Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography
The fusion model based on noninvasive radiomics and clinical features of unenhanced CT images has high clinical value for distinguishing focal hepatic lesions.
Authors
- Wenbin Ji (ORCID: https://orcid.org/0000-0002-1609-8925)
- Zhuangxuan Ma (ORCID: https://orcid.org/0000-0003-3296-2952)
- Liang Jin (ORCID: https://orcid.org/0000-0002-7552-7849)
- Xiaogang Li (ORCID: https://orcid.org/0000-0001-9255-7559)
- Ling Zhang (ORCID: https://orcid.org/0000-0002-2756-0759)
- Nan Yang (ORCID: https://orcid.org/0000-0003-4237-4513)
- Xi Qian (ORCID: https://orcid.org/0000-0001-5338-3592)
Institutions
- Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
- Huadong Hospital (CN)
- Eye & ENT Hospital of Fudan University (CN)
Publication Details
- Journal
- Abdominal Radiology
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1007/s00261-024-04685-y
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- National Key Research and Development Program of China