  While current research has shown the importance of Multi-parametric MRI
(mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed
for how to incorporate the specific structures of the mpMRI data, such as the
regional heterogeneity and between-voxel correlation within a subject. This
paper proposes a machine learning-based method for improved voxel-wise PCa
classification by taking into account the unique structures of the data. We
propose a multi-resolution modeling approach to account for regional
heterogeneity, where base learners trained locally at multiple resolutions are
combined using the super learner, and account for between-voxel correlation by
efficient spatial Gaussian kernel smoothing. The method is flexible in that the
super learner framework allows implementation of any classifier as the base
learner, and can be easily extended to classifying cancer into more
sub-categories. We describe detailed classification algorithm for the binary
PCa status, as well as the ordinal clinical significance of PCa for which a
weighted likelihood approach is implemented to enhance the detection of the
less prevalent cancer categories. We illustrate the advantages of the proposed
approach over conventional modeling and machine learning approaches through
simulations and application to in vivo data.
