Enhancing foundation model transfer for prostate cancer detection with patch-level contrastive learning.
Authors
Affiliations (14)
Affiliations (14)
- Department of Radiology, Stanford University, Stanford, CA, USA. [email protected].
- Department of Radiology, Stanford University, Stanford, CA, USA.
- Institute of Computational and Mathematical Engineering, Stanford University, Stanford, USA.
- Department of Urology, Stanford University, Stanford, CA, USA.
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Epidemiology and Clinical Research, Stanford University, Stanford, USA.
- School of Medicine, Stanford University, Stanford, CA, USA.
- Department of Urology, University of California San Diego, La Jolla, CA, USA.
- Department of Radiology, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Department of Radiology, Stanford University, Stanford, CA, USA. [email protected].
- Department of Urology, Stanford University, Stanford, CA, USA. [email protected].
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. [email protected].
Abstract
Accurate localization of prostate cancer on magnetic resonance imaging (MRI) remains challenging due to the subtle appearance of cancer, resulting in missed clinically significant cancers, large inter-reader variability and large numbers of confounders that require biopsy confirmation. Vision foundation models have shown promise, but direct transfer to prostate MRI is challenging due to the substantial domain gap between natural images and prostate MRI and the subtle appearance of prostate cancer. We therefore developed prostate vision contrastive networks (ProViCNet), a weakly supervised model utilizing patch-level contrastive learning on MRI to support MRI-based screening, biopsy targeting and focal treatment planning. ProViCNet was trained and validated (using 4401 patients across six cohorts) as a prostate cancer detection model on MRI. Training labels were biopsy-confirmed radiologist annotations, while the evaluation labels included both biopsy and surgery-confirmed lesions. ProViCNet demonstrated consistent detection and segmentation performance across multiple internal and external validation cohorts, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.875 to 0.966, and outperforming radiologists in the MRI expert reader study (0.907 versus 0.805, p < 0.01). We also integrated ProViCNet with serum PSA to develop a virtual screening test, which preserved high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p < 0.001) among men with PSA ≥ 4 ng/mL, thereby potentially reducing unnecessary biopsies. These findings highlight ProViCNet's potential for enhancing the accuracy of prostate cancer diagnosis and reducing unnecessary biopsies.