Pathological Prostate Gleason Score Prediction Using MRI Radiomics and Machine Learning Algorithms

Prostate cancer (PCa) is one of the most critical diseases in all-male groups. Clinical diagnosis and follow-up is commonly performed using MRI, which proved to have good specificity and sensitivity for identifying prostate cancer. The pathologic grade or PCa aggressivity is calculated by the Gleason score as a scale. Gleason patterns are numbered in rising cellular disorder and misfortune of standard glandular design from 1 to 5. As of lately, in addition to conventional parameters extracted from MRI, there has been noticeable progress in the extraction of high-throughput quantitative features from medical images, called radiomics. In this work, we enrolled 140 patients with prostate cancer. The whole prostate was segmented manually to extract 51 radiomic features, including shape-based, histogram based, and texture-base features. Data were randomly split into 70% for train and 30% as test datasets, respectively. Z-score normalization was applied to features, and after normalization of features, these were fed to different feature selection and classifiers. We demonstrated the feasibility of differentiation of the Gleason score (3+4 from 4+3) using MRI radiomics and machine learning algorithms.