Classification of Prostate Cancer Grades and T-Stages Based on Tissue Elasticity Using Medical Image Analysis

In this paper, we study the correlation of tissue (i.e. prostate) elasticity with the spread and aggression of prostate cancers. We describe an improved, in-vivo method that estimates the individualized, relative tissue elasticity parameters directly from medical images. Although elasticity reconstruction, or elastograph, can be used to estimate tissue elasticity, it is less suited for in-vivo measurements or deeply-seated organs like prostate. We develop a non-invasive method to estimate tissue elasticity values based on pairs of medical images, using a finite-element based biomechanical model derived from an initial set of images, local displacements, and an optimization-based framework. We demonstrate the feasibility of a statistically-based multi-class learning method that classifies a clinical T-stage and Gleason score using the patient’s age and relative prostate elasticity values reconstructed from computed tomog-