Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

PURPOSE: To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model. METHODS:...

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Detalles Bibliográficos
Autores principales: Liu, Hai-Feng, Lu, Yang, Wang, Qing, Lu, Yu-Jie, Xing, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693828/
https://www.ncbi.nlm.nih.gov/pubmed/38050577
http://dx.doi.org/10.2147/JHC.S434895

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693828/
https://www.ncbi.nlm.nih.gov/pubmed/38050577
http://dx.doi.org/10.2147/JHC.S434895

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