An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma
The CT-derived radiomics signature and nomogram show promise for preoperative ALK mutation prediction in lung adenocarcinoma. These tools could enhance clinical decision-making by identifying candidates for targeted therapies, though further validation is needed to optimize their application in diverse populations.
Authors
- Lei Yang (ORCID: https://orcid.org/0000-0003-3718-2138)
- Taijuan Zhang
- Hui Ding (ORCID: https://orcid.org/0000-0002-1920-7613)
- Zaixian Zhang (ORCID: https://orcid.org/0000-0002-1331-4856)
- Yaqiong Ge
- Zhiming Li (ORCID: https://orcid.org/0000-0002-8248-7953)
- Shunli Liu (ORCID: https://orcid.org/0000-0001-7571-9675)
Institutions
- Qingdao University (CN)
- Affiliated Hospital of Qingdao University (CN)
- United Imaging Healthcare (China) (CN)
- Qingdao Women and Children's Hospital (CN)
Publication Details
- Journal
- Current Medical Imaging Formerly Current Medical Imaging Reviews
- Published
- 2025-08-04
- DOI
- https://doi.org/10.2174/0115734056376470250718053430
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00