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Proceedings Paper

Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways
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Paper Abstract

Over 40% of Glioblastoma (GBM) patients do not respond to conventional chemo-radiation therapy (chemo-RT) and relapse within 6-9 months, suggesting that they may have been better suited for other targeted therapies. Currently, there are no biomarkers that can reliably predict patients' response to chemo-RT in GBM. We seek to evaluate the role of radiomic markers on pre-treatment MRI to predict GBM patients' response to chemo-RT. Further, to establish a biological underpinning of the radiomic markers, we identified radiogenomic correlates of the radiomic markers with signaling pathways that are known to impact chemo-RT response. A total of 49 studies with Gd-T1w, T2w, FLAIR MRI protocols and corresponding gene expression were obtained from Ivy GAP (n=29) and TCIA (n=20) databases. Responders (n=22) were patients with progression-free survival (PFS) of at least ≥ 6 months, while non-responders (n=27) had PFS < 6 months. 13 molecular pathways were curated from the MSigDB Hallmark gene set. For each study, enhancing tumor on MRI was manually segmented by an expert reader. 1390 3D-radiomic features (Gabor, Haralick, and Laws energy) were extracted from this region across all MRI protocols. Joint mutual information identified the 3 most predictive radiomic features in the training set (n=29). This was followed by correlating these features with the gene set enrichment analysis (GSEA) score computed for every pathway. A support vector machine (SVM) classifier was trained using these 3 features and validated on a test set (n=20) that resulted in an Area Under Curve (AUC) of 0.71 to distinguish chemo-RT responders from non-responders. Laws energy descriptor (characterizing appearance of edges, spots, and ripples) from the enhancing region on Gd-T1w MR images were found to best predict chemo-RT response. Radiogenomic correlation with GSEA scores revealed that these radiomic features were significantly associated with PI3K/AKT/mTOR (promotes cell proliferation, survival) and apoptosis (programmed cell death) signaling pathways (p < 0.03, False Discovery Rate = 5%).

Paper Details

Date Published: 13 March 2019
PDF: 12 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501B (13 March 2019); doi: 10.1117/12.2512258
Show Author Affiliations
Niha Beig, Case Western Reserve Univ. (United States)
Prateek Prasanna, Case Western Reserve Univ. (United States)
Virginia Hill, Northwestern Univ. (United States)
Ruchika Verma, Case Western Reserve Univ. (United States)
Vinay Varadan, Case Western Reserve Univ. (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)
Louis Stokes Cleveland Veterans Administration Medical Ctr. (United States)
Pallavi Tiwari, Case Western Reserve Univ. (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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