Interpretable multiparametric MRI radiomics-based machine learning model for preoperative differentiation between benign and malignant prostate masses: a diagnostic, multicenter study
Radiomic features from mpMRI combined with machine learning facilitate accurate preoperative evaluation of the malignancy in prostate masses. SHAP can disclose the underlying prediction process of the ML model, which may promote its clinical applications.
Authors
- Guohua Huang
- Zhangcheng Liu (ORCID: https://orcid.org/0000-0001-5208-5083)
- Wenjun Zhou (ORCID: https://orcid.org/0000-0003-2346-8151)
- Shuai Su (ORCID: https://orcid.org/0000-0002-1648-0817)
- Jian-Hua Lan
- Delin Wang (ORCID: https://orcid.org/0000-0003-0689-3199)
- Yu Luo (ORCID: https://orcid.org/0000-0002-0711-2385)
- Kun Han (ORCID: https://orcid.org/0000-0001-9272-5567)
- Jindong Zhang (ORCID: https://orcid.org/0000-0002-5660-1281)
- Ling Jiang (ORCID: https://orcid.org/0000-0002-6589-1399)
- Jue Wang (ORCID: https://orcid.org/0000-0002-6509-8219)
Institutions
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Panzhihua Central Hospital (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-05-05
- DOI
- https://doi.org/10.3389/fonc.2025.1541618
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Centre Scientifique et Technique du Bâtiment