Pathology Without a Knife: MRI-based Non-invasive Determination of Prostate Cancer Grade with Physics-Informed Deep Learning

Motivation: Millions of prostate biopsies are being ordered each year, a great majority of which yield negative results. A reliable and non-invasive method for detecting prostate cancer grade is critical. Goal(s): To develop a robust and efficient MRI-based non-invasive model to detect the Gleason score of the lesions without the need for a biopsy. Approach: We propose a physics-informed autoencoder that integrates the strengths of model-based and deep learning-based methods, while overcoming their respective weaknesses. Results: Physically-interpretable biomarkers that our model yields correlate strongly with Gleason score, providing important new diagnostic markers, and laying the groundwork for a potential new quantitative MRI method. Impact: The proposed model offers for many potential usages in diagnostic radiology, by presenting a non-invasive method for diagnosing and staging prostate cancer, potentially affecting about a million patients annually by reducing unnecessary biopsies and saving millions in healthcare costs.