Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model
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Xue Fu
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Zhiying Chen
Abstract
In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 12071215
Acknowledgments
This study was supported by the Natural Science Foundation of China (Grant No. 12071215). The results here are in whole based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.
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Research ethics: This article does not contain any studies with human participants performed by any of the authors. The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Research funding: This study was supported by the Natural Science Foundation of China (Grant No. 12071215).
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Artikel in diesem Heft
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Artikel in diesem Heft
- Frontmatter
- Reviews
- Perception of defecation intent: applied methods and technology trends
- Anatomic variability of the human femur and its implications for the use of artificial bones in biomechanical testing
- Research Articles
- The intensity of subacute local biological effects after the implantation of ALBO-OS dental material based on hydroxyapatite and poly(lactide-co-glycolide): in vivo evaluation in rats
- Comparison of fatigue lifetime of new generation CAD/CAM crown materials on zirconia and titanium abutments in implant-supported crowns: a 3D finite element analysis
- Breaking the silence: empowering the mute-deaf community through automatic sign language decoding
- The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms
- A multi-chamber soft robot for transesophageal echocardiography: continuous kinematic matching control of soft medical robots
- Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model
