Longitudinal dynamic MRI radiomic models for early prediction of prognosis in locally advanced cervical cancer treated with concurrent chemoradiotherapy
We built machine learning models from dynamic features in longitudinal images and found that the ΔMRI<sub>mid</sub>-based model can serve as a non-invasive indicator for the early prediction of prognosis in LACC patients receiving CCRT. The integrated models with clinical characteristics further enhanced the predictive performance.
Authors
- Rong Cai (ORCID: https://orcid.org/0000-0003-1234-8971)
- Yiwei Wang (ORCID: https://orcid.org/0000-0003-1314-1833)
- Jiayi Chen (ORCID: https://orcid.org/0000-0001-6417-290X)
- Ji-Feng Xiao
- Haoping Xu
- Cai Chang (ORCID: https://orcid.org/0000-0001-7996-765X)
- Dan Ou (ORCID: https://orcid.org/0000-0001-6427-4199)
Institutions
- Ruijin Hospital (CN)
- Shanghai Genon Biological Products (China) (CN)
- Shanghai Huayi Group (China) (CN)
Publication Details
- Journal
- Radiation Oncology
- Published
- 2024-12-21
- DOI
- https://doi.org/10.1186/s13014-024-02574-8
- Citations
- 6
- Primary Topic
- Endometrial and Cervical Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 3.50
Funders
- Science and Technology Commission of Shanghai Municipality
- Shanghai Hospital Development Center
- National Key Research and Development Program of China