Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival

Neuro-Oncology Neuro-Oncology 17(11), 1525 – 1537, 2015 doi:10.1093/neuonc/nov117 Advance Access date 22 July 2015 Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival Departments of Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas (P.W., M.H., J.W., G.T., A.S.B., M.M.L., R.R.C.); Department of Radiology, Neuroradiology Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (P.W.); Department of Radiology, Division of Neuroradiology/ENT, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania (A.F.); Bioinformatics Analyst III, Clinical Monitoring Research Program (CMRP), Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Rockville, Maryland (J.K.); Department of Radiology, Neuroradiology Division, Stanford University, Stanford, California (M.W.); Cancer Research, Sage Bionetworks, Seattle, Washington (E.S.H.); Department of Diagnostic and Interventional Imaging, University of Texas Health Sciences Center, Houston, Texas (S.S.H.); Department of Radiology, Stanford University, Stanford, California (D.L.R.); Department of Radiology, University of California San Diego, San Diego, California (J.Y.C.); Neuroradiology Section, St Jude Children’s Research Hospital, Memphis, Tennessee (S.N.H.); Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., Rockville, Maryland (J.F.); Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia (C.A.H.); Department of Neurosurgery, Baylor College of Medicine, Houston, Texas (P.O.Z.); Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas (P.O.Z.); Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas (R.R.C.) Corresponding Author: Rivka R. Colen, MD, UT MD Anderson Cancer Center, Department of Diagnostic Radiology, Neuroradiology division, Room FCT16.5037 – Unit 1482, 1400 Pressler Street, Houston, TX 77030 (). Background. Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM. Methods. We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image. Results. Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P ¼ .03) and eloquent brain involvement (P , .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm3 and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps ¼ .004 and .003, respectively). Conclusions. Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials. Keywords: glioblastoma, imaging, overall survival, progression free survival, TCGA. Received 25 March 2015; accepted 28 May 2015 # The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: . 1525 Pattana Wangaryattawanich, Masumeh Hatami, Jixin Wang, Ginu Thomas, Adam Flanders, Justin Kirby, Max Wintermark, Erich S. Huang, Ali Shojaee Bakhtiari, Markus M. Luedi, Syed S. Hashmi, Daniel L. Rubin, James Y. Chen, Scott N. Hwang, John Freymann, Chad A. Holder, Pascal O. Zinn, and Rivka R. Colen Wangaryattawanich et al.: Imaging predictors of overall and progression-free survival in GBM 1526 defined by Zinn et al. 34 This study nonselectively reports the entire standardized qualitative imaging parameters of the dataset of TCIA and associated volumetrics with regard to correlates and predictors of both OS and PFS. Methods Patient Population and Clinical Variables In this study, we used the original material and data provided by TCGA, which is a publicly available resource containing multidimensional genomic and clinical information on GBM and other cancers.31 The project in TCGA was conducted in compliance with regulations and policies for the protection of human subjects, and approvals by institutional review boards were appropriately obtained. The preoperative MRIs of the corresponding patients of the project in TCGA were made available for public download from TCIA, which was established by the collaboration between NCI and multiple institutions in the United States.32 We retrospectively identified 94 treatment-naı̈ve GBM patients from TCGA who had both clinical and imaging data available. The clinical variables consisted of age, gender, and Karnofsky performance status (KPS). Qualitative and Semi-quantitative Imaging Analysis The qualitative and semi-quantitative imaging dataset annotations were based upon the VASARI feature set for human glioma. This comprehensive feature set contains standardized terminologies of the most common features used to describe primary cerebral neoplasia on standard pre- and postcontrast enhanced MRI.25,31 – 33 The open-source PACS (picture archiving and communication system) workstation, the ClearCanvas platform (http://www.clearcanvas.ca/), was used for imaging assessments. Board-certified neuroradiologists (C.A.H., 16 y experience; S.N.H., 6 y; M.W., 7 y; P.R., 5 y; R.R.C., 3 y; and M.J., 4 y) were recruited and trained in the use of the feature set. A minimum of 3 different VASARI scores were obtained for each patient. The scores were then collected centrally in the NCI system and subsequently analyzed. The lists of VASARI imaging features, scoring values, and their definitions are summarized in Table 1.33 Quantitative Volumetric Imaging Analysis Image acquisition, volume selection, and sequence definition Preoperative fluid-attenuated inversion recovery (FLAIR) and postcontrast T1-weighted imaging (T1WI) data were downloaded from TCIA and used for segmentation of the 3 different GBM compartments, namely edema/tumor invasion, tumor, and necrosis. The area of peritumoral T2/FLAIR hyperintensity in GBM reflects an admixture of infiltrative tumor and vasogenic edema. (...truncated)