title: Semi supervised multi kernel (SeSMiK) graph embedding: identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Rutgers University, USA. pallavit@eden.rutgers.edu
authors.forename: Pallavi
authors.initials: P
authors.lastname: Tiwari
authors.forename: John
authors.initials: J
authors.lastname: Kurhanewicz
authors.forename: Mark
authors.initials: M
authors.lastname: Rosen
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D003936:Diagnosis, Computer-Assisted; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor