  Magnetic resonance imaging (MRI) has become a crucial tool in the diagnosis
and staging of prostate cancer, owing to its superior tissue contrast. However,
it also creates large volumes of data that must be assessed by trained experts,
a time-consuming and laborious task. This has prompted the development of
machine learning tools for the automation of Prostate cancer (PCa) risk
classification based on multiple MRI modalities (T2W, ADC, and high-b-value
DWI). Understanding and interpreting the predictions made by the models,
however, remains a challenge. We analyze Random Forests (RF) and Support Vector
Machines (SVM), for two complementary datasets, the public Prostate-X dataset,
and an in-house, mostly early-stage PCa dataset to elucidate the contributions
made by first-order statistical features, Haralick texture features, and local
binary patterns to the classification. Using correlation analysis and Shapley
impact scores, we find that many of the features typically used are strongly
correlated, and that the majority of features have negligible impact on the
classification. We identify a small set of features that determine the
classification outcome, which may aid the development of explainable AI
approaches.
