Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data
The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.
Authors
- Changtao Jiang (ORCID: https://orcid.org/0000-0002-5206-2372)
- Shuqi Li (ORCID: https://orcid.org/0009-0007-4467-6773)
- Yufei Fang
- Lei Ling (ORCID: https://orcid.org/0000-0001-6316-2128)
- Jie Xu (ORCID: https://orcid.org/0000-0001-9233-4363)
- Junjun Qiu (ORCID: https://orcid.org/0000-0002-2063-2684)
- Xinwei Peng (ORCID: https://orcid.org/0000-0001-9868-0136)
- Chenyan Guo (ORCID: https://orcid.org/0000-0003-1203-5705)
- He Zhang (ORCID: https://orcid.org/0000-0003-3782-1411)
- Keqin Hua (ORCID: https://orcid.org/0000-0002-4546-5229)
- Jue Wang (ORCID: https://orcid.org/0000-0002-6509-8219)
Institutions
- Beijing Academy of Artificial Intelligence (CN)
- Obstetrics and Gynecology Hospital of Fudan University (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Xi’an Jiaotong-Liverpool University (CN)
Publication Details
- Journal
- JMIR Cancer
- Published
- 2025-06-30
- DOI
- https://doi.org/10.2196/69057
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72