Machine learning-driven clinical decision support for radical local consolidative therapy in synchronous oligometastatic NSCLC: A SEER population-based analysis of 17 cancer registries (2018–2021)
This multicenter, population-based study is the first to establish and internally validate a machine learning-based risk stratification tool for patients with synchronous oligometastatic NSCLC. Radical LCT, particularly when combined with chemotherapy, was associated with improved OS in this cohort. The model may facilitate preliminary risk stratification and assist multidisciplinary management, but external validation and integration of key clinical and molecular variables are needed before clinical application. Prospective multicenter validation is warranted.
Authors
- Hefei Liu (ORCID: https://orcid.org/0000-0001-6533-7112)
- Dan Hu (ORCID: https://orcid.org/0000-0002-6288-816X)
- Xuan Wang (ORCID: https://orcid.org/0000-0002-0830-0898)
- Chen Liu (ORCID: https://orcid.org/0009-0003-0430-6146)
- Yingjie Wang (ORCID: https://orcid.org/0000-0002-2646-0772)
- Xiaoli Kang (ORCID: https://orcid.org/0000-0003-1091-065X)
- Jianchun Zhang
- Xiaolong Hu (ORCID: https://orcid.org/0000-0002-7086-5339)
- Gang Ren (ORCID: https://orcid.org/0000-0003-4082-1025)
- Haifeng Pang
Institutions
- University of Science and Technology of China (CN)
- Anhui Medical University (CN)
- Beijing Geriatric Hospital (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Peking University Shougang Hospital (CN)
- Air Force General Hospital PLA (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1097/js9.0000000000003599
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00