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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https://www.frontiersin.org/articles/10.3389/fncom.2022.916511/full

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