Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study
The XGBoost model demonstrated robust predictive performance for preoperative identification of STAS in lung cancer patients, showing high generalizability in external validation. These findings suggest that machine learning-based predictions could guide clinical decision-making and improve surgical outcomes by identifying high-risk patients for more aggressive treatment strategies.
Authors
- Yue Zhao (ORCID: https://orcid.org/0000-0002-9554-438X)
- Yongmeng Li
- Chuan-Qi Chen
- Zhan Zhang (ORCID: https://orcid.org/0000-0003-0824-5515)
- Haiming Li (ORCID: https://orcid.org/0000-0002-4610-3086)
- Hui Tian (ORCID: https://orcid.org/0000-0002-8932-3003)
- Y. Wang (ORCID: https://orcid.org/0000-0002-4120-4873)
- Yuanyuan Ma (ORCID: https://orcid.org/0000-0002-4951-5094)
- Tian Yu (ORCID: https://orcid.org/0000-0002-3223-1773)
- Z Li (ORCID: https://orcid.org/0000-0001-9646-0439)
- S Zhang (ORCID: https://orcid.org/0000-0002-0695-2032)
Institutions
- Shandong Provincial QianFoShan Hospital (CN)
- Qilu Hospital of Shandong University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1186/s12885-025-13783-z
- Citations
- 11
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 16.70
Funders
- Key Technology Research and Development Program of Shandong