Abstract #1462

Weakly Supervised Learning for Prostate Cancer Detection from Co-registered bi-parametric MRI and Patient’s Clinal Data

Fatemeh Zabihollahy^1,2, Emerson Paul Grabke^1,2,3,4, and Masoom A. Haider^1,2

¹Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada, ²Lunenfeld-Tanenbaum Research Institute, Sinai Health System, University of Toronto, Toronto, ON, Canada, ³KITE Research Institute, University Health Network and University of Toronto, Toronto, ON, Canada, ⁴Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Cancer, Prostate, Image Registration

Motivation: Bi-parametric MRI (bpMRI) is now part of the diagnostic workup for prostate cancer (PCa). Radiologists cognitively coregister bpMRI sequences when interpreting MRI. Conversely, machine learning (ML) algorithms have difficulty learning this implicit coregistration because of the distortion often present in diffusion-weighted images.

Goal(s): Introduce a novel method for automated 1) bpMRI coregistration; and 2) detection of csPCa.

Approach: A weakly supervised learning paradigm was employed for bpMRI co-registration. A combination of co-registered bpMRI and the patient’s clinical data was used for automated csPCa detection.

Results: The proposed method achieved a true and false positive rate of 86% and 41% on 100 test cases.

Impact: The obtained results demonstrated the value of co-registration and including patient clinical data for designing ML-based methods for automated csPCa detection. The proposed algorithm might improve the accuracy of reading bpMRI, thereby beneficial for patients with prostate cancer.

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