Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy

With the wide array of multi scale, multi-modal data now available for disease characterization, the major challenge in integrated disease diagnostics is to able to represent the different data streams in a common framework while overcoming differences in scale and dimensionality. This common knowledge representation framework is an important pre-requisite to develop integrated meta-classifiers for disease classification. In this paper, we present a unified data fusion framework, Semi Supervised Multi Kernel Graph Embedding (SeSMiK-GE). Our method allows for representation of individual data modalities via a combined multi-kernel framework followed by semi- supervised dimensionality reduction, where partial label information is incorporated to embed high dimensional data in a reduced space. In this work we evaluate SeSMiK-GE for distinguishing (a) benign from cancerous (CaP) areas, and (b) aggressive high-grade prostate cancer from indolent low-grade by integrating information from 1.5 Tesla in vivo Magnetic Resonance Imaging (anatomic) and Spectroscopy (metabolic). Comparing SeSMiK-GE with unimodal T2w, MRS classifiers and a previous published non-linear dimensionality reduction driven combination scheme (ScEPTre) yielded classification accuracies of (a) 91.3% (SeSMiK), 66.1% (MRI), 82.6% (MRS) and 86.8% (ScEPTre) for distinguishing benign from CaP regions, and (b) 87.5% (SeSMiK), 79.8% (MRI), 83.7% (MRS) and 83.9% (ScEPTre) for distinguishing high and low grade CaP over a total of 19 multi-modal MRI patient studies.