论文信息 - 3D Deep Attention Network for Survival Prediction from Magnetic Resonance Images in Glioblastoma

3D Deep Attention Network for Survival Prediction from Magnetic Resonance Images in Glioblastoma

Existing deep convolutional neural network-based survival analysis neither consider the modern attention mechanism nor use 3D tomographic medical images such as magnetic resonance images (MRI). This paper for the first time presents a 3D deep convolutional neural network using attention mechanism for survival prediction from multiparametric MRI in glioblastoma (GBM) patients. The attention module is incorporated into the residual network to enhance the representation power of meaningful features while suppress unimportant ones. The proposed model achieves an C-index of 0.71 in the training dataset and 0.68 in an independent test dataset, which outperforms both the traditional Cox model (0.60,0.54) and the non-attentive model (0.63,0.61). It indicates that the proposed 3D attention network has the potential of offering better performance in predicting survival using MRI than traditional survival analysis.

[1] Xiaogang Wang,et al. Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2] D Faraggi,et al. A neural network model for survival data. , 1995, Statistics in medicine.

[3] Ping Wang,et al. Machine Learning for Survival Analysis , 2019, ACM Comput. Surv..

[4] Jieping Ye,et al. A Multi-Task Learning Formulation for Survival Analysis , 2016, KDD.

[5] Jill S Barnholtz-Sloan,et al. Predicting cancer outcomes from histology and genomics using convolutional networks , 2017 .

[6] Guangtao Zhai,et al. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme , 2017, Scientific Reports.

[7] Gelareh Zadeh,et al. GBM's multifaceted landscape: highlighting regional and microenvironmental heterogeneity. , 2014, Neuro-oncology.

[8] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[9] D.,et al. Regression Models and Life-Tables , 2022 .

[10] Junzhou Huang,et al. Deep convolutional neural network for survival analysis with pathological images , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[11] Chaofeng Liang,et al. A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme , 2017, Scientific Reports.