Machine learning-based ultrasound radiomics for predicting TP53 mutation status in hepatocellular carcinoma
Machine learning-based ultrasound radiomics signatures accurately predict <i>TP53</i> gene mutations in HCC. The XGBoost-based combined model, which combined ultrasound radiomics features with clinical features, showed the best performance and represented a promising noninvasive approach for screening <i>TP53</i>-mutated HCC.
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)
- Yuanyuan Liu (ORCID: https://orcid.org/0000-0002-7126-9782)
- Lianzhong Zhang (ORCID: https://orcid.org/0000-0002-3756-6951)
- Xiguo Cai
Institutions
- Henan Provincial People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-04-28
- DOI
- https://doi.org/10.3389/fmed.2025.1565618
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Natural Science Foundation of China