Lesion‐guided selective multi‐modal integration for prostate cancer segmentation and PI‐RADS grading in MP‐MRI
The proposed model's robustness in handling diverse lesion presentations, combined with its reliable assessments, underscores its significant clinical applicability. Our model offers substantial advancements in both segmentation accuracy and PI-RADS grading, addressing the challenges of inter-reader variability and the need for high expertise in conventional diagnostic practices. This technological innovation holds promise for enhancing early, accurate detection and risk assessment in prostate cancer management, ultimately improving patient outcomes.
Authors
- Shanshan Wang (ORCID: https://orcid.org/0009-0007-3448-7258)
- Menglin Wu (ORCID: https://orcid.org/0000-0001-6392-0478)
- Yuhan Zhang (ORCID: https://orcid.org/0000-0003-1764-5043)
- Xuetao Mu (ORCID: https://orcid.org/0000-0002-7444-5948)
- Pablo D. Burstein
- Fan Li (ORCID: https://orcid.org/0009-0005-4616-3424)
- Yu‐Hui Tao (ORCID: https://orcid.org/0000-0002-6119-2769)
- Jie Zhu (ORCID: https://orcid.org/0000-0001-9798-3346)
Institutions
- Shanghai University (CN)
- Shanghai Medical College of Fudan University (CN)
- Nanjing Tech University (CN)
- Chinese University of Hong Kong (HK)
- Nanjing University of Information Science and Technology (CN)
- Chinese PLA General Hospital (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
- The Medical Device (United Kingdom) (GB)
- Nano Carbon (Poland) (PL)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-19
- DOI
- https://doi.org/10.1002/mp.70019
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00