ArXiv Preprint
Non-invasive prostate cancer detection from MRI has the potential to
revolutionize patient care by providing early detection of
clinically-significant disease (ISUP grade group >= 2), but has thus far shown
limited positive predictive value. To address this, we present an MRI-based
deep learning method for predicting clinically significant prostate cancer
applicable to a patient population with subsequent ground truth biopsy results
ranging from benign pathology to ISUP grade group~5. Specifically, we
demonstrate that mixed supervision via diverse histopathological ground truth
improves classification performance despite the cost of reduced concordance
with image-based segmentation. That is, where prior approaches have utilized
pathology results as ground truth derived from targeted biopsies and
whole-mount prostatectomy to strongly supervise the localization of clinically
significant cancer, our approach also utilizes weak supervision signals
extracted from nontargeted systematic biopsies with regional localization to
improve overall performance. Our key innovation is performing regression by
distribution rather than simply by value, enabling use of additional pathology
findings traditionally ignored by deep learning strategies. We evaluated our
model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams
collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted)
biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating
that deep networks trained with mixed supervision of histopathology can
significantly exceed the performance of the Prostate Imaging-Reporting and Data
System (PI-RADS) clinical standard for prostate MRI interpretation.
Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde, Tim Ullrich, Jeffry P. Simko, Hao Nguyen, Thomas A. Hope, Peder E. Z. Larson, Kirti Magudia
2022-12-13
