The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with 68Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma
The <sup>68</sup>Ga-FAPI PET radiomics model has high efficacy in predicting MVI in HCC, which is important for the development of HCC treatment plan and post-treatment evaluation. Different thresholds of SUV<sub>max</sub> in semi-automatic delineation methods exert a degree of influence on performance of the radiomics model.
Authors
- Rongqin Fan
- Demei Chen
- Xiaoliang Chen (ORCID: https://orcid.org/0000-0002-3505-5314)
- Xueqin Long (ORCID: https://orcid.org/0000-0002-6885-805X)
- Rui Zhou (ORCID: https://orcid.org/0000-0003-1431-6850)
- Yanmei Wang
Institutions
- Chongqing Cancer Hospital (CN)
- ProHealth Care (US)
Publication Details
- Journal
- Academic Radiology
- Published
- 2024-12-07
- DOI
- https://doi.org/10.1016/j.acra.2024.11.034
- Citations
- 6
- Primary Topic
- Peptidase Inhibition and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.70