IB-08 COMPUTATIONAL IMAGING FEATURES DERIVED FROM MRI IMAGES OF THE BRAIN CAN DISCRIMINATE IMMUNE SIGNATURE STATUS IN GLIOBLASTOMA MULTIFORME (GBM)

• Published in: Neuro-oncology (Charlottesville, Va.) Vol. 16; no. suppl 5; p. v108
• Main Authors: Narang, S., Rao, G., Heimberger, A., Martinez, J., Rao, A.
• Format: Journal Article
• Language: English
• Published: 01.11.2014
• ISSN: 1522-8517; 1523-5866; 1523-5866
• DOI: 10.1093/neuonc/nou257.8

PURPOSE: To determine which computationally derived imaging features are correlated with immune gene-signature activity in GBM. Recently, we showed that immune-related genes are enriched within molecular subtypes of GBMs. The significance of immune system response to the tumor is becoming relevant as a therapeutic strategy. In this study, we investigated an imaging-based method for determining the immune system response to GBM. METHODS AND MATERIALS: The study was conducted on pre-surgical Contrast enhanced T1-post and T2-FLAIR MRI images from 83 GBM patients from The Cancer Genome Atlas(TCGA) database. The tumors were segmented semi-automatically (MITK toolkit). Image heterogeneity features were extracted for 3D volumes using in-house MatlabTM scripts . Features include three-dimensional statistical, transform-based and model-based features, pertaining to pixel gray-level heterogeneity measures such as energy, entropy, correlationGenetic programming-based models are used to discriminate subjects according to up- or down-regulation of six immune signatures (measured using Gene Set Enrichment Analysis): immune effector, immune suppression, immune effector process, regulation of the immune effector process, positive regulation of immune system process and negative regulation of immune system process. Receiver Operating Characteristic curves (ROC), and true positive/false positive rates (TPR/FPR) were used to assess the performance of the immune signature status classifier. RESULTS: Based on six different gene-sets associated with immune response, we found that image-derived features are capable of accurately predicting immune signature status in GBMs. ROC analysis reveals that image-features are capable of determining up- or down regulation of diverse immune signatures. The true positive rates (TPR) for each of the six immune signatures are 89%, 83%, 80%, 78%, 72% and 69% respectively (with FPR less than 20%). CONCLUSION: This study presents preliminary evidence that MRI image-derived volumetric and texture features are predictive of immune activity in GBM at the molecular level.