Multimodal prediction of tyrosine kinase inhibitors therapy outcomes in advanced EGFR-mutated NSCLC patients
The multimodal models (TR-M and PFS-M) demonstrated high accuracy in predicting the therapy response and directly estimating of PFS following EGFR-TKIs treatment in stage IV NSCLC patients, revealing significant potential for aiding clinical decisions and facilitating personalized treatment strategies.
Authors
- Nanying Che (ORCID: https://orcid.org/0000-0001-7890-8957)
- Yongmeng Li
- Xiaodong Chai (ORCID: https://orcid.org/0000-0002-2243-2301)
- Hongxia Li (ORCID: https://orcid.org/0000-0003-1390-1951)
- Moxuan Yang
- Zichen Liu (ORCID: https://orcid.org/0000-0001-6694-6008)
- Junfeng Zeng
- Genxiu Chen
- Wei Wang (ORCID: https://orcid.org/0000-0003-3185-2487)
- Shuhao Wang (ORCID: https://orcid.org/0000-0002-5467-3548)
- Tongmei Zhang (ORCID: https://orcid.org/0000-0003-4271-3773)
- Kun Li
Institutions
- Tianjin University of Science and Technology (CN)
- Capital Medical University (CN)
- China University of Petroleum, Beijing (CN)
- Beijing Chest Hospital (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1186/s12967-025-06956-8
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Beijing Nova Program