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Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 19 May 2022

This article has been updated

Abstract

Objectives

To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas.

Methods

In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance.

Results

In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p < 0.001) than the clinical nomogram.

Conclusions

DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value.

Key Points

• DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation.

• DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images.

• DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.

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Abbreviations

AUC:

Area under the curve

CNN:

Convolutional neural network

GBM:

Glioblastoma

IBS:

Integrated Brier score

IDH:

Isocitrate dehydrogenase

KPS:

Karnofsky performance status

LrGG:

Lower-grade glioma

NRI:

Net reclassification improvement

ROC:

Receiver operating characteristics

TCIA:

The Cancer Imaging Archive

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Funding

This study has received funding from the National Natural Science Foundation of China (No. U20A20171), Guangdong Basic and Applied Basic Research Foundation (2020B1515120046), Youth Innovation Promotion Association of the Chinese Academy of Sciences (2018364), and Guangdong Key Project (2018B030335001).

Author information

Author notes

  1. Zhi-Cheng Li and Jing Yan contributed equally to this work.

Authors and Affiliations

  1. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhi-Cheng Li, Shenghai Zhang & Dong Liang

  2. National Innovation Center for Advanced Medical Devices, Shenzhen, China

    Zhi-Cheng Li & Dong Liang

  3. Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China

    Jing Yan

  4. Department of Neurosurgery, The 3rd Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Chaofeng Liang

  5. Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China

    Xiaofei Lv

  6. Department of Radiology, The 3rd Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Yan Zou

  7. School of Information Engineering, Guangdong Medical University, Dongguan, China

    Huailing Zhang

  8. Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 1 Jian she Dong Road, Zhengzhou, 450052, Henan, China

    Zhenyu Zhang

  9. 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, 651 Dongfeng East Road, Guangzhou, 510060, China

    Yinsheng Chen

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  1. Zhi-Cheng Li
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Corresponding authors

Correspondence to Zhenyu Zhang or Yinsheng Chen.

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The guarantor of this publication is Zhi-Cheng Li.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Zhi-Cheng Li) has significant statistical expertise.

Informed consent

Written informed consent was waved by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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The original online version of this article was revised: the co-first authors Zhi-Cheng Li and Jing Yan were not referenced as equally contributing authors. Additionally, the captions to Figure 3, 4 and 6 were displayed incorrectly.

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Li, ZC., Yan, J., Zhang, S. et al. Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study. Eur Radiol 32, 5719–5729 (2022). https://doi.org/10.1007/s00330-022-08640-7

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  • DOI: https://doi.org/10.1007/s00330-022-08640-7

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