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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.

Team

Aritrick Chatterjee

Research Assistant Professor, Department of Radiology

Aytekin Oto

Professor, Departments of Radiology and Surgery

Michael Maire

Assistant Professor, Computer Science