CT-based radiomics and deep learning to predict EGFR mutation status in lung adenocarcinoma
Our study demonstrated that integrating radiomics and deep learning contributed to EGFR mutation prediction, providing a non-invasive approach to support personalized treatment decisions in lung adenocarcinoma.
Authors
- Juan Xu (ORCID: https://orcid.org/0000-0002-2591-2335)
- Qian Sun (ORCID: https://orcid.org/0000-0001-8556-2464)
- Xingzhi Jiang (ORCID: https://orcid.org/0000-0001-5556-6261)
- Wang Chen (ORCID: https://orcid.org/0000-0003-4723-5395)
- Can Wang (ORCID: https://orcid.org/0000-0002-0959-5501)
- Wei Li (ORCID: https://orcid.org/0000-0002-9515-6651)
- Lei Yu (ORCID: https://orcid.org/0000-0002-7288-2458)
Institutions
- Xuzhou Medical College (CN)
- Nantong University (CN)
- Zhongda Hospital Southeast University (CN)
- Yancheng First People's Hospital (CN)
- Yancheng Third People's Hospital (CN)
- Yancheng Teachers University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3389/fonc.2025.1597548
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00