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Proceedings Paper

Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma
Author(s): Niha Beig; Jay Patel; Prateek Prasanna; Sasan Partovi; Vinay Varadan; Anant Madabhushi; Pallavi Tiwari
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Paper Abstract

Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor with a median survival of 14 months. Hypoxia is a hallmark trait in GBM that is known to be associated with angiogenesis, tumor growth, and resistance to conventional therapy, thereby limiting treatment options for GBM patients. There is thus an urgent clinical need for non-invasively capturing tumor hypoxia in GBM towards identifying a subset of patients who would likely benefit from anti-angiogenic therapies (bevacizumab) in the adjuvant setting. In this study, we employed radiomic descriptors to (a) capture molecular variations of tumor hypoxia on routine MRI that are otherwise not appreciable; and (b) employ the radiomic correlates of hypoxia to discriminate patients with short-term survival (STS, overall survival (OS) < 7 months), mid-term survival (MTS) (7 months<OS<16 months), and long-term survival (LTS, OS>16 months). A total of 97 studies (25 STS, 36 MTS, 36 LTS) with Gadolinium T1-contrast (Gd-T1c), T2w, and FLAIR protocols with their corresponding gene expression profiles were obtained from the cancer genome atlas (TCGA) database. For each MRI study, necrotic, enhancing tumor, and edematous regions were segmented by an expert. A total of 30 radiomic descriptors (i.e. Haralick, Laws energy, Gabor) were extracted from every region across all three MRI protocols. By performing unsupervised clustering of the expression profile of hypoxia associated genes, a "low", "medium", or "high" index was defined for every study. Spearman correlation was then used to identify the most significantly correlated MRI features with the hypoxia index for every study. These features were further used to categorize each study as STS, MTS, and LTS using Kaplan-Meier (KM) analysis. Our results revealed that the most significant features (p < 0.05) were identified as Laws energy and Haralick features that capture image heterogeneity on FLAIR and Gd-T1w sequences. We also found these radiomic features to be significantly associated with survival, distinguishing MTS from LTS (p=.005) and STS from LTS (p=.0008).

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341U (3 March 2017); doi: 10.1117/12.2255694
Show Author Affiliations
Niha Beig, Case Western Reserve Univ. (United States)
Jay Patel, Case Western Reserve Univ. (United States)
Prateek Prasanna, Case Western Reserve Univ. (United States)
Sasan Partovi, Case Western Reserve Univ. (United States)
Vinay Varadan, Case Western Reserve Univ. (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)
Pallavi Tiwari, Case Western Reserve Univ. (United States)
Carl E. Ravin Advanced Imaging Labs.

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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