Comparison of region‐of‐interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas

Abstract

Purpose:To compare routine ROI analysis and three different histogram analyses in the grading of glial neoplasms. The hypothesis is that histogram methods can provide a robust and objective technique for quantifying perfusion data in brain gliomas. Current region-of-interest (ROI)-based methods for the analysis of dynamic susceptibility contrast perfusion magnetic resonance imaging (DSC MRI) data are operator-dependent. Materials and Methods:A total of 92 patients underwent conventional and DSC MRI. Multiple histogram metrics were obtained for cerebral blood flow (CBF), cerebral blood volume (CBV), and relative CBV (rCBV) maps using tumoral (T), peritumoral (P), and total tumoral (TT) analysis. Results were compared to histopathologic grades. Statistical analysis included Mann-Whitney (MW) tests, Spearman rank correlation coefficients, logistic regression, and McNemar tests. Results:The maximum value of rCBV (rCBV max ) showed highly significant correlation with glioma grade (r ϭ 0.734, P Ͻ 0.001). The strongest histogram correlations (P Ͻ 0.0001) occurred with rCBV T SD (r ϭ 0.718), rCBV P SD 25 (r ϭ 0.724) and rCBV TT SD 50 (r ϭ 0.685). Multiple rCBV T , rCBV P , and rCBV TT histogram metrics showed significant correlations. CBF and CBV histogram metrics were less strongly correlated with glioma grade than rCBV histogram metrics. HISTOGRAM ANALYSIS is a quantitative technique used in a number of neuroimaging studies but is most commonly used in magnetization transfer ratio studies of patients with diffuse cerebral disease such as multiple sclerosis (1-4). Dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC MRI) is an established technique in the evaluation of cerebral glial neoplasms. Relative cerebral blood volume (rCBV) measurements show reliable correlation with tumor grade and histopathologic findings of tumor neovascularity (5-16). Perfusion data is most commonly analyzed using multiple small region-of-interest (ROI) measurements, an operator-dependent method. ROI measurement of tumor rCBV has shown good reproducibility, but nevertheless remains operator-dependent and somewhat subjective, with an unavoidable component of interobserver and intraobserver variability. Reliable, reproducible data may only be obtained by experienced operators. Therefore, developing a more objective method that simplifies the analysis may allow even inexperienced operators to obtain reproducible data. Importantly, as surrogate imaging markers for angiogenesis are being investigated to determine the efficacy of antiangiogenic therapies (17), intra-and interinstitutional reproducibility becomes crucial, particularly when performing multicenter clinical trials using DSC MRI. The hypothesis is that histogram methods are comparable to, if not superior to current ROI-based methods for calculating the maximum value of rCBV (rCBV max ) in the prediction of glioma grade. We compare three methods of histogram analysis using both ROI- Conclusion

Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

“…This may be partially due to the relatively complex image analysis with high inherent user-dependence. Recently, histogram analysis methods have been proposed to address this issue (10,11). Based on distribution analysis of rCBV values within the glioma volume, results suggest that histogram analysis methods provide reproducible data with high interobserver agreement.…”

“…Furthermore, in combination with conventional MR imaging, several studies have shown that dynamic susceptibility contrast (DSC) MR imaging of gliomas is a good presurgical indicator of glioma grade (4 -6) and patient outcome regardless of glioma grade (7,8). Typically, glioma grading from DSC is based on analysis of cerebral blood volume (CBV) values within the glioma area, using either a hot-spot method (4,9) or a histogram analysis method (10,11). Correct identification of glioma tissue however, may be dependent on experienced operators with good anatomical knowledge, making current grading methods inherently operator-dependent and time consuming (6,10,11).…”

“…Typically, glioma grading from DSC is based on analysis of cerebral blood volume (CBV) values within the glioma area, using either a hot-spot method (4,9) or a histogram analysis method (10,11). Correct identification of glioma tissue however, may be dependent on experienced operators with good anatomical knowledge, making current grading methods inherently operator-dependent and time consuming (6,10,11). Recently, both supervised and unsupervised segmentation methods for identification of brain tissue structures have been proposed.…”

Histogram analysis of the microvasculature of intracerebral human and murine glioma xenografts

“…Histogram analysis allows the analysis of distributions of pixels over tumors. Specific areas can therefore be analyzed against others (14). In the present work, only murine gliomas showed significant differences in percentiles compared to their healthy contralateral part.…”

MRI perfusion measurements calculated using advanced deconvolution techniques predict survival in recurrent glioblastoma treated with bevacizumab

“…Dynamic susceptibility contrast (DSC)-MRI is a specific perfusion MR technique that is often used to estimate & Benjamin M. Ellingson bellingson@mednet.ucla.edu cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit times (MTT) in patients with brain tumors and other pathologies [8][9][10][11][12]. DSC-MRI has been shown to predict tumor grade [13][14][15] and predict survival in lowgrade gliomas [16], but preliminary evaluations in antiangiogenic therapies have not been particularly fruitful.…”