Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning
Radiomics combined with machine learning and based on <sup>18</sup>F-FDG PET/CT images can effectively determine EGFR mutation status in NSCLC patients. Radiomics-based nomograms provide a non-invasive and visually intuitive prediction tool for screening NSCLC patients with EGFR mutations in a clinical setting.
Authors
- Yi Yang
- Jianbo Li (ORCID: https://orcid.org/0000-0003-0031-1645)
- Xuemei Wang (ORCID: https://orcid.org/0000-0002-3516-0966)
- Qin Shi (ORCID: https://orcid.org/0000-0002-9403-752X)
- Qiang Xie
- Ming Ni (ORCID: https://orcid.org/0000-0003-1357-6263)
- Ji-Kui Xie
Institutions
- University of Science and Technology of China (CN)
- Inner Mongolia Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-02
- DOI
- https://doi.org/10.3389/fonc.2025.1510386
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44