Journal
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Volume 58, Issue 8, Pages 1767-1777Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-020-02179-9
Keywords
Brain tumor segmentation; Glioblastoma; Survival prediction; Hypercolumn; Convolutional neural network (CNN); PixelNet
Categories
Funding
- Singapore Academic Research Fund [R-397-000-297-114]
- NMRC Bedside and Bench [R-397-000-245-511]
Ask authors/readers for more resources
Glioblastoma multiforme (GBM) is a very aggressive and infiltrative brain tumor with a high mortality rate. There are radiomic models with handcrafted features to estimate glioblastoma prognosis. In this work, we evaluate to what extent of combining genomic with radiomic features makes an impact on the prognosis of overall survival (OS) in patients with GBM. We apply a hypercolumn-based convolutional network to segment tumor regions from magnetic resonance images (MRI), extract radiomic features (geometric, shape, histogram), and fuse with gene expression profiling data to predict survival rate for each patient. Several state-of-the-art regression models such as linear regression, support vector machine, and neural network are exploited to conduct prognosis analysis. The Cancer Genome Atlas (TCGA) dataset of MRI and gene expression profiling is used in the study to observe the model performance in radiomic, genomic, and radiogenomic features. The results demonstrate that genomic data are correlated with the GBM OS prediction, and the radiogenomic model outperforms both radiomic and genomic models. We further illustrate the most significant genes, such as IL1B, KLHL4, ATP1A2, IQGAP2, and TMSL8, which contribute highly to prognosis analysis.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available