Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT‐based radiomics features
The SVM model has emerged as a commendable non-invasive technique, showing high precision and dependability in forecasting EGFR mutation statuses in individuals with LAC. The radiomics model derived from PET/CT scans holds promise as a prognostic indicator of EGFR mutations in LAC, offering a valuable tool that could refine personalized therapeutic strategies and ultimately enhance the prognosis for LAC patients.
Authors
- Di Jin (ORCID: https://orcid.org/0000-0002-7445-9936)
- Rong Xu (ORCID: https://orcid.org/0000-0003-3127-4795)
- Changchun Wang (ORCID: https://orcid.org/0000-0003-3183-2160)
- Zhikang Deng (ORCID: https://orcid.org/0009-0003-2960-9012)
- Bing Fan (ORCID: https://orcid.org/0000-0003-4439-6150)
- Pei Huang (ORCID: https://orcid.org/0000-0003-3794-0162)
- Yaohong Deng
Institutions
- Nanchang University (CN)
- Xinjiang Medical University (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Tumor Hospital of Xinjiang Medical University (CN)
- Jiangxi Provincial People's Hospital (CN)
- Panyu District Central Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1002/mp.17780
- 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