A hybrid classification model with radiomics and CNN for high and low grading of prostate cancer Gleason score on mp-MRI

Abstract

Prostate cancer is a prevalent malignancy among men, and the precise diagnosis and grading of prostate cancer have become key research areas in contemporary medicine. Magnetic resonance imaging is an ideal non-invasive method for the detection and diagnosis of prostate cancer, offering superior spatial resolution compared with invasive biopsy procedures. We developed a hybrid model based on radiomics and deep learning, which performs binary classification on prostate cancer cases at the Gleason score ≤ 3 + 4 and ≥ 4 + 3 cases by utilizing T2-weighted imaging and the apparent diffusion coefficient. In the radiomics model, we incorporated a combined region of interest that included a 5 mm margin around the tumor and employed a mirror comparison method for feature selection. In the deep learning model, we utilized a convolutional neural network framework enhanced by the CPCB and SE-Net modules to improve the discrimination ability of our model. A retrospective study was conducted on 650 cases from two hospitals, and our hybrid model achieved superior performance in terms of accuracy, recall, and F1 score compared with the individual models, with respective accuracy values of 0.907, 0.944, and 0.918.