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  • Clinically Applicable and Biologically Validated MRI Radiomic Test Method Predicts Glioblastoma Genomic Landscape and Survival

    Final Number:

    Pascal O Zinn MD, PhD; Sanjay K Singh PhD; Aikaterini Kotrotsou; Faramak Zandi; Ginu Thomas; Masumeh Hatami; Markus M Luedi MD; Ahmed Elakkad; Islam Hassan; Joy Gumin BS; Erik P Sulman MD, PhD; Frederick F. Lang MD; Rivka R. Colen MD

    Study Design:
    Laboratory Investigation

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2016 Annual Meeting

    Introduction: Imaging is the modality of choice for non-invasive characterization of biologic tissue and organ systems; imaging serves as early diagnostic tool for most disease processes and is rapidly evolving; thus transforming the way we diagnose and follow patients over time. A vast number of cancer imaging characteristics have been correlated to underlying genomics; however, none have established causality. Therefore, our objectives were to test if there is a causal relationship between imaging and genomic information; and, to develop a clinically relevant radiomic pipeline for glioblastoma molecular characterization.

    Methods: Functional validation was performed using a prototypic in-vivo RNA-interference based orthotopic xenograft mouse model. The automated pipeline collects 4800 MRI-derived texture features per tumor. Using univariate feature selection and boosted tree predictive modeling, a patient specific genomic probability map was derived and patient survival predicted (TCGA/ MD Anderson datasets).

    Results: Data demonstrated a significant xenograft to human association (AUC 84%, P<0.001). Further, EGFR amplification (AUC 86%, P<0.0001), MGMT methylation/expression (AUC92%, P=0.001), GBM molecular subgroups (AUC 88%, P=0.001), and survival in two independent datasets (AUC 90%, P<0.001) was predicted.

    Conclusions: Our results for the first time illustrate a causal relationship between imaging features and genomic tumor composition. We present a directly clinically applicable analytical imaging method termed Radiome Sequencing to allow for automated image analysis, prediction of key genomic events and survival. This method is scalable and applicable to any type of medical imaging. Further, it allows for human-mouse matched co-clinical trials, in depth endpoint analysis, and upfront noninvasive high-resolution radiomics-based diagnostic, prognostic, and predictive biomarker development.

    Patient Care: Our newly developed and in-vivo mechanistically validated method allows for upfront noninvasive diagnostics for GBM genomic hallmarks and survival. Further it will facilitate biomarker development and co-clinical trial design.

    Learning Objectives: 1. to understand clincical implication of radiomic GBM characterization 2. to understand causality in radiogenomics using a preclinical orthotopic mouse model 2. texture analysis in brain tumors


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