Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell Carcinoma: Machine Learning Modeling Development and Validation Study
The proposed SVM-based model enables accurate prediction of postoperative recurrence in ESCC patients with high sensitivity, specificity, and discriminative power, offering a valuable tool for clinical risk stratification.
Authors
- X Y Feng
- Kang Jiang (ORCID: https://orcid.org/0000-0002-0884-1651)
- Ying Kang (ORCID: https://orcid.org/0000-0003-2963-0201)
- Weiyi Liao
- Fangyu Wang (ORCID: https://orcid.org/0000-0002-7129-8655)
- Yi Shen (ORCID: https://orcid.org/0000-0002-4447-1053)
- Qiong Jiang (ORCID: https://orcid.org/0000-0001-8083-7748)
- Zhuang Zhuang Cong
- Meng Xu
- Zhi Sheng Jiang (ORCID: https://orcid.org/0000-0002-8716-2920)
- Lin Wu (ORCID: https://orcid.org/0000-0002-1962-4652)
- Jing Luo (ORCID: https://orcid.org/0009-0008-3540-4191)
Institutions
- Nanjing General Hospital of Nanjing Military Command (CN)
- Jinling Institute of Technology (CN)
- Nanjing Medical University (CN)
- Nanjing University (CN)
Publication Details
- Journal
- JMIR Cancer
- Published
- 2025-10-23
- DOI
- https://doi.org/10.2196/68027
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00