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Sci Rep. 2017 Sep 4;7(1):10353. doi: 10.1038/s41598-017-10649-8.

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Author information

1
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China.
2
Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
3
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. zc.li@siat.ac.cn.
4
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
5
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
6
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China. zhaiguangtao@sjtu.edu.cn.

Abstract

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.

PMID:
28871110
PMCID:
PMC5583361
DOI:
10.1038/s41598-017-10649-8
[Indexed for MEDLINE]
Free PMC Article

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