Progression‐Free Survival Prediction Model Based on <scp>AI</scp>‐Enhanced Dynamic Radiomics for Personalized <scp>EGFR</scp>‐<scp>TKI</scp> Treatment Monitoring Patients With Lung Adenocarcinoma
This study developed treatment response prediction models for EGFR-mutant LUAD patients. These models demonstrated strong predictive value for PFS in patients treated with EGFR-TKIs, potentially enabling a more efficient personalized CT scan schedule.
Authors
- Bowen Hou (ORCID: https://orcid.org/0000-0002-2262-1006)
- Huabin Hu (ORCID: https://orcid.org/0000-0003-1352-2206)
- Yan’e Liu
- Xiangfeng Luo (ORCID: https://orcid.org/0000-0003-4577-9241)
- Lu Yang (ORCID: https://orcid.org/0000-0002-4286-9586)
- Baoshan Cao (ORCID: https://orcid.org/0000-0001-7939-4820)
- Cheng Xu (ORCID: https://orcid.org/0000-0002-1526-0775)
- Xin Zhu
Institutions
- Peking University (CN)
- Peking University Third Hospital (CN)
Publication Details
- Journal
- Thoracic Cancer
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1111/1759-7714.70010
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Peking University
- Peking University Third Hospital
- National Key Research and Development Program of China