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

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, Rivka R. Colen

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Abstract

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 =. 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 =. 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.

Original languageEnglish (US)
Pages (from-to)1525-1537
Number of pages13
JournalNeuro-oncology
Volume17
Issue number11
DOIs
StatePublished - Nov 2015

All Science Journal Classification (ASJC) codes

  • Oncology
  • Clinical Neurology
  • Cancer Research

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