Tumour habitat-based radiomics analysis enhances the ability to predict prostate cancer aggressiveness with biparametric MRI-derived features
We developed a habitat-based radiomics model with a greater ability to distinguish between csPCa and non-csPCa on the basis of bp-MRI than a traditional radiomics model and clinical model. This introduces a novel approach for assessing the heterogeneity of PCa and offers urologists a quantitative, noninvasive method for preoperatively evaluating the aggressiveness of PCa.
Authors
- Mengjuan Li (ORCID: https://orcid.org/0000-0002-5692-9287)
- Shengnan Yin (ORCID: https://orcid.org/0000-0001-5209-8124)
- Yiding Ji (ORCID: https://orcid.org/0000-0003-2678-7051)
- Yan Lü (ORCID: https://orcid.org/0000-0002-6402-8788)
- Long Jin
- Ning Ding (ORCID: https://orcid.org/0000-0002-2729-6126)
Institutions
- Soochow University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-03-17
- DOI
- https://doi.org/10.3389/fonc.2025.1504132
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44