Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma
The DLRPC model effectively predicts the response of EGFR-mutant lung adenocarcinoma patients to TKIs, providing a promising tool for personalized treatment decisions in lung cancer management.
Authors
- Zhiyuan Sun (ORCID: https://orcid.org/0000-0003-1779-9261)
- Wentao Zhu (ORCID: https://orcid.org/0000-0001-9290-1778)
- Sitong Zhou (ORCID: https://orcid.org/0000-0003-2413-5949)
- Taotao Yang (ORCID: https://orcid.org/0000-0003-3066-1849)
- Yang Gao (ORCID: https://orcid.org/0000-0001-6444-6544)
- Xianqi Wang
- Yuan Jin (ORCID: https://orcid.org/0009-0002-1987-6216)
- Peng Chen (ORCID: https://orcid.org/0000-0002-5810-8159)
- Wei Chen (ORCID: https://orcid.org/0000-0001-6132-7021)
Institutions
- Army Medical University (CN)
- Nanjing General Hospital of Nanjing Military Command (CN)
- Zhejiang Lab (CN)
- Southwest Hospital (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1186/s12967-025-06480-9
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Centre of Excellence and Applied Sport Science Research, Queensland Academy of Sport
- Chongqing Municipal Key Laboratory of Signal and Information Processing