Abstract:

T staging plays an important role in the preoperative evaluation of rectal cancer. However, the traditional stage judging method directly based on the patients' MRI images is not effective. In this paper, we proposed to predict the T-stage of rectal cancer by using radiomics. First, the imaging data of 105 patients with rectal cancer were obtained, the patients in T1 and T2 stages were classified as non-breakthrough muscular layer group (31 cases), and the patients in T3 and T4 stages were classified as breakthrough muscular layer group (74 cases). In the axial T2WI image of patients, the region of interest (ROI) was segmented, and the radiomics features were extracted using the pyradiomics toolkit. The high-dimensional features were selected using least absolute shrinkage and selection operator (LASSO), and the feature data highly related to T stage were obtained. Four machine learning methods including logistic regression, support vector machine (SVM), gradient boosting decision tree (GBDT) and random forest were used in modeling respectively. Cross validation was performed to evaluate the performance of each model. 100 dimensional features were extracted from each image layer, and 7 features highly related to T stage were obtained after lasso feature selection. Among the four machine learning methods, SVM performed best. The average area under curve (AUC), accuracy, sensitivity and specificity of SVM method were 0.968 5, 0.886 4, 0.962 5 and 0.899 2 respectively, and the accuracy of verification set reached about 0.904 7. The result proved that radiomics can greatly improve the accuracy of T-stage prediction of rectal cancer.

Key words: magnetic resonance imaging, rectal cancer staging, machine learning, support vector machine (SVM)