Classifying Cancer Grades Using Temporal Ultrasound for Transrectal Prostate Biopsy

We propose a cancer grading approach for transrectal ultrasound-guided prostate biopsy based on analysis of temporal ultrasound signals. Histopathological grading of prostate cancer reports the statistics of cancer distribution in a biopsy core. We propose a coarse-to-fine classification approach, similar to histopathology reporting, that uses statistical analysis and deep learning to determine the distribution of aggressive cancer in ultrasound image regions surrounding a biopsy target. Our approach consists of two steps; in the first step, we learn high-level latent features that maximally differentiate benign from cancerous tissue. In the second step, we model the statistical distribution of prostate cancer grades in the space of latent features. In a study with 197 biopsy cores from 132 subjects, our approach can effectively separate clinically significant disease from low-grade tumors and benign tissue. Further, we achieve the area under the curve of 0.8 for separating aggressive cancer from benign tissue in large tumors.