An integrated radiomics and deep learning model on multisequence MRI for preoperative prediction of lymphovascular space invasion in endometrial cancer
A multimodal MRI-based model integrating both RFs and DFs achieved superior performance for noninvasive prediction of LVSI in patients with EC. This approach holds potential to enhance preoperative risk stratification and guide personalized treatment planning.
Authors
- Jinliang Niu (ORCID: https://orcid.org/0000-0002-4465-5477)
- Jin Cheng (ORCID: https://orcid.org/0009-0006-5744-8131)
- Guo Li (ORCID: https://orcid.org/0000-0001-7646-4213)
- Hong-Jian Luo
- Jia-Liang Ren
- Ke Wang (ORCID: https://orcid.org/0000-0003-4654-8429)
- Xiaoli Song (ORCID: https://orcid.org/0000-0002-9868-7837)
Institutions
- Shanxi Medical University (CN)
- Peking University (CN)
- Peking University People's Hospital (CN)
- Second Hospital of Shanxi Medical University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1186/s12880-025-02091-4
- Primary Topic
- Endometrial and Cervical Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00