  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.
