Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations

Abstract

Purpose 

Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies.

Methods 

In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue.

Results 

Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization.

Conclusion 

Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.

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