An interpretable clinical-radiomics-deep learning model based on magnetic resonance imaging for predicting postoperative Gleason grading in prostate cancer: a dual-center study
This study developed and validated interpretable MRI-based machine learning models that combine clinical data with radiomics and deep learning features from different regions of interest, demonstrating good performance in predicting postoperative Gleason grading in PCa.
Authors
- Xiaoqian Deng (ORCID: https://orcid.org/0000-0002-4633-6314)
- Fuyu Guo (ORCID: https://orcid.org/0000-0003-1478-0362)
- Yingzhong Yang
- Yangang Zhang (ORCID: https://orcid.org/0000-0002-6317-3166)
- Yue Wang (ORCID: https://orcid.org/0000-0002-1211-2822)
- Yanbin Liu (ORCID: https://orcid.org/0000-0003-4199-397X)
- Wei Yao (ORCID: https://orcid.org/0009-0007-5489-239X)
- Peng Yue (ORCID: https://orcid.org/0000-0003-1871-6317)
- Shiwei Sun (ORCID: https://orcid.org/0000-0002-0351-3720)
Institutions
- Shanxi Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Xinzhou Teachers University (CN)
- First Hospital of Shanxi Medical University (CN)
- Second Hospital of Shanxi Medical University (CN)
- Taiyuan Central Hospital (CN)
- Shanxi Academy of Medical Sciences (CN)
- Fourth People's Hospital of Taiyuan (CN)
- Shanxi Datong University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-18
- DOI
- https://doi.org/10.3389/fonc.2025.1615012
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00