CT-based radiomics integrated model for brain metastases in stage III/IV ALK-positive lung adenocarcinoma patients
CT-derived radiomic signatures show promise as a tool for predicting BM within 2 years after detection of primary lung adenocarcinoma detection with ALK+. Combing these radiomic signatures with clinical features can enhance risk stratification for these patients.
Authors
- Wen Gao (ORCID: https://orcid.org/0000-0003-0077-4236)
- Shuke Li (ORCID: https://orcid.org/0000-0001-5715-9942)
- Teng Zhang (ORCID: https://orcid.org/0000-0002-7729-8023)
- Wei Zhang (ORCID: https://orcid.org/0000-0003-0875-263X)
- Fen Wang (ORCID: https://orcid.org/0000-0002-4876-710X)
- Jing He (ORCID: https://orcid.org/0000-0002-6668-632X)
- Caiyun Li (ORCID: https://orcid.org/0009-0000-9452-6860)
- Mei Yuan (ORCID: https://orcid.org/0000-0002-5287-9198)
- Tongfu Yu
Institutions
- Nanjing University of Chinese Medicine (CN)
- Xuzhou Medical College (CN)
- Huaian First People’s Hospital (CN)
- Second People’s Hospital of Huai’an (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-06-18
- DOI
- https://doi.org/10.3389/fonc.2025.1585930
- 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
- Government of Jiangsu Province
- Nanjing University of Posts and Telecommunications
- Nanjing University