18F-FDG PET/CT radiomics for distinguishing LUAD from LUSC in non-small cell lung cancer
In this study, radiomics features extracted from both intratumoral and peritumoral regions on 18F-FDG PET/CT demonstrated high predictive value for classifying NSCLC pathological subtypes. Notably, the 1 mm peritumoral region exhibited the best performance for differentiating lung adenocarcinomas, especially in the independent testing cohort. However, these findings are based on a single-center cohort with a limited sample size and require further validation in larger, multicenter studies to confirm their clinical utility and generalizability.
Authors
- Winston Chong (ORCID: https://orcid.org/0000-0001-7645-2861)
- Jingze Li (ORCID: https://orcid.org/0000-0002-8455-7601)
- Z. Li
- Xinmeng Jasmine Mu (ORCID: https://orcid.org/0000-0002-8079-0828)
- Xin Yu Zhu
- Lei Zhang (ORCID: https://orcid.org/0009-0007-9339-3331)
- Biwen Mo (ORCID: https://orcid.org/0000-0003-3667-0076)
- Jie Qin (ORCID: https://orcid.org/0000-0002-5792-5464)
- Ge Zhao (ORCID: https://orcid.org/0009-0001-1414-1432)
- Yu Zeng (ORCID: https://orcid.org/0000-0002-8562-1310)
- Fu Wei (ORCID: https://orcid.org/0009-0000-5173-6331)
Institutions
- Qingdao University (CN)
- Guilin Medical University (CN)
- Affiliated Hospital of Qingdao University (CN)
- Shanghai University of Medicine and Health Sciences (CN)
- Affiliated Hospital of Guilin Medical College (CN)
- Qilu Hospital of Shandong University (CN)
Publication Details
- Journal
- Medicine
- Published
- 2025-12-05
- DOI
- https://doi.org/10.1097/md.0000000000045776
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00