Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients

• Type of Material: Article
• Authors: Manal Nicolasjilwan, University of Virginia Health System; Ying Hu, National Cancer Institute; Chunhua Yan, National Cancer Institute; Daoud Meerzaman, National Cancer Institute; Chad Holder, Emory University; David Gutman, Emory University; Rajan Jain, Henry Ford Hospital; Rivka Colen, The University of Texas MD Anderson Cancer Center; Daniel L. Rubin, Stanford University; Pascal O. Zinn, The University of Texas MD Anderson Cancer Center; Scott N. Hwang, St. Jude Children's Research Hospital; Prashant Raghavan, University of Virginia Health System; Dima A Hammoud, National Institutes of Health; Lisa M Scarpace, Departments of Neurosurgery, Henry Ford; Tom Mikkelsen, Departments of Neurosurgery, Henry Ford; James Chen, University of California San Diego; Olivier Gevaert, Stanford University; Kenneth Buetow, Arizona State University Life Science; John Freymann, SAIC-Frederick, Inc; Justin Kirby, SAIC-Frederick, Inc; Adam E. Flanders, Thomas Jefferson University Hospital; Max Wintermark, University of Virginia Health System
• Language: English
• Date: 2015-07-01
• Publisher: Elsevier Masson
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Elsevier Masson SAS.
• Final Published Version (URL): http://dx.doi.org/10.1016/j.neurad.2014.02.006
• Title of Journal or Parent Work: Journal de Neuroradiologie / Journal of Neuroradiology
• Volume: 42
• Issue: 4
• Start Page: 212
• End Page: 221
• Keywords: Clinical Neurology; MULTIFORME; Magnetic resonance imaging; GLIOMA; Neurosciences & Neurology; Glioblastoma; Survival; PROGRESSION; Radiology, Nuclear Medicine & Medical Imaging; Science & Technology; ASTROCYTOMA; Prognosis; Life Sciences & Biomedicine; THERAPY; CARE; MATTER; Neuroimaging; Genetics; RADIATION
• Research Categories: Health Sciences, Oncology; Health Sciences, Radiology

Abstract

Purpose: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. Methods: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. Results: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679 ± 0.068, Akaike's information criterion 566.7, P< 0.001). Conclusion: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.