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While current research has shown the importance of Multi-parametric MRI(mpMRI) in diagnosing prostate cancer (PCa), further investigation is neededfor how to incorporate the specific structures of the mpMRI data, such as theregional heterogeneity and between-voxel correlation within a subject. Thispaper proposes a machine learning-based method for improved voxel-wise PCaclassification by taking into account the unique structures of the data. Wepropose a multi-resolution modeling approach to account for regionalheterogeneity, where base learners trained locally at multiple resolutions arecombined using the super learner, and account for between-voxel correlation byefficient spatial Gaussian kernel smoothing. The method is flexible in that thesuper learner framework allows implementation of any classifier as the baselearner, and can be easily extended to classifying cancer into moresub-categories. We describe detailed classification algorithm for the binaryPCa status, as well as the ordinal clinical significance of PCa for which aweighted likelihood approach is implemented to enhance the detection of theless prevalent cancer categories. We illustrate the advantages of the proposedapproach over conventional modeling and machine learning approaches throughsimulations and application to in vivo data.

Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI