An Integrated Clinical‐Radiomics‐Deep Learning Model Based on <scp> <sup>18</sup> F </scp> ‐ <scp>FDG PET</scp> / <scp>CT</scp> for Predicting <scp>EGFR</scp> Mutation Status in Lung Adenocarcinoma
This study highlights the potential of integrating clinical, radiomic, and deep learning features as a noninvasive approach for accurately predicting EGFR mutation status in lung adenocarcinoma.
Authors
- Heqing Yi (ORCID: https://orcid.org/0000-0002-5164-4821)
- Marcus Hacker (ORCID: https://orcid.org/0000-0002-4222-4083)
- Yun Wang (ORCID: https://orcid.org/0000-0001-9309-8614)
- Changyu Sun (ORCID: https://orcid.org/0000-0001-8102-7130)
- Zhaoqing Chen (ORCID: https://orcid.org/0000-0001-9168-1478)
- Yukun Cai
- Jing Li (ORCID: https://orcid.org/0000-0003-4072-4102)
- Jingjing Zhang (ORCID: https://orcid.org/0000-0002-1349-9184)
- Xiang Li
Institutions
- Huzhou Vocational and Technical College (CN)
- Zhejiang Cancer Hospital (CN)
- Beijing Chest Hospital (CN)
- Amicable Knowledge Solution University (IN)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1002/cam4.71370
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China