Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model

Xue Fu; Chunxiao Chen; Zhiying Chen; Jie Yu; Liang Wang

doi:10.1515/bmt-2022-0221

Article

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model

Xue Fu , Chunxiao Chen , Zhiying Chen , Jie Yu and Liang Wang

Published/Copyright: September 4, 2024

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From the journal Biomedical Engineering / Biomedizinische Technik Volume 69 Issue 6

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.

Keywords: survival prediction; radiogenomics; deep learning; multi-task; small sample

Corresponding author: Chunxiao Chen, Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China, E-mail: ccxbme@nuaa.edu.cn

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/.

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.
Informed consent: Informed consent was obtained from all individuals included in this study.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Research funding: This study was supported by the Natural Science Foundation of China (Grant No. 12071215).

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Received: 2022-06-06

Accepted: 2024-08-21

Published Online: 2024-09-04

Published in Print: 2024-12-17

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Articles in the same Issue

https://doi.org/10.1515/bmt-2022-0221

Keywords for this article

survival prediction; radiogenomics; deep learning; multi-task; small sample