Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models
Deep learning models based on PET/CT imaging can effectively detect, segment, and classify hypermetabolic lung lesions, and identify suspicious adjacent lesions. These results highlight the potential of artificial intelligence in clinical decision-making and lung disease diagnosis.
Authors
- Ning Wen (ORCID: https://orcid.org/0000-0001-9776-8056)
- Faquan Ji
- Qing Zhang (ORCID: https://orcid.org/0000-0001-6871-3435)
- Peng Yao (ORCID: https://orcid.org/0000-0003-0172-4310)
- Martin Quan (ORCID: https://orcid.org/0000-0001-8398-3695)
- Biao Li (ORCID: https://orcid.org/0000-0002-0493-2062)
- Jiajia Hu (ORCID: https://orcid.org/0000-0002-6221-8748)
- Yangchun Chen (ORCID: https://orcid.org/0000-0002-9076-6581)
- Zi Yang
- Ran Cheng (ORCID: https://orcid.org/0009-0003-2343-5023)
Institutions
- Nanjing University of Chinese Medicine (CN)
- Shanghai Jiao Tong University (CN)
- Ruijin Hospital (CN)
- Shanghai Pulmonary Hospital (CN)
- Yangpu Hospital of Tongji University (CN)
- Lu'an First People's Hospital (CN)
Publication Details
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1007/s00259-025-07215-0
- 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
- National Key Research and Development Program of China