Machine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients
To predict the status of EGFR expression in HCC patients, the ultrasomics model and combined model created by five machine learning algorithms can be utilized as efficient and noninvasive techniques, and the ultrasomics model and combined model established by RF classifier have the best predictive performance.
Authors
- Shaobo Duan (ORCID: https://orcid.org/0000-0003-3150-3666)
- D Bu
- Shanshan Ren (ORCID: https://orcid.org/0009-0002-6901-1501)
- Yujing Ma (ORCID: https://orcid.org/0009-0009-0874-2926)
- Yahong Li (ORCID: https://orcid.org/0000-0003-1222-3095)
- Lianzhong Zhang (ORCID: https://orcid.org/0000-0002-3756-6951)
- Xiguo Cai
Institutions
- Zhengzhou University (CN)
- Henan Provincial People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-11-18
- DOI
- https://doi.org/10.3389/fmed.2024.1483291
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China