Improving Cancer Foci Detection in Prostate Cancer Using Multiparametric MRI/MRS and Machine Learning to Better Manage the Disease
Observational Model: Case-Only, Time Perspective: Prospective
Primary Objective: distinguishing high-grade tumors vs. low-grade tumors and normal prostate
Whether advanced MR imaging techniques can be used to train machine-learning techniques to distinguish high-grade tumors from low-grade tumors and normal prostate. The machine-learning techniques will be trained using histopathology data as the ground truth. To achieve this we will obtain volumetric images of the various tissue attributes (listed below) and match them to histopathology: Vascular permeability (ktrans) using dynamic contrast enhanced MRI (DCE-MRI) Morphological changes captured using T2 and diffusion changes using diffusion weighted MRI (DW-MRI) Metabolic signatures of (choline+creatine)/citrate) or CC/C using magnetic resonance spectroscopic imaging (MRSI) Correlate in vivo imaging findings to ex vivo histopathology using deformable image registration Develop a multiclass support vector machine (SVM) using the set of multi-parametric images as input, and use it predict a score akin to the Gleason score.
Nilesh Mistry, PhD
United States: Institutional Review Board
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