROC curves.The SHAP method was utilized to analyze and investigate the contri-bution and risk threshold of different variables.Results In the training set and validation set,the Radscores for high and low Ki-67 expression in GIST patients were(5.50±8.27)vs(-2.16±5.56)and(2.15±1.71)vs(-3.43±6.90),respectively,with statistically significant differences(P＜0.001).The Radscore had AUCs of 0.749 and 0.729 for predicting Ki-67 expression in GIST patients in the training set and validation set,respectively.The SVM classification model integrating radiomics features and traditional CT features showed AUCs of 0.812 and 0.791 in the training set and validation set,respectively.The SHAP analysis results demonstrated that the Radscore and tumor diameter made significant positive contributions to the model.Personalized feature attribution results indicated that a Radscore＞-0.1175 and tumor diameter＞5.5 cm corresponded to a greater risk prediction ability for high Ki-67 expression in GIST patients.Conclusion An interpretable SVM model based on enhanced CT radiomics features and traditional semantic features can provide individualized preoperative prediction of Ki-67 expression in GIST patients,offering reliable imaging biomarkers for clinical personalized treatment decisions.
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