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Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma

  • Magnetic Resonance
  • Published:
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Abstract

Objective

To develop and validate a volume-based, quantitative imaging marker by integrating multi-parametric MR images for predicting glioblastoma survival, and to investigate its relationship and synergy with molecular characteristics.

Methods

We retrospectively analysed 108 patients with primary glioblastoma. The discovery cohort consisted of 62 patients from the cancer genome atlas (TCGA). Another 46 patients comprising 30 from TCGA and 16 internally were used for independent validation. Based on integrated analyses of T1-weighted contrast-enhanced (T1-c) and diffusion-weighted MR images, we identified an intratumoral subregion with both high T1-c and low ADC, and accordingly defined a high-risk volume (HRV). We evaluated its prognostic value and biological significance with genomic data.

Results

On both discovery and validation cohorts, HRV predicted overall survival (OS) (concordance index: 0.642 and 0.653, P < 0.001 and P = 0.038, respectively). HRV stratified patients within the proneural molecular subtype (log-rank P = 0.040, hazard ratio = 2.787). We observed different OS among patients depending on their MGMT methylation status and HRV (log-rank P = 0.011). Patients with unmethylated MGMT and high HRV had significantly shorter survival (median survival: 9.3 vs. 18.4 months, log-rank P = 0.002).

Conclusion

Volume of the high-risk intratumoral subregion identified on multi-parametric MRI predicts glioblastoma survival, and may provide complementary value to genomic information.

Key points

• High-risk volume (HRV) defined on multi-parametric MRI predicted GBM survival.

• The proneural molecular subtype tended to harbour smaller HRV than other subtypes.

• Patients with unmethylated MGMT and high HRV had significantly shorter survival.

• HRV complements genomic information in predicting GBM survival

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Authors and Affiliations

  1. Department of Radiation Oncology, Stanford University, 1070 Arastradero Rd., Palo Alto, CA, 94304, USA

    Yi Cui, Jia Wu & Ruijiang Li

  2. Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido, Japan

    Yi Cui, Khin Khin Tha, Hiroki Shirato & Ruijiang Li

  3. School of Electrical Engineering and Automation, Tianjin University, Tianjin Shi, China

    Shangjie Ren

  4. Department of Radiology and Nuclear Medicine, Hokkaido University, Hokkaido, Japan

    Khin Khin Tha & Hiroki Shirato

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Correspondence to Yi Cui.

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Guarantor

The scientific guarantor of this publication is Dr. Ruijiang 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.

Funding

This research was partially funded by the NIH (grant number: R01 CA193730), and partially supported by the Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, founded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. Part of the data used in this research was obtained from The Cancer Imaging Archive (TCIA) sponsored by the Cancer Imaging Program, DCTD/NCI/NIH.

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One of the authors has significant statistical expertise.

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Institutional Review Board approval was obtained.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

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• multicentre study

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Cui, Y., Ren, S., Tha, K.K. et al. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 27, 3583–3592 (2017). https://doi.org/10.1007/s00330-017-4751-x

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