Supervised regularized canonical correlation analysis: Integrating histologic and proteomic data for predicting biochemical failures

Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data, called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. In this paper, we present a novel Supervised Regularized Canonical Correlation Analysis (SRCCA) algorithm that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at risk for biochemical recurrence following radical prostatectomy. For a cohort of 19 prostate cancer patients, SRCCA was able to yield a lower fused dimensional metaspace comprising both the histological and proteomic attributes. In conjunction with SRCCA, a random forest classifier was able to identify patients at risk for biochemical failure with a maximum accuracy of 93%. The classifier performance in the SRCCA space was statistically significantly higher compared to the fused data representations obtained either with Canonical Correlation Analysis (CCA) or Regularized CCA.