Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features
In this study, the SVM-based combined model, which integrated clinical-semantic and radiomic features, demonstrated promising performance among the four classifiers-based combined models in distinguishing between ADC and SCC. However, due to the study's single-center, retrospective design and the lack of statistically significant differences in AUC for some models, the findings should be interpreted with caution. These results show potential but require future multi-center prospective validation before clinical application.
Authors
- Yingjin Zhang (ORCID: https://orcid.org/0000-0001-9296-6440)
- Xinru Zheng (ORCID: https://orcid.org/0009-0002-7516-8230)
- Zhaoxiang Ye (ORCID: https://orcid.org/0000-0003-3157-8393)
- Jing Wang (ORCID: https://orcid.org/0000-0003-1580-1356)
- Jiaxin Liu (ORCID: https://orcid.org/0000-0002-8770-2093)
- Jing Liang (ORCID: https://orcid.org/0000-0002-7003-7920)
- Yanju Li (ORCID: https://orcid.org/0000-0003-2924-4319)
- Xiaomeng Yang (ORCID: https://orcid.org/0000-0003-4109-1562)
Institutions
- Tianjin University of Traditional Chinese Medicine (CN)
- Tianjin Medical University Cancer Institute and Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-25
- DOI
- https://doi.org/10.3389/fonc.2025.1726193
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science and Technology Major Project