Survival analysis of patients with high-grade gliomas based on data mining of imaging variables

Evangelia I. Zacharaki, N. Morita, P. Bhatt, D. M. O'Rourke, E. R. Melhem, C. Davatzikos

Research output: Contribution to journalArticle

Abstract

BACKGROUND AND PURPOSE: The prediction of prognosis in HGGs is poor in the majority of patients. Our aim was to test whether multivariate prediction models constructed by machine-learning methods provide a more accurate predictor of prognosis in HGGs than histopathologic classification. The prediction of survival was based on DTI and rCBV measurements as an adjunct to conventional imaging. MATERIALS AND METHODS: The relationship of survival to 55 variables, including clinical parameters (age, sex), categoric or continuous tumor descriptors (eg, tumor location, extent of resection, multi-focality, edema), and imaging characteristics in ROIs, was analyzed in a multivariate fashion by using data-mining techniques. A variable selection method was applied to identify the overall most important variables. The analysis was performed on 74 HGGs (18 anaplastic gliomas WHO grades III/IV and 56 GBMs or gliosarcomas WHO grades IV/IV). RESULTS: Five variables were identified as the most significant, including the extent of resection, mass effect, volume of enhancing tumor, maximum B0 intensity, and mean trace intensity in the nonenhancing/edematous region. These variables were used to construct a prediction model based on a J48 classification tree. The average classification accuracy, assessed by cross-validation, was 85.1%. Kaplan-Meier survival curves showed that the constructed prediction model classified malignant gliomas in a manner that better correlates with clinical outcome than standard histopathology. CONCLUSIONS: Prediction models based on data-mining algorithms can provide a more accurate predictor of prognosis in malignant gliomas than histopathologic classification alone.

Original languageEnglish (US)
Pages (from-to)1065-1071
Number of pages7
JournalAmerican Journal of Neuroradiology
Volume33
Issue number6
DOIs
StatePublished - Jun 1 2012

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

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    Zacharaki, E. I., Morita, N., Bhatt, P., O'Rourke, D. M., Melhem, E. R., & Davatzikos, C. (2012). Survival analysis of patients with high-grade gliomas based on data mining of imaging variables. American Journal of Neuroradiology, 33(6), 1065-1071. https://doi.org/10.3174/ajnr.A2939