Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT radiomic subtypes: a multi-cohort study
This study establishes a radiomic subtype associated with NSCLC immunotherapy efficacy and tumor immune microenvironment. The findings provide a non-invasive tool for personalized treatment, enabling early identification of immunotherapy-responsive patients and optimized therapeutic strategies.
Authors
- Peng Mo
- Yunting Liu (ORCID: https://orcid.org/0000-0003-3799-0362)
- Yi Li (ORCID: https://orcid.org/0000-0002-8876-0041)
- Yiqun Chen (ORCID: https://orcid.org/0000-0003-4218-084X)
- Bingxin Gong
- Lian Yang (ORCID: https://orcid.org/0000-0003-4664-2474)
- Qianqian Fan (ORCID: https://orcid.org/0000-0002-5372-9933)
- Qingjie Sun (ORCID: https://orcid.org/0000-0002-6914-7794)
- Yuanxi Li
- Wei Tan (ORCID: https://orcid.org/0009-0002-0824-0095)
- Yusheng Guo (ORCID: https://orcid.org/0000-0003-2977-9356)
- Chuansheng Zheng (ORCID: https://orcid.org/0000-0002-2435-1417)
- Lianwei Miao
Institutions
- Fujian Medical University (CN)
- Union Hospital (HK)
- Nantong University (CN)
- Affiliated Hospital of Nantong University (CN)
- Wuhan College (CN)
- Hubei Provincial Center for Disease Control and Prevention (CN)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Wuhan University of Science and Technology (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1097/js9.0000000000002839
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44