A more selective risk-stratified, machine learning-based, lung cancer thromboprophylaxis protocol following VATS segmentectomy: a prospective cohort study
Our explainable risk-stratification ML model not only accurately predicts the risk of VTE following VATS segmentectomy in early-stage NSCLC patients, but also exhibits substantial clinical relevance to adverse prognostic outcomes in this patient cohort.
Authors
- Yili Fu (ORCID: https://orcid.org/0000-0002-9806-615X)
- Jing Wang (ORCID: https://orcid.org/0000-0001-5701-476X)
- Shuqiao Yang
- Songping Cui
- Di Wang (ORCID: https://orcid.org/0009-0006-3620-7857)
- Hui Li (ORCID: https://orcid.org/0000-0003-4759-1779)
- Bin Hu (ORCID: https://orcid.org/0000-0001-5906-1527)
- Qing Zhao (ORCID: https://orcid.org/0009-0005-3578-791X)
- Jinbai Miao (ORCID: https://orcid.org/0009-0007-7094-1032)
- Qirui Chen
Institutions
- Harvard University (US)
- Capital Medical University (CN)
- Tianjin First Center Hospital (CN)
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
- Massachusetts General Hospital (US)
- Tianjin Medical University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-12-05
- DOI
- https://doi.org/10.1097/js9.0000000000004151
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00