Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study
The presented combined model based on GPTV<sub>3</sub> effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.
Authors
- Minjiang Chen (ORCID: https://orcid.org/0000-0002-4040-6115)
- Chunli Kong (ORCID: https://orcid.org/0000-0003-0578-1056)
- Jiansong Ji (ORCID: https://orcid.org/0000-0002-6026-3676)
- Shuiwei Xia
- Chenying Lu
- Jianbin Li (ORCID: https://orcid.org/0000-0002-4053-1792)
- Yongjun Chen (ORCID: https://orcid.org/0000-0002-6486-2000)
- Yanjun Li (ORCID: https://orcid.org/0000-0002-6277-4189)
- Feng Ye
- Jing Yang (ORCID: https://orcid.org/0000-0002-7439-4802)
- Guihan Lin
- Yumin Hu (ORCID: https://orcid.org/0000-0001-9119-4944)
- Weibo Mao (ORCID: https://orcid.org/0000-0003-2990-0857)
- Weiyue Chen (ORCID: https://orcid.org/0000-0002-1313-0168)
- Jianfei Tu
Institutions
- Ningbo University (CN)
- Wenzhou Medical University (CN)
- Lishui University (CN)
- Jiaxing University (CN)
- First Hospital of Jiaxing (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-03-13
- DOI
- https://doi.org/10.1186/s40644-025-00856-2
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Natural Science Foundation of China