Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network

Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network

ObjectivesThis study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM).MethodsIn this study, a convolutional denoising autoencoder (DAE) network combined with the lo...

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Bibliographic Details
Main Authors:	Ting Yan, Zhenpeng Yan, Lili Liu, Xiaoyu Zhang, Guohui Chen, Feng Xu, Ying Li, Lijuan Zhang, Meilan Peng, Lu Wang, Dandan Li, Dong Zhao
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2023-01-01
Series:	Frontiers in Computational Neuroscience
Subjects:	glioblastoma multiforme deep learning survival prediction denoising autoencoder time-dependent ROC curve Cox proportional hazard regression
Online Access:	https://www.frontiersin.org/articles/10.3389/fncom.2022.916511/full

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