Automatic Prediction of T2/T3 Staging of Rectal Cancer Based on Radiomics and Machine Learning

Abstract

The staging of rectal cancer is very important to determine the treatment plans. This study investigated the relationship between the imaging features and the rectal cancer staging, so that the staging of rectal cancer can be automatically predicted based on the imaging features. A total of 81 patients who underwent with T2 or T3 stage rectal cancer from April 2018 to March 2019 were included. Firstly, tumor was labeled by the radiologist to outline the ROI (region of interest) in the high-resolution MRI images. Then the ROI was segmented by FCNN model and MedicalNet model. Secondly, features of the ROI were extracted by radiomics method. Thirdly, the key features were screened out from large number of features. Finally, a machine learning model was trained to predict rectal cancer stage. Two machine learning tools, back-projected neural network (BPNN) and support vector machine method (SVM) were used for the T2/T3 staging prediction of rectal cancer. The accuracy of our methods was 88.2%∼90.5% in the testing dataset, with a confidence interval of 95%, the sensitivity was 90.8%∼91.2%, the specificity was 85.9%∼87.6%, which were better than the traditional method. The area under the curve (AUC) of the BPNN method was 0.81 ± 0.01, which had better prediction performance than the SVM method (AUC = 0.75 ± 0.03). Some of the radiomics features have a significant relationship with the T2/T3 stage of rectal cancer, so it is possible to effectively predict the T2/T3 stage of rectal cancer using the selected radiomics features and machine learning methods.