Abstract: Gastrointestinal stromal tumors(GIST) are the most common mesenchymal tumors of the gastrointestinal tract with non-directional differentiation,varying malignancy potential and deficient specificity.Therefore,it is a more concerned issue to diagnosis benign or malignant of GIST.However,it is relatively difficult to use pathological biopsy and CT imaging to study solid tumors heterogeneity.This paper proposed a noninvasive method based on a large number of quantitative radiomics features extracted from CT images and SVM classifier to discriminate benign or malignant of GIST.120 patients with GISTs were enrolled in this retrospective study.Firstly,four non-texture features (shape features) and forty-three texture features were extracted from the tumour region of CT images of each patiant.For the initial feature set,ReliefF and forward selection were executed sequentially to feature selection.Then,SVM classifier was trained by the optimal feature subset for benign or malignant discrimination of GIST.14 texture features were selected for the optimal feature subset from the original feature set.The AUC,accuracy,sensitivity and specificity of the model were 0.9949,0.9277,0.9537 and 0.9018 in the training set,and 0.8524,0.8313,0.8197 and 0.8420 in the test set.The model established by the radiomics method provides a noninvasive detection method for predicting the benign or malignant of GIST,and this mothed maybe as an auxiliary diagnosis tool to improve the accuracy efficiently for malignant and benign discrimination of GIST.

Key words: Feature selection, Gastrointestinal stromal tumors, Radiomics, Support vector machine