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

ABSTRACT: 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 f

SUBMITTER: Jin J 

PROVIDER: S-EPMC9930806 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature