Added Value of Viscoelasticity for MRI-Based Prediction of Ki-67 Expression of Hepatocellular Carcinoma Using a Deep Learning Combined Radiomics (DLCR) Model

SIMPLE SUMMARY: This study aimed to explore the added value of magnetic resonance elastography (MRE) in the prediction of Ki-67 expression in hepatocellular carcinoma (HCC) using a deep learning combined radiomics (DLCR) model. A total of 108 histopathology-proven HCC patients who underwent preopera...

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Detalles Bibliográficos
Autores principales: Hu, Xumei, Zhou, Jiahao, Li, Yan, Wang, Yikun, Guo, Jing, Sack, Ingolf, Chen, Weibo, Yan, Fuhua, Li, Ruokun, Wang, Chengyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179448/
https://www.ncbi.nlm.nih.gov/pubmed/35681558
http://dx.doi.org/10.3390/cancers14112575

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179448/
https://www.ncbi.nlm.nih.gov/pubmed/35681558
http://dx.doi.org/10.3390/cancers14112575

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