Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer
Journal:
arXiv
Published Date:
Feb 1, 2025
Abstract
Accurate prostate cancer diagnosis remains challenging. Even when using MRI,
radiologists exhibit low specificity and significant inter-observer
variability, leading to potential delays or inaccuracies in identifying
clinically significant cancers. This leads to numerous unnecessary biopsies and
risks of missing clinically significant cancers. Here we present prostate
vision contrastive network (ProViCNet), prostate organ-specific vision
foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal
Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was
trained and validated using 4,401 patients across six institutions, as a
prostate cancer detection model on radiology images relying on patch-level
contrastive learning guided by biopsy confirmed radiologist annotations.
ProViCNet demonstrated consistent performance across multiple internal and
external validation cohorts with area under the receiver operating curve values
ranging from 0.875 to 0.966, significantly outperforming radiologists in the
reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to
0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a
virtual screening test, and we showed that we can maintain the high sensitivity
for detecting clinically significant cancers while more than doubling
specificity from 15% to 38% (p<0.001), thereby substantially reducing
unnecessary biopsies. These findings highlight that ProViCNet's potential for
enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies,
thereby optimizing diagnostic pathways.
