Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI

Purpose: The identification of prognostic biomarkers plays a pivotal role in the management of glioblastoma. The aim of this study was to assess the role of magnetic resonance dynamic susceptibility contrast imaging (DSC-MRI) with histogram analysis in the prognostic evaluation of patients suffering from glioblastoma. Materials and methods: Sixty-eight patients with newly diagnosed pathologically verified GBM were retrospectively evaluated. All patients underwent MRI investigations, including DSC-MRI, surgical procedure and received postoperative focal radiotherapy plus daily temozolomide (TMZ), followed by adjuvant TMZ therapy. Relative cerebral blood volume (rCBV) histograms were generated from a volume of interest covering the solid portions of the tumor and statistically evaluated for kurtosis, skewness, mean, median and maximum value of rCBV. To verify if histogram parameters could predict survival at 1 and 2 years, receiver operating characteristic (ROC) curves were obtained. Kaplan–Meier method was used to calculate patient’s overall survival. Results: rCBV kurtosis and rCBV skewness showed significant differences between subjects surviving > 1 and > 2 years, According to ROC analysis, the rCBV kurtosis showed the best statistic performance compared to the other parameters; respectively, values of 1 and 2.45 represented an optimised cut-off point to distinguish subjects surviving over 1 or 2 years. Kaplan–Meier curves showed a significant difference between subjects with rCBV kurtosis values higher or lower than 1 (respectively 1021 and 576 days; Log-rank test: p = 0.007), and between subjects with rCBV kurtosis values higher or lower than 2.45 (respectively 802 and 408 days; Log-rank test: p = 0.001). Conclusion: The histogram analysis of perfusion MRI proved to be a valid method to predict survival in patients affected by glioblastoma.