Prostate Lesion Detection and Salient Feature Assessment Using Zone-Based Classifiers

TL;DR

This study evaluates machine learning classifiers for prostate lesion detection in mpMRI, identifying the most effective models and salient features for different prostate zones to improve diagnostic accuracy and interpretability.

Contribution

It compares various classifiers and highlights salient features for each prostate zone, enhancing model interpretability and aiding clinical decision-making.

Findings

Ensemble algorithms excel in PZ and TZ zones.

CNNs perform best in the AS zone.

Salient features differ by prostate zone, aiding interpretability.

Abstract

Multi-parametric magnetic resonance imaging (mpMRI) has a growing role in detecting prostate cancer lesions. Thus, it is pertinent that medical professionals who interpret these scans reduce the risk of human error by using computer-aided detection systems. The variety of algorithms used in system implementation, however, has yielded mixed results. Here we investigate the best machine learning classifier for each prostate zone. We also discover salient features to clarify the models' classification rationale. Of the data provided, we gathered and augmented T2 weighted images and apparent diffusion coefficient map images to extract first through third order statistical features as input to machine learning classifiers. For our deep learning classifier, we used a convolutional neural net (CNN) architecture for automatic feature extraction and classification. The interpretability of the CNN results was improved by saliency mapping to understand the classification mechanisms within. Ultimately, we concluded that effective detection of peripheral and anterior fibromuscular stroma (AS) lesions depended more on statistical distribution features, whereas those in the transition zone (TZ) depended more on textural features. Ensemble algorithms worked best for PZ and TZ zones, while CNNs were best in the AS zone. These classifiers can be used to validate a radiologist's predictions and reduce inter-reader variability in patients suspected to have prostate cancer. The salient features reported in this study can also be investigated further to better understand hidden features and biomarkers of prostate lesions with mpMRIs.