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Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

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

Abstract

Objectives

To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

Methods

The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS.

Results

The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01).

Conclusions

Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient’s prognosis and guiding individualized treatment.

Key Points

• MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics.

• DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation.

• The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

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Data availability

The RNA-seq and WES data has been deposited into Genome Sequence Archive (GSA) under accession code HRA000802 (https://ngdc.cncb.ac.cn/gsa-human/submit/hra/subHRA001168/finishedOverview), HRA000932 (https://ngdc.cncb.ac.cn/gsa-human/submit/hra/subHRA001358/finishedOverview), and HRA000972 (https://ngdc.cncb.ac.cn/gsa-human/submit/hra/subHRA001402/finishedOverview). The image and survival data from FAHZZU, NFHNFMU, HPH, TAHSYSU, and GGHGMC are not publicly available due to patient privacy constraints, but are available upon reasonable request from the corresponding authors (Zhenyu Zhang, Zhicheng Li, and Jingliang Cheng).

Abbreviations

AI:

Artificial intelligence

CAM:

Class activation map

CGGA:

Chinese Glioma Genome Atlas

CHT:

Chemotherapy

CI:

Confidence interval

CNN:

Convolutional neural network

CNV:

Copy number variation

DEG:

Differentially expressed gene

DLIS:

Deep learning imaging signature

FAHZZU:

First Affiliated Hospital of Zhengzhou University

FDR:

False discovery rate

GBM:

Glioblastoma multiforme

GGHGMC:

Guangzhou General Hospital of Guangzhou Military Command

GSVA:

Gene set variation analysis

HPH:

Henan Provincial Hospital

HR:

Hazard ratio

IDH:

Isocitrate dehydrogenase

KPS:

Karnofsky performance status

NFHNFMU:

Nangfang Hospital of Nangfang Medical University

NRI:

Net reclassification improvement

OS:

Overall survival

RB:

Retinoblastoma

RNA-seq:

RNA sequencing

RT:

Radiation therapy

RTK:

Receptor tyrosine kinase

SNV:

Single nucleotide variant

TAHSYSU:

The 3rd Affiliated Hospital of Sun Yat-Sen University

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Imaging Archive

WES:

Whole-exome sequencing

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Code availability

The Python codes for implementation of the CNN used to calculate the DLIS score, as well as the validation imaging data, were deposited into a publicly accessible repository available at https://doi.org/10.24433/CO.5979724.v1.

Funding

This study has received funding by the Key-Area Research and Development Program of Guangdong Province (2021B0101420006), the National Natural Science Foundation of China (Nos. U20A20171, 82102149, 61901458, 61571432, 81702465, 8217111948, 82173090, U1804172, U1904148), the Excellent Youth Talent Cultivation Program of Innovation in Health Science and Technology of Henan Province (No. YXKC2022061), the Key Program of Medical Science and Technique Foundation of Henan Province (No. SBGJ202002062), the Joint Construction Program of Medical Science and Technique Foundation of Henan Province (No. LHGJ20190156), the Science and Technology Program of Henan Province (No. 202102310136, 202102310138, 202102310113, 202102310083), Guangdong Basic and Applied Basic Research Foundation (2020B1515120046), and Guangdong Key Project (2018B030335001).

Author information

Author notes

  1. Jing Yan, Qiuchang Sun and Xiangliang Tan contributed equally to this work.

Authors and Affiliations

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

    Jing Yan & Jingliang Cheng

  2. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Qiuchang Sun, Tianhao Mu, Yuanshen Zhao, Jingxian Duan, Shifu Chen & Zhi-Cheng Li

  3. University of Chinese Academy of Sciences, Beijing, China

    Qiuchang Sun & Zhi-Cheng Li

  4. Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China

    Xiangliang Tan, Qiaoli Yao & Yikai Xu

  5. Department of Neurosurgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China

    Chaofeng Liang

  6. Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, 510010, China

    Hongmin Bai

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

    Wenchao Duan, Yuning Qiu, Dongling Pei, Chen Sun, Wenqing Wang, Zhen Liu, Xuanke Hong, Xiangxiang Wang, Yu Guo, Xianzhi Liu & Zhenyu Zhang

  8. HaploX Biotechnology, Shenzhen, Guangdong, China

    Tianhao Mu, Danni Liu & Shifu Chen

  9. Department of Neurosurgery, Henan Provincial Hospital, Zhengzhou, 450052, Henan Province, China

    Yang Guo

  10. Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan Province, China

    Weiwei Wang

  11. Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518045, China

    Zhi-Cheng Li

Authors
  1. Jing Yan
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Corresponding authors

Correspondence to Jingliang Cheng, Zhi-Cheng Li or Zhenyu Zhang.

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Guarantor

The scientific guarantor of this publication is Zhenyu Zhang.

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 consents were obtained from patients whose fresh tumor specimens were used for RNA-seq and WES. For the rest patients, written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multi-center study

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Yan, J., Sun, Q., Tan, X. et al. Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study. Eur Radiol 33, 904–914 (2023). https://doi.org/10.1007/s00330-022-09066-x

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