Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis
The stacking ensemble learning model based on <sup>18</sup>F-FDG PET/CT radiomics demonstrates potential for predicting LNM in lung adenocarcinoma, and the quantitative analysis of radiomic features holds significant biological significance.
Authors
- Shulin Li (ORCID: https://orcid.org/0000-0001-5657-4183)
- Fang Chen (ORCID: https://orcid.org/0000-0002-6675-5508)
- Lei Wang (ORCID: https://orcid.org/0009-0009-8362-0275)
- Zhiming Xiang
Institutions
- Guangzhou University of Chinese Medicine (CN)
- Panyu Hospital of Chinese Medicine (CN)
- Panyu District Central Hospital (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-08-25
- DOI
- https://doi.org/10.3389/fonc.2025.1618494
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Natural Science Foundation of China