CT radiomics and human-machine hybrid system for differentiating mediastinal lymphomas from thymic epithelial tumors
The clinico-radiomics model outperformed three radiologists in differentiating lymphomas from TETs on CT. The use of the human-machine hybrid system significantly improved the performance of radiologists, especially junior radiologists. It provides a real-time decision tool to reduce bias and mistakes in radiologist diagnosis and enhances the diagnostic confidence of junior radiologists. This attempt may lead to more human-machine hybrid systems being explored in the diagnosis of different diseases to drive future clinical applications.
Authors
- Li Zhu (ORCID: https://orcid.org/0000-0002-5040-5415)
- Kehui Nie (ORCID: https://orcid.org/0000-0003-3340-2674)
- Shengjian Zhang (ORCID: https://orcid.org/0000-0002-0225-8828)
- H. J. Yang (ORCID: https://orcid.org/0009-0004-8274-9863)
- Jiahui Yu (ORCID: https://orcid.org/0000-0002-1215-3851)
- Han Xia
Institutions
- Shanghai Jiao Tong University (CN)
- Shanghai Chest Hospital (CN)
- Fudan University Shanghai Cancer Center (CN)
- Kangmei Pharmaceutical (China) (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1186/s40644-024-00808-2
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Shanghai Municipal Health Commission