Manal Nicolasjilwan1, Ying Hu2, Chunhua Yan2, Daoud Meerzaman2, Chad A Holder3, David Gutman4, Rajan Jain5, Rivka Colen6, Daniel L Rubin7, Pascal O Zinn8, Scott N Hwang9, Prashant Raghavan1, Dima A Hammoud10, Lisa M Scarpace11, Tom Mikkelsen11, James Chen12, Olivier Gevaert13, Kenneth Buetow14, John Freymann15, Justin Kirby15, Adam E Flanders16, Max Wintermark17. 1. Division of Neuroradiology, University of Virginia Health System, Charlottesville, VA, United States. 2. Center for Biomedical Informatics & Information Technology, National Cancer Institute, Bethesda, MD, United States. 3. Department of Radiology and Imaging Sciences Division of Neuroradiology, Emory University School of Medicine, Atlanta, GA, United States. 4. Department of Biomedical Informatics, Emory University, Atlanta, GA, United States. 5. Departments of Radiology and Neurosurgery, Henry Ford, Detroit, MI, United States. 6. Division of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States. 7. Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, United States. 8. Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States. 9. Neuroradiology Section, St. Jude Children's Research Hospital, Memphis, TN, United States. 10. Radiology and Imaging Sciences, National Institutes of Health, Clinical Center, Bethesda, MD, United States. 11. Departments of Neurosurgery, Henry Ford, Detroit, MI, United States. 12. Division of Neuroradiology, University of California, San Diego, CA, United States. 13. Center for Cancer Systems Biology (CCSB) & Department of Radiology, Stanford University, Stanford, CA, United States. 14. Arizona State University Life Science, Tempe, AZ, United States. 15. SAIC-Frederick, Inc., Frederick, MD, United States. 16. Division of Neuroradiology, Thomas Jefferson University Hospital, Philadelphia, PA, United States. 17. Division of Neuroradiology, University of Virginia Health System, Charlottesville, VA, United States; CHU de Vaudois, Department of Radiology, Lausanne, Switzerland. Electronic address: max.wintermark@gmail.com.
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.
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.
Authors: E Verger; I Valduvieco; Ll Caral; T Pujol; T Ribalta; N Viñolas; T Boget; L Oleaga; Y Blanco; F Graus Journal: Clin Transl Oncol Date: 2011-10 Impact factor: 3.405
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