  Multi-parametric magnetic resonance imaging (mpMRI) plays an increasingly
important role in the diagnosis of prostate cancer. Various computer-aided
detection algorithms have been proposed for automated prostate cancer detection
by combining information from various mpMRI data components. However, there
exist other features of mpMRI, including the spatial correlation between voxels
and between-patient heterogeneity in the mpMRI parameters, that have not been
fully explored in the literature but could potentially improve cancer detection
if leveraged appropriately. This paper proposes novel voxel-wise Bayesian
classifiers for prostate cancer that account for the spatial correlation and
between-patient heterogeneity in mpMRI. Modeling the spatial correlation is
challenging due to the extreme high dimensionality of the data, and we consider
three computationally efficient approaches using Nearest Neighbor Gaussian
Process (NNGP), knot-based reduced-rank approximation, and a conditional
autoregressive (CAR) model, respectively. The between-patient heterogeneity is
accounted for by adding a subject-specific random intercept on the mpMRI
parameter model. Simulation results show that properly modeling the spatial
correlation and between-patient heterogeneity improves classification accuracy.
Application to in vivo data illustrates that classification is improved by
spatial modeling using NNGP and reduced-rank approximation but not the CAR
model, while modeling the between-patient heterogeneity does not further
improve our classifier. Among our proposed models, the NNGP-based model is
recommended considering its robust classification accuracy and high
computational efficiency.
