Deep learning on brain metastasis for predicting EGFR genotype and EGFR-TKI therapy response in metastatic NSCLC: a multicenter study
The ETS may have the potential to work as a non-invasive tool for predicting EGFR mutation status and response to EGFR-TKI therapy, which holds promise as a non-invasive tool to assist clinicians in making decisions about personalized treatment strategies.
Authors
- Ying Fan (ORCID: https://orcid.org/0000-0002-2925-507X)
- Chunna Yang
- Zekun Wang (ORCID: https://orcid.org/0000-0001-5175-4118)
- Yiyao Sun
- Bo Sun (ORCID: https://orcid.org/0000-0002-1660-2378)
- Zhiguang Yang (ORCID: https://orcid.org/0000-0001-7006-6134)
- Yahong Luo
- Wenyan Jiang (ORCID: https://orcid.org/0009-0004-6647-605X)
- Shuailin You
Institutions
- Dalian Medical University (CN)
- Fudan University (CN)
- Yantai Nanshan University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3389/fbioe.2025.1637095
- Primary Topic
- Lung Cancer Treatments and Mutations
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China