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This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (137) Decision Support for Health Professionals (2222) Cancer Survivorship (451) Machine Learning (3158) Postoperative Monitoring and Telemonitoring (244) Gastroenterology and Hepatology (201) Perioperative Risk Assessment (52)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68027, first published .
Doctor wearing a mask examines a woman with a sore throat in a clinic.

Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell Carcinoma: Machine Learning Modeling Development and Validation Study

Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell Carcinoma: Machine Learning Modeling Development and Validation Study

Authors of this article:

Meng Qing Xu1 Author Orcid Image ;   Zhi Sheng Jiang2 Author Orcid Image ;   Wan Yu Liao1, 3 Author Orcid Image ;   Ying Kang1, 3 Author Orcid Image ;   Xiao Yue Feng1, 3 Author Orcid Image ;   Kang Jiang1 Author Orcid Image ;   Qiong Jiang1 Author Orcid Image ;   Zhuang Zhuang Cong2 Author Orcid Image ;   Jing Luo2 Author Orcid Image ;   Lin Wu1 Author Orcid Image ;   Yi Shen2 Author Orcid Image ;   Fang Yu Wang1, 3 Author Orcid Image
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Journals

  1. Li K, Qian Z, Mao J, Lu S, Zhang J, Han Y, Leng X. Applications of artificial intelligence in postoperative surveillance and management of esophageal squamous cell carcinoma. Frontiers in Artificial Intelligence 2026;9 View
  2. Long F, Zhang H, Zhang S, Dong Z, Huang X, Hu R. Machine learning applications in the detection and treatment of esophageal cancer. Discover Oncology 2026;17(1) View