A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer
The combined model integrating rad-score and clinical semantic features effectively evaluates PFS in EOC patients. The radiomics nomogram provides a non-invasive, simple, and feasible method to predict PFS in EOC patients, which may facilitate clinical decision-making.
Authors
- Fengyuan Luo
- Yinping Leng
- Lianggeng Gong (ORCID: https://orcid.org/0000-0001-7900-874X)
- Fei Peng (ORCID: https://orcid.org/0000-0002-5446-9854)
- Wenjie Liu (ORCID: https://orcid.org/0000-0002-8044-5338)
- Jingjing Zhou (ORCID: https://orcid.org/0009-0000-1874-9296)
Institutions
- Nanchang University (CN)
- Jiangxi Provincial Academy of Medical Sciences (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- University of South China (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1186/s12885-025-14265-y
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hunan Province