Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study
The XGBoost model provides robust preoperative prediction of STAS and may assist clinicians in optimizing surgical strategies for patients with stage I lung adenocarcinoma.
Authors
- Qing Ren (ORCID: https://orcid.org/0000-0002-8802-4810)
- Jian You (ORCID: https://orcid.org/0000-0002-9637-8379)
- Xiaomeng Xu (ORCID: https://orcid.org/0000-0002-1717-538X)
- Kai On Chu
- Jianda Hu (ORCID: https://orcid.org/0000-0002-4438-2544)
- Lin Liu (ORCID: https://orcid.org/0000-0002-3576-0304)
- Huijun Wang (ORCID: https://orcid.org/0000-0002-9992-6457)
- Xiaolin Wang (ORCID: https://orcid.org/0000-0002-3911-6911)
- Yusheng Shu (ORCID: https://orcid.org/0000-0002-8423-0422)
- Jun Wu (ORCID: https://orcid.org/0000-0001-5537-6804)
Institutions
- Xuzhou Medical College (CN)
- Dalian Medical University (CN)
- Northern Jiangsu People's Hospital (CN)
- Wuxi People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-29
- DOI
- https://doi.org/10.3389/fonc.2025.1682633
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Jiangsu Commission of Health