Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules
A interpretable radiomics model integrating CTR and clinical features enables effective non-invasive prediction of EGFR mutation status in small LUAD nodules, supporting molecular risk stratification when tissue genotyping is unavailable.
Authors
- Xiaoqiang Qiu (ORCID: https://orcid.org/0000-0001-9881-3664)
- Jiamei Jin (ORCID: https://orcid.org/0000-0001-6072-3831)
- Yiming Liu (ORCID: https://orcid.org/0000-0003-2836-8272)
- Mingming Hu (ORCID: https://orcid.org/0000-0002-6626-9657)
- Hua Han (ORCID: https://orcid.org/0000-0003-4713-4631)
- Kai Zhao (ORCID: https://orcid.org/0009-0000-5186-2952)
- Zirui Zhu (ORCID: https://orcid.org/0009-0003-3192-0379)
- Jiaxin Wen
- Wenhan Cai (ORCID: https://orcid.org/0000-0002-8347-4505)
- Z. Q. Xue (ORCID: https://orcid.org/0000-0003-0538-5539)
Institutions
- Nankai University (CN)
- Chinese PLA General Hospital (CN)
- Hainan General Hospital (CN)
- Hainan 301 Hospital (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1080/07853890.2025.2607160
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00