基于放射组学特征的胃肠道间质瘤的分类预测

胃肠道间质瘤(GastroIntestinal Stromal Tumors,GIST)是常见的胃肠道肿瘤,具有非定向分化特征,缺乏特异性,且具有恶性潜能,所以GIST的良恶性诊断是临床较为关注的问题。然而,病理活检及CT检查等临床鉴别手段在研究肿瘤异质性方面存在一定困难。文中提出一种基于CT图像提取大量量化的放射组学特征并利用SVM分类器对GIST良恶性进行分类预测的非侵入式方法。首先,应用放射组学方法对120个患有GIST的病人的CT图像肿瘤区域分别提取4个非纹理特征和43个纹理特征。 然后,应用基于ReliefF的前向选择算法进行特征选择,再用最佳特征子集训练得到的SVM分类器来对GIST良恶性进行分类预测。实验中,共有14个纹理特征入选最佳特征子集,且SVM分类模型对GIST良恶性分类的AUC、准确率、敏感性、特异性在训练集中分别为0.9949,0.9277,0.9537,0.9018；在测试集中分别为0.8524,0.8313,0.8197,0.8420。该方法以放射组学的研究方法建立的模型,为GIST良恶性预测提供了一种非入侵式的检测手段,有望成为一种辅助诊断工具,以提高临床GIST良恶性诊断的准确率。

Prediction of Malignant and Benign Gastrointestinal Stromal Tumors Based on Radiomics Feature

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.