Transitional Zone Prostate Cancer: Performance of Texture-based Machine Learning and Image-based Deep Learning

This study is aimed to explore the performance of texture-based machine learning and image-based deep-learning for enhancing detection of Transitional-zone prostate cancer (TZPCa) in the background of benign prostatic hyperplasia (BPH), using a one-to-one correlation between prostatectomy-based pathologically proven lesion and MRI. Seventy patients confirmed as TZPCa and twenty-nine patients confirmed as BPH without TZPCa by radical prostatectomy. For texture analysis, a radiologist drew the region of interest (ROI) for the pathologically correlated TZPCa and the surrounding BPH on T2WI. Significant features were selected using Least Absolute Shrinkage and Selection Operator (LASSO), trained by 3 types of machine learning algorithms (logistic regression [LR], support vector machine [SVM], and random forest [RF]) and validated by the leave-one-out method. For image-based machine learning, both TZPCa and BPH without TZPCa images were trained using convolutional neural network (CNN) and underwent 10-fold cross validation. Sensitivity, specificity, positive and negative predictive values were presented for each method. The diagnostic performances presented and compared using an ROC curve and AUC value. All the 3 Texture-based machine learning algorithms showed similar AUC (0.854-0.861)among them with generally high specificity (0.710-0.775). The Image-based deep learning showed high sensitivity (0.946) with good AUC (0.802) and moderate specificity (0.643). Texture -based machine learning can be expected to serve as a support tool for diagnosis of human-suspected TZ lesions with high AUC values. Image-based deep learning could serve as a screening tool for detecting suspicious TZ lesions in the context of clinically suspected TZPCa, on the basis of the high sensitivity.