A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest

Abstract

Abstract Background Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI). Methods Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). Then texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification. Results The highest classification accuracy was 73.50% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 81.50% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using the SVM model and initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 77.78% using SVM and 85.71% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively. Conclusions The classification results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.