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Proceedings Paper

Supervised multi-view canonical correlation analysis: fused multimodal prediction of disease diagnosis and prognosis
Author(s): Asha Singanamalli; Haibo Wang; George Lee; Natalie Shih; Mark Rosen; Stephen Master; John Tomaszewski; Michael Feldman; Anant Madabhushi
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Paper Abstract

While the plethora of information from multiple imaging and non-imaging data streams presents an opportunity for discovery of fused multimodal, multiscale biomarkers, they also introduce multiple independent sources of noise that hinder their collective utility. The goal of this work is to create fused predictors of disease diagnosis and prognosis by combining multiple data streams, which we hypothesize will provide improved performance as compared to predictors from individual data streams. To achieve this goal, we introduce supervised multiview canonical correlation analysis (sMVCCA), a novel data fusion method that attempts to find a common representation for multiscale, multimodal data where class separation is maximized while noise is minimized. In doing so, sMVCCA assumes that the different sources of information are complementary and thereby act synergistically when combined. Although this method can be applied to any number of modalities and to any disease domain, we demonstrate its utility using three datasets. We fuse (i) 1.5 Tesla (T) magnetic resonance imaging (MRI) features with cerbrospinal fluid (CSF) proteomic measurements for early diagnosis of Alzheimer’s disease (n = 30), (ii) 3T Dynamic Contrast Enhanced (DCE) MRI and T2w MRI for in vivo prediction of prostate cancer grade on a per slice basis (n = 33) and (iii) quantitative histomorphometric features of glands and proteomic measurements from mass spectrometry for prediction of 5 year biochemical recurrence postradical prostatectomy (n = 40). Random Forest classifier applied to the sMVCCA fused subspace, as compared to that of MVCCA, PCA and LDA, yielded the highest classification AUC of 0.82 +/- 0.05, 0.76 +/- 0.01, 0.70 +/- 0.07, respectively for the aforementioned datasets. In addition, sMVCCA fused subspace provided 13.6%, 7.6% and 15.3% increase in AUC as compared with that of the best performing individual view in each of the three datasets, respectively. For the biochemical recurrence dataset, Kaplan-Meier curves generated from classifier prediction in the fused subspace reached the significance threshold (p = 0.05) for distinguishing between patients with and without 5 year biochemical recurrence, unlike those generated from classifier predictions of the individual modalities.

Paper Details

Date Published: 13 March 2014
PDF: 13 pages
Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903805 (13 March 2014); doi: 10.1117/12.2043762
Show Author Affiliations
Asha Singanamalli, Case Western Reserve Univ. (United States)
Haibo Wang, Case Western Reserve Univ. (United States)
George Lee, Case Western Reserve Univ. (United States)
Natalie Shih, Univ. of Pennsylvania (United States)
Mark Rosen, Univ. of Pennsylvania (United States)
Stephen Master, Univ. of Pennsylvania (United States)
John Tomaszewski, Univ. at Buffalo (United States)
Michael Feldman, Univ. of Pennsylvania (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 9038:
Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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