Predictive value of radiomics modeling based on dynamic and static 18F-FDG PET/CT imaging for the differential diagnosis of lymph nodes in lung cancer
A composite model based on an RF model of PET/CT+K<sub>i</sub> images combined with dynamic quantitative <i>K</i> <sub>i</sub> is highly effective in differentiating FDG-avid LN metastasis in lung cancer. This model provides greater net benefit and clinical value.
Authors
- Ying Liang (ORCID: https://orcid.org/0000-0002-6216-4743)
- Yarong Zhang
- Xieraili Wumener
- Jun Zhao (ORCID: https://orcid.org/0000-0002-9887-5512)
- Hongjian Wang (ORCID: https://orcid.org/0000-0002-9956-7511)
- Zhiheng Hu (ORCID: https://orcid.org/0000-0002-9165-3781)
- Jiuhui Zhao
- Yaohong Deng
Institutions
- Xinjiang Medical University (CN)
- Dalian Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- National Cancer Center (US)
- Shanghai East Hospital (CN)
- Xinjiang Uygur Autonomous Region Uygur Medicine Hospital (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-22
- DOI
- https://doi.org/10.3389/fonc.2025.1637366
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Medical Sciences
- Peking Union Medical College