Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis
An explainable machine learning model that combines dynamic FDG metabolic and flow features can predict benign or malignant lung cancer patients more accurately than conventional parameters such as SUVmax or net influx rate K<sub>i</sub>.
Authors
- Yaping Wu (ORCID: https://orcid.org/0000-0002-2979-5332)
- Ying Liang (ORCID: https://orcid.org/0000-0002-0545-1797)
- Na Dang
- Tao Sun (ORCID: https://orcid.org/0000-0001-8062-3432)
- Meiyun Wang (ORCID: https://orcid.org/0000-0003-4454-5005)
- Zhiheng Yao
- Jinpeng Zhou
- Yubo Wang (ORCID: https://orcid.org/0009-0004-7125-9762)
Institutions
- Chinese Academy of Sciences (CN)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1007/s00259-025-07231-0
- Citations
- 5
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- Science, Technology and Innovation Commission of Shenzhen Municipality
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province