Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT
Based on dual-time-point <sup>18</sup>F-FDG PET/CT images, the 3D nnU-net with a majority voting method, demonstrates excellent performance in predicting the malignant risk of GGOs. This methodology serves as a valuable adjunct for physicians in the risk prediction and assessment of GGOs.
Authors
- Bulin Du
- Xuena Li (ORCID: https://orcid.org/0000-0003-1109-0980)
- Yuhang Liu (ORCID: https://orcid.org/0000-0001-6844-7480)
- Yaming Li
- Jian Wang (ORCID: https://orcid.org/0000-0002-5338-6395)
Institutions
- First Hospital of China Medical University (CN)
- China Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1186/s40644-025-00834-8
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Natural Science Foundation of China