Analysis of Independent and Correlated Imaging Features using Scalar-on-matrix Regression

Oregon Convention Center 

Image features that characterize objects from kidney biopsies may offer insight into disease prognosis as novel biomarkers. For each subject, we construct a matrix of image features that are measured for each object from that subject's biopsy. We proposed the CLUstering Structured laSSO (CLUSSO), a novel scalar-on-matrix regression method that allows for unbalanced numbers of objects across subjects, to predict scalar outcomes from matrices of independent image features. CLUSSO averages images feature values within subgroups of objects as determined by cluster analysis. We showed through simulations that CLUSSO has fewer false positives (FPs) and more true positives for identifying truly predictive features relative to a naive method that averages feature values across all objects. To handle correlated image features, we developed the Random CLUSSO, an extension of CLUSSO that averages estimated feature coefficients across bootstrapped samples with subsampled features. These methods are applied to tubular image features of kidney biopsies from glomerular disease patients to predict kidney function.

Computational pathology

Image features

Scalar-on-matrix regression

Unsupervised clustering

Variable selection 

Section on Statistics in Imaging