Using optimized CT type to predict histological classifications of thymic epithelial tumors: a radiomics integrated analysis
Radiomics features extracted from CT more effectively represented thymic epithelial tumor (TET) heterogeneity than morphological features. The radiomics model using contrast-enhanced CT outperformed that using non-contrast-enhanced CT in identifying histological classifications of TET. The integrated model, combining radiomics and morphological features, exhibited the highest performance in predicting TET histological classifications.
Authors
- Zhengping Zhang (ORCID: https://orcid.org/0000-0001-8624-0061)
- Tian Xingcang
- Juan Chen (ORCID: https://orcid.org/0000-0001-5801-9124)
- Zhaojun Wang
- Shuping Meng
- Li Zhu (ORCID: https://orcid.org/0000-0003-1394-6404)
- Xiaoyan Yang (ORCID: https://orcid.org/0000-0001-5088-7816)
- Y Qu
- K. Mi (ORCID: https://orcid.org/0000-0003-3022-7217)
- Yanzhu Han
Institutions
- Ningxia Medical University (CN)
- Ningxia Medical University General Hospital (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-03-22
- DOI
- https://doi.org/10.1186/s13244-025-01933-7
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Natural Science Foundation of Ningxia Province