Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI

Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise...

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Bibliographic Details
Published in:Journal of applied statistics 2023-02, Vol.50 (3), p.805-826
Main Authors: Jin, Jin, Zhang, Lin, Leng, Ethan, Metzger, Gregory J., Koopmeiners, Joseph S.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
Clinical significance
Heterogeneity
Machine learning
Multi-parametric MRI
multi-resolution modeling
ordinal clinical significance of PCa
Prostate cancer
Regional development
Spatial data
Statistical methods
super learner
voxel-wise PCa classification
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Summary:Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2021.2017411