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Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system

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An Editorial Comment to this article was published on 07 August 2021

Abstract

Objectives

To determine reproducible MRI parameters predictive of molecular subtype and risk stratification in glioma and develop a structured reporting system.

Methods

All study patients were initially diagnosed with glioma, 141 from the Cancer Genome Atlas and 131 from our tertiary institution, as training and validation sets, respectively. Images were analyzed by three neuroradiologists with 1–7 years of experience. MRI features including contrast enhancement pattern, necrosis, margin, edema, T2/FLAIR mismatch, internal cyst, and cerebral blood volume higher than normal cortex were reported using a structured reporting system. The pathology was stratified into five risk types: (1) oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, 1p19q co-deleted; (2) diffuse astrocytoma, IDH-mutant, grade II–III; (3) glioblastoma, IDH-mutant, grade IV; (4) diffuse astrocytoma, IDH-wild, grade II–III; and (5) glioblastoma, IDH-wild, grade IV. Significant predictors were selected using multivariate logistic regression, and diagnostic performance was tested using a validation set.

Results

Reproducible imaging parameters exhibiting > 50% agreement across readers included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. In the validation set, prediction of risk type 5 exhibited the highest diagnostic performance with AUCs of 0.92 (reader 1) and 0.93 (reader 2) with predominant enhancement, followed by risk type 2 with AUCs of 0.95 and 0.95 with T2/FLAIR mismatch sign and no necrosis, and risk type 1 with AUCs of 0.84 and 0.83 with internal cyst or necrosis. Risk types 3 and 4 were difficult to visually predict.

Conclusions

Imaging parameters with high reproducibility enabling prediction of IDH-wild-type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant diffuse astrocytoma were identified.

Key Points

• Reproducible MRI parameters for determining molecular subtypes of glioma included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement.

• IDH-wild type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant low-grade astrocytoma were identified using MRI parameters with high inter-reader reproducibility.

• Identification of IDH-wild type low-grade glioma and IDH-mutant glioblastoma was difficult by visual analysis.

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Abbreviations

1p/19q:

Chromosome arms 1p and 19q

CBV:

Cerebral blood volume

FLAIR:

Fluid-attenuated inversion recovery

IDH:

Isocitrate dehydrogenase

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Imaging Archive

VASARI:

Visually AcceSAble Rembrandt Images

WHO:

World Health Organization

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Funding

This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (grant numbers: NRF-2020R1A2B5B01001707 and NRF-2020R1A2C4001748).

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Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Korea

    Yeo Kyung Nam, Ji Eun Park, Minjae Kim & Ho Sung Kim

  2. Department of Statistics and Data Science, Korea National Open University, Seoul, Korea

    Seo Young Park

  3. Department of Radiology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

    Minkyoung Lee

  4. Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, Korea

    Soo Jung Nam

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  1. Yeo Kyung Nam
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Correspondence to Ji Eun Park.

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The scientific guarantor of this publication is Ho Sung Kim.

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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 has significant statistical expertise (Seo Young Park, 8 years of experienced statistician).

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• retrospective

• cross-sectional study

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Nam, Y.K., Park, J.E., Park, S.Y. et al. Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system. Eur Radiol 31, 7374–7385 (2021). https://doi.org/10.1007/s00330-021-08015-4

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