Abstract:
Patients with glioblastoma (GBM) have a poor survival rate. In order to facilitate early interventions and personalized therapeutic treatment, there is a pressing need fo...Show MoreMetadata
Abstract:
Patients with glioblastoma (GBM) have a poor survival rate. In order to facilitate early interventions and personalized therapeutic treatment, there is a pressing need for employing routine non-invasive MRI for preoperative GBM survival prediction. In this paper, we investigate to what extent regional radiomics similarity networks (R2SNs) can be used to predict overall survival (OS) time in GBM. Different from the widely used MRI-derived radiomics features that focus on single or several brain regions independently, the R2SNs can take into account the potential associations among brain regions with radiomics similarity for improved survival prediction. Specifically, we first introduce a distance correlation based R2SN (DC-R2SN), where distance correlation (instead of Pearson’s correlation in the traditional R2SN) is adopted to measure the more complex interactions between a pair of brain regions defined by the corresponding radiomics features. A graph neural network (GNN) framework is then proposed for fusing DCR2SNs and clinical data to predict OS time of GBM patients. Experimental results on the publicly available UPenn-GBM database demonstrate the effectiveness of our proposed GNN based survival prediction framework with the DC-R2SNs.
Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 14-19 April 2024
Date Added to IEEE Xplore: 18 March 2024
ISBN Information:
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- IEEE Keywords
- Index Terms
- Brain Networks ,
- Predictor Of Survival ,
- Data Fusion ,
- Graph Neural Networks ,
- Pearson Correlation ,
- Magnetic Resonance Imaging ,
- Overall Survival ,
- Brain Regions ,
- Early Treatment ,
- Overall Survival Time ,
- Glioblastoma Patients ,
- Correlation Distance ,
- Radiomic Features ,
- Single Brain Region ,
- Pairs Of Brain Regions ,
- Clinical Characteristics ,
- Support Vector Machine ,
- Loss Of Generality ,
- Feature Representation ,
- Fully-connected Layer ,
- Graph Attention Network ,
- Graph Attention ,
- Functional Network Connectivity ,
- Dense Connections ,
- Gross Total Resection ,
- Min-max Scaling ,
- Sparse Graph ,
- Fusion Strategy ,
- Short-term Survival ,
- Radiomic Feature Extraction
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Brain Networks ,
- Predictor Of Survival ,
- Data Fusion ,
- Graph Neural Networks ,
- Pearson Correlation ,
- Magnetic Resonance Imaging ,
- Overall Survival ,
- Brain Regions ,
- Early Treatment ,
- Overall Survival Time ,
- Glioblastoma Patients ,
- Correlation Distance ,
- Radiomic Features ,
- Single Brain Region ,
- Pairs Of Brain Regions ,
- Clinical Characteristics ,
- Support Vector Machine ,
- Loss Of Generality ,
- Feature Representation ,
- Fully-connected Layer ,
- Graph Attention Network ,
- Graph Attention ,
- Functional Network Connectivity ,
- Dense Connections ,
- Gross Total Resection ,
- Min-max Scaling ,
- Sparse Graph ,
- Fusion Strategy ,
- Short-term Survival ,
- Radiomic Feature Extraction
- Author Keywords