Many men have received unnecessary radical treatments for prostate cancer, due to unreliable methods for assessing risk and metastasis. Dr. Mehdi Moradi, and Dr. Peter Black at the UBC Department of Urologic Sciences, hope to change this. Their latest project, which recently received $100,000 in research funding from CIHR, aims to build a tissue classifier that will enhance the diagnosis and risk assessment of prostate cancer.
By using machine learning and big data methodologies, they will be capturing the unique characteristics of prostate cancer on a molecular level. The parameters that characterize the tissue will be extracted with medical imaging modalities, such as MRI and ultrasound, and will be combined with the molecular signature acquired from whole genome sequencing. The imaging and molecular signature together make for a comprehensive profile of cancerous prostate tissue.