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Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Purpose

We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

Methods

This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

Results

Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

Conclusions

MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

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Data availability

VASARI feature data are available by request to the corresponding author. Image data is the property of each institution.

Code availability

Code is available by request after approval by all authors.

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Funding

This study was funded by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Science, Information, and Communication Technologies & Future Planning (Grant Nos. 2017R1D1A1B03030440 and 2020R1A2C1003886).

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Authors and Affiliations

  1. Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, South Korea

    Sung Soo Ahn, Yae Won Park, Kyunghwa Han & Seung-Koo Lee

  2. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    Sung Soo Ahn, Chansik An & Soonmee Cha

  3. Department of Radiology and Research Institute, National Health Insurance Service Ilsan Hospital, Goyang, South Korea

    Chansik An

  4. Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea

    Jong Hee Chang

  5. Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea

    Se Hoon Kim

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Contributions

All authors contributed to the study conception and design. SSA, SC and CA designed the study. SSA, SC, JHC, and SHK assisted in data acquisition and compiled the database. CA and KH conducted the data preprocessing and statistical analysis. SSA wrote the first draft of the manuscript, and SC provided the critical revision of the manuscript. All authors contributed to and approved the final manuscript.

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Correspondence to Soonmee Cha.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this retrospective study, formal consent was not required.

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Ahn, S.S., An, C., Park, Y.W. et al. Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma. J Neurooncol 154, 83–92 (2021). https://doi.org/10.1007/s11060-021-03801-y

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