An MRI-pathology foundation model for noninvasive diagnosis and grading of prostate cancer.
Authors
Affiliations (14)
Affiliations (14)
- School of Internet, Anhui University, Hefei, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
- Department of Urology, Peking University Third Hospital, Beijing, China.
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China.
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Post and Telecommunications, Chongqing, China.
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China.
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
- Department of Automation, Tsinghua University, Beijing, China.
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. [email protected].
- Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China. [email protected].
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University and Jiangsu Province Hospital, Nanjing, China. [email protected].
- Department of Urology, Peking University Third Hospital, Beijing, China. [email protected].
- Department of Urology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China. [email protected].
Abstract
Prostate cancer is a leading health concern for men, yet current clinical assessments of tumor aggressiveness rely on invasive procedures that often lead to inconsistencies. There remains a critical need for accurate, noninvasive diagnosis and grading methods. Here we developed a foundation model trained on multiparametric magnetic resonance imaging (MRI) and paired pathology data for noninvasive diagnosis and grading of prostate cancer. Our model, MRI-based Predicted Transformer for Prostate Cancer (MRI-PTPCa), was trained under contrastive learning on nearly 1.3 million image-pathology pairs from over 5,500 patients in discovery, modeling, external and prospective cohorts. During real-world testing, prediction of MRI-PTPCa demonstrated consistency with pathology and superior performance (area under the curve above 0.978; grading accuracy 89.1%) compared with clinical measures and other prediction models. This work introduces a scalable, noninvasive approach to prostate cancer diagnosis and grading, offering a robust tool to support clinical decision-making while reducing reliance on biopsies.