EMPrAvISE: A Computerized Decision Support System for Automated Prostate Cancer Detection from Multi-Protocol MRI

Satish Viswanath¹, B. Nicolas Bloch², Jonathan Chappelow¹, Pratik Patel¹, Neil Rofsky³, Robert Lenkinski⁴, Elisabeth Genega⁴, Anant Madabhushi¹

We present a novel technique, Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE), for building a computerized meta-classifier to predict the spatial extent of prostate cancer (CaP) in vivo via multi-protocol (T2-weighted, Dynamic Contrast Enhanced, Diffusion-weighted) MRI data. We employ automated registration, quantitative image descriptors, and a novel ensemble representation technique in our methodology. Evaluation of our automated predictions for spatial extent of CaP at a pixel level (against registered extents of CaP on MRI) results in EMPrAvISE showing a statistically significant improvement (AUC=0.73) over individual protocols (T2w, DCE,DWI), as well as simple multi-protocol feature concatenation.