Predicting EGFR gene mutation in lung adenocarcinoma using spectral CT combined with AI parameters: a diagnostic accuracy study
The integration of spectral CT and AI-derived quantitative parameters with clinical indicators demonstrates significant potential for noninvasive prediction of EGFR mutation in lung adenocarcinoma. This non-invasive predictive approach could reduce unnecessary invasive biopsies. Particularly in patients with contraindications to invasive procedures, this model offers a viable alternative.
Authors
- Meilan Huang (ORCID: https://orcid.org/0000-0001-9063-8056)
- Guowen Xu (ORCID: https://orcid.org/0000-0002-9764-9345)
- Xiangmei Zhan
- Min Xie (ORCID: https://orcid.org/0000-0002-7189-7156)
- Lilan She (ORCID: https://orcid.org/0009-0009-6975-500X)
- Yunjing Xue (ORCID: https://orcid.org/0000-0003-4563-4763)
Institutions
- Fujian Medical University (CN)
- Union Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-10-30
- DOI
- https://doi.org/10.3389/fonc.2025.1611759
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fujian Medical University