  Prostate cancer is the second deadliest cancer for American men. While
Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted
biopsies for prostate cancer diagnosis, its utility remains limited due to high
rates of false positives and false negatives as well as low inter-reader
agreements. Machine learning methods to detect and localize cancer on prostate
MRI can help standardize radiologist interpretations. However, existing machine
learning methods vary not only in model architecture, but also in the ground
truth labeling strategies used for model training. In this study, we compare
different labeling strategies, namely, pathology-confirmed radiologist labels,
pathologist labels on whole-mount histopathology images, and lesion-level and
pixel-level digital pathologist labels (previously validated deep learning
algorithm on histopathology images to predict pixel-level Gleason patterns) on
whole-mount histopathology images. We analyse the effects these labels have on
the performance of the trained machine learning models. Our experiments show
that (1) radiologist labels and models trained with them can miss cancers, or
underestimate cancer extent, (2) digital pathologist labels and models trained
with them have high concordance with pathologist labels, and (3) models trained
with digital pathologist labels achieve the best performance in prostate cancer
detection in two different cohorts with different disease distributions,
irrespective of the model architecture used. Digital pathologist labels can
reduce challenges associated with human annotations, including labor, time,
inter- and intra-reader variability, and can help bridge the gap between
prostate radiology and pathology by enabling the training of reliable machine
learning models to detect and localize prostate cancer on MRI.
