Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data

Jin Jin, Lin Zhang, Ethan Leng, Gregory J. Metzger, Joseph S. Koopmeiners

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2021

Bibliographical note

Funding Information:
This work was supported by NCI R01 CA155268, NCI R01 CA241159, NCI P30 CA077598, NIBIB P41 EB027061, and the Assistant Secretary of Defense for Health affairs, through the Prostate Cancer Research Program under Award No. W81XWH‐15‐1‐0478. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense.

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.

Keywords

  • Bayesian hierarchical modeling
  • multi-parametric MRI
  • multiimage spatial modeling
  • nearest neighbor Gaussian process
  • voxel-wise PCa classification

PubMed: MeSH publication types

  • Journal Article

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