Integration of radiomics, habitat imaging, and deep learning for MRI-based prediction of parametrial invasion in cervical cancer: A dual-center study
The multimodal integrated model, combining radiomics, habitat imaging, 2.5D deep learning, and clinical features, demonstrated superior predictive performance for parametrial invasion in cervical cancer compared with individual models. This approach may enhance preoperative assessment, guide clinical decision-making, and optimize treatment strategies.
Authors
- Yuemei Cui
- Xinyou Wang (ORCID: https://orcid.org/0009-0006-2736-1617)
- Jiangfeng Lu (ORCID: https://orcid.org/0000-0002-4713-5477)
- Jun Wang (ORCID: https://orcid.org/0000-0001-7317-0113)
- Shichao Han (ORCID: https://orcid.org/0000-0001-5404-7347)
- LI Ya (ORCID: https://orcid.org/0000-0003-0501-2628)
- Na Jing (ORCID: https://orcid.org/0009-0009-8848-011X)
Institutions
- Dalian Medical University (CN)
- Second Affiliated Hospital of Dalian Medical University (CN)
- Dalian Municipal Central Hospital (CN)
Publication Details
- Journal
- Magnetic Resonance Imaging
- Published
- 2025-10-30
- DOI
- https://doi.org/10.1016/j.mri.2025.110542
- Primary Topic
- Endometrial and Cervical Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00