Radiogenomics model for overall survival prediction of glioblastoma

Author:

Wijethilake Navodini,Islam Mobarakol,Ren Hongliang

Funder

Singapore Academic Research Fund

The National Medical Research Council

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Reference40 articles.

1. Alex V, Safwan M, Krishnamurthi G (2017) Automatic segmentation and overall survival prediction in gliomas using fully convolutional neural network and texture analysis. In: International MICCAI Brainlesion Workshop. Springer, pp 216–225

2. Bai Y, Zhang QG, Wang XH (2014) Downregulation of tes by hypermethylation in glioblastoma reduces cell apoptosis and predicts poor clinical outcome. European Journal of Medical Research 19(1):66

3. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C (2017) Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection. The Cancer Imaging Archive, pp 286

4. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017) Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific Data 4:170117

5. Bansal A, Chen X, Russell B, Gupta A, Ramanan D (2017) Pixelnet: Representation of the pixels, by the pixels, and for the pixels. arXiv: 170206506

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