Classification of renal tumor histology subtypes based on radiomics features of CT images

Abstract

Objective Accurate preoperative differential diagnosis of fat⁃poor angiomyolipoma (fp⁃AML) and clear cell renal cell carcinoma (ccrcc) is essential for proper treatment planning. In order to increase the accuracy of discrimination of fp⁃AML from ccrcc, we develop a classification model based on radiomics technology. Methods The study retrospectively collected CT images of 18 cases with fp⁃AML and 42 cases with ccrcc from department of radiology, the Second Affiliated Hospital of Suzhou University. Firstly, 430 radiomics features were extracted from CT images. Then, the feature selection was carried by three steps: Pearson’s correlation matrices were calculated to remove redundant features, Welch’s t⁃test was utilized to determine the statistically significant features, and sequential forward floating selection method was used to select the discriminative features. Finally, k⁃nearest neighborhood, random forest, support vector machine and adaboost classifiers were built for classification. Results The model built by SVM classifier achieved the best classification performance, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curves of 91.67%, 88.89%, 92.86%, 84.21%, 95.12%, and 0.9418. Conclusions The proposed model can increase the classification accuracy of discrimination of fp⁃AML from ccrcc, and has great potential in helping radiologists to discriminate fp⁃AML from ccrcc.