Abstract Number:
2014
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Jeremy Rubin (1), Fan Fan (2), Laura Barisoni (3), Andrew Janowczyk (4), Jarcy Zee (1)
Institutions:
(1) University of Pennsylvania, N/A, (2) Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, (3) Division of AI and Computational Pathology, Department of Pathology, Duke University, Durham, NC, (4) Department of Biomedical Engineering, Emory University, Atlanta, GA
Co-Author(s):
Fan Fan
Department of Biomedical Engineering, Emory University and Georgia Institute of Technology
Laura Barisoni
Division of AI and Computational Pathology, Department of Pathology, Duke University
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Computational pathology|Image features|Scalar-on-matrix regression|Unsupervised clustering|Variable selection |
Sponsors:
Section on Statistics in Imaging
Tracks:
Imaging
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