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

ABSTRACT:

Objectives

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).

Methods

In this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan-Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability.

Results

The concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard reg

SUBMITTER: Yan T 

PROVIDER: S-EPMC9871481 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature