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Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients

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Abstract

Purpose

Although glioblastoma (GBM) is the most common primary brain malignancy, few tools exist to pre-operatively risk-stratify patients by overall survival (OS) or common genetic alterations. We developed an MRI-based radiomics model to identify patients with EGFR amplification, MGMT methylation, GBM subtype, and OS greater than 12 months.

Methods

We retrospectively identified 235 patients with pathologically confirmed GBMs from the Cancer Genome Atlas (88; TCGA) and MD Anderson Cancer Center (147; MDACC). After two neuroradiologists segmented MRI tumor volumes, we extracted first-order and second-order radiomic features (gray-level co-occurrence matrices). We used the Maximum Relevance Minimum Redundancy technique to identify the 100 most relevant features and validated models using leave-one-out-cross-validation and validation on external datasets (i.e., TCGA). Our results were reported as the area under the curve (AUC).

Results

The MDACC patient cohort had significantly higher OS (22 months) than the TCGA dataset (14 months). On both LOOCV and external validation, our radiomics models were able to identify EGFR amplification (all AUCs > 0.83), MGMT methylation (all AUCs > 0.85), GBM subtype (all AUCs > 0.92), and OS (AUC > 0.91 on LOOCV and 0.71 for TCGA validation).

Conclusions

Our robust radiomics pipeline has the potential to pre-operatively discriminate common genetic alterations and identify patients with favorable survival.

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Author notes

  1. Mathew Pease and Zachary C. Gersey have contributed equally to this work.

  2. R. R. Colen and P. O. Zinn are senior authors have contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, 200 Lothrop St., Suit 400B, Pittsburgh, PA, 15213, USA

    Mathew Pease, Zachary C. Gersey & P. O. Zinn

  2. Department of Diagnostic Radiology, UPMC, Pittsburgh, PA, USA

    Murat Ak, Serafettin Zenkin, Priyadarshini Mamindla & R. R. Colen

  3. Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Ahmed Elakkad, Aikaterini Kotrotsou, Nabil Elshafeey, Vinodh A. Kumar & Ashok J. Kumar

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Contributions

MA., AE, AK, SZ, NE, KV, KA, helped with data collection and segmentation. MP, ZCG, RRC, POZ, PM helped with analysis of data and interpretation of results. MP, ZCG, RRC, POZ wrote the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to P. O. Zinn.

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Pease, M., Gersey, Z.C., Ak, M. et al. Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients. J Neurooncol 160, 253–263 (2022). https://doi.org/10.1007/s11060-022-04150-0

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