Cargando…
Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis
PURPOSE: Synaptophysin (SYP) gene expression levels correlate with the survival rate of glioma patients. This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expre...
Autores principales: | Xiao, Zheng, Yao, Shun, Wang, Zong-ming, Zhu, Di-min, Bie, Ya-nan, Zhang, Shi-zhong, Chen, Wen-li |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202412/ https://www.ncbi.nlm.nih.gov/pubmed/34136394 http://dx.doi.org/10.3389/fonc.2021.663451 |
Ejemplares similares
-
Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma
por: Sun, Chen, et al.
Publicado: (2022) -
Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study
por: Zhang, Zhaotao, et al.
Publicado: (2021) -
The combination of radiomics features and VASARI standard to predict glioma grade
por: You, Wei, et al.
Publicado: (2023) -
A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging
por: Wang, Zhen-hua, et al.
Publicado: (2021) -
Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features
por: Deng, Da-Biao, et al.
Publicado: (2022)