Abstract
Purpose
Glioblastoma is the most common primary brain tumor worldwide. Computer-aided survival prediction can provide a scientific basis for doctors to develop treatment plans to effectively avoid excessive treatment and waste of medical resources. Therefore, we used an end-to-end deep network based on radiomics to make survival predictions of patients with glioblastoma.
Methods
360 magnetic resonance imaging images of glioblastoma patients were randomly selected from the TCIA database including T1 weighted and T2 weighted images. Based on the traditional residual network (ResNet), a two-branch residual network survival prediction (BSP) model was proposed to extract and learn the features from T1 and T2 images separately. Furthermore, considering the association of tumor area and whole brain tissues, a multi-branch residual network survival prediction (M-BSP) model was proposed, which can make full use of the features of the tumor image and the whole brain image.
Results
The classification accuracy of M-BSP model using different amounts of residual blocks on test sequence was 89%, 83% and 83%, respectively, demonstrating that the M-BSP model with two residual blocks performed better in prediction. Further, the survival analysis of the prediction results indicated that the M-BSP model can effectively classify patients into a high-risk group and a low-risk group.
Conclusion
The classification and prediction results demonstrated that the M-BSP model can attain superior classification results, which can assist doctors in making diagnostic decisions and developing treatment plans.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Yueshuang, L., Xiaoyi, W., Weihua, L., et al. (2018). Radiomics in gliomas: A promising assistance for glioma clinical research. Journal of Central South University Medical Sciences, 43(4), 354–359.
Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012). Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer, 48(4), 441–446.
Lao, J., Chen, Y., Li, Z. C., et al. (2017). A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Scientific Reports, 7(1), 10353. https://doi.org/10.1038/s41598-017-10649-8.
Ahmad, C., Christian, D., Matthew, T., et al. (2017). Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget, 8(61), 104393–104407.
Ardila, D., Kiraly, A. P., Bharadwaj, S., et al. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954–961.
Chang, K., Zhang, B., Guo, X., et al. (2016). Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro-oncology, 18(12), 1680–1687.
Nicolasjilwan, M., Hu, Y., Yan, C., et al. (2015). Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. Journal of Neuroradiology, 42(4), 212–221.
Gutman, D. A., Cooper, L. A., Hwang, S. N., et al. (2013). MR imaging predictors of molecular profile and survival: Multi-institutional study of the TCGA glioblastoma data set. Radiology, 267(2), 560–569.
Bloice, M. D., Stocker, C., & Holzinger, A. (2017). Augmentor: An image augmentation library for machine learning. Journal of Open Source Software. https://doi.org/10.21105/joss.00432.
He, K., Zhang, X., Ren, S., et al. (2016). Deep residual learning for image recognition. Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90.
Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25(2), 1097–1105.
Godbole, S., & Sarawagi, S. (2004). Discriminative methods for multi-labeled classification, 8th pacific/Asia conference on advances in knowledge discovery and data. CiteSeer. https://doi.org/10.1007/978-3-540-24775-3_5.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
Hanley, J. A., & Mcneil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.
Efron, B. (1988). Logistic regression, survival analysis, and the Kaplan-Meier curve. Journal of the American Statistical Association, 83(402), 414–425.
Simonyan K, Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. Computer Science. arXiv:1409.1556.
Srivastava, N., Hinton, G., Krizhevsky, A., et al. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
Ioffe S, Szegedy C (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on international conference on machine learning. JMLR.org. arXiv/1502.03167.
Xu, Z., Chang, X., Xu, F., et al. (2012). L-1/2 Regularization: A thresholding representation theory and a fast solver. IEEE transactions on Neural Networks and Learning Systems, 23(7), 1013–1027.
Nie, D., Lu, J., Zhang, H., et al. (2019). Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages. Sci Rep, 9, 1103. https://doi.org/10.1038/s41598-018-37387-9.
Acknowledgements
This study was supported by the National Natural Science Foundation of China (Grant Nos. 61773205, 61171059).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fu, X., Chen, C. & Li, D. Multi-branch Residual Network Applied to Predict the Three-Year Survival of Patients with Glioblastoma. J. Med. Biol. Eng. 40, 655–662 (2020). https://doi.org/10.1007/s40846-020-00559-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40846-020-00559-y