Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network - Yan, Ting; Yan, Zhenpeng; Liu, Lili; Zhang, Xiaoyu; Chen, Guohui; Xu, Feng; Li, Ying; Zhang, Lijuan; Peng, Meilan; Wang, Lu; Li, Dandan; Zhao, Dong (Frontiers in Computational Neuroscience, 2023)
Abstract Objectives: To establish and validate a prognostic model based on magnetic resonance imaging (MRI) and clinical features to predict the survival time of glioblastoma multiforme (GBM) patients. 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, time-dependent ROC curve, nomogram, and calibration curve were performed to assess the survival predictive ability. Results: The concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high-risk and low-risk groups based on the median prognostic index (PI). Kaplan-Meier curve was used for survival analysis (p= <2e-16 in the training set, p= 3e-04 in the validation set, p= 0.007 in the test set), which showed the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve (AUC) of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683 and 0.898. Conclusions: The survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of GBM patients.