There is a critical need for new methods for the screening and diagnosis of prostate cancer. Using conventional MRI, around 15 to 30 percent of clinically-significant cancers are missed, even by expert radiologists. The application of computer-aided detection and artificial intelligence tools to multi-parametric MRI shows promise in aiding radiologists in prostate cancer diagnosis, but low specificity and high false positive rates remain a concern. This project will assess whether the combination of deep learning methods and data from hybrid multi-dimensional MRI (HM-MRI) — a non-invasive technique developed by UChicago radiologists that provides tissue composition measures similar to the gold standard of pathology — can improve the diagnostic accuracy of detecting prostate cancer.
Research Assistant Professor, Department of Radiology
Professor, Departments of Radiology and Surgery
Assistant Professor, Computer Science