Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI
Machine learning-based radiomics using CE-T1WI data is highly effective in distinguishing between NSCLC and SCLC in patients with BMs. The LightGBM model showed the best performance, suggesting that this approach shows promise as a supportive, non-invasive diagnostic tool, pending further validation in prospective clinical settings.
Authors
- Wei Du (ORCID: https://orcid.org/0000-0003-3622-2265)
- Xueming Xia
- Qiheng Gou
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Sichuan Cancer Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fonc.2025.1599882
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Sichuan University
- Sichuan Province Science and Technology Support Program