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

This 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). In this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of t...

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Bibliographic Details
Published in:	Frontiers in computational neuroscience Vol. 16; p. 916511
Main Authors:	Yan, Ting, Yan, Zhenpeng, Liu, Lili, Zhang, Xiaoyu, Chen, Guohui, Xu, Feng, Li, Ying, Zhang, Lijuan, Peng, Meilan, Wang, Lu, Li, Dandan, Zhao, Dong
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 10.01.2023 Frontiers Media S.A
Subjects:	Alzheimer's disease Bias Brain cancer Cox proportional hazard regression Datasets Deep learning denoising autoencoder Edema Glioblastoma Glioblastoma multiforme Magnetic resonance imaging Morphology Neural networks Neuroscience Patients Prediction models Radiomics Risk groups Survival Survival analysis survival prediction time-dependent ROC curve Tumors deep learning glioblastoma multiforme denoising autoencoder survival prediction time-dependent ROC curve Cox proportional hazard regression
ISSN:	1662-5188, 1662-5188
Online Access:	Get full text
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