A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model
The combined model integrating delta-radiomics with clinical predictors demonstrates superior predictive performance for evaluating targeted therapy efficacy in EGFR-mutated advanced lung adenocarcinoma, significantly outperforming conventional radiomics models relying exclusively on pre-treatment imaging data.
Authors
- Jie Lei (ORCID: https://orcid.org/0000-0002-3429-4436)
- L. Zhang
- Xiuying Chen (ORCID: https://orcid.org/0009-0006-1851-583X)
- Peiyan Hua (ORCID: https://orcid.org/0000-0001-9153-9287)
- Zhishen Wu (ORCID: https://orcid.org/0000-0003-0200-1296)
- Peng Zhang (ORCID: https://orcid.org/0000-0003-1777-3790)
Institutions
- Mianyang Third People's Hospital (CN)
- First Automotive Works (China) (CN)
- Changchun 208 Hospital (CN)
- Second Affiliated Hospital of Jilin University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-05-30
- DOI
- https://doi.org/10.3389/fmed.2025.1599206
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- People's Government of Jilin Province