Assessment of histogram analysis in distinguishing between low-grade and high-grade brainstem glioma.

Abstract

Objectives: This retrospective study was designed to evaluate the value of histogram analysis on the apparent diffusion coefficient (ADC) map in distinguishing between low-grade and high-grade brainstem glioma (BSG). In this article, we used the two VOI (volume of interest) placements of the entire tumour method and the entire solid part method, thus aiming to compare the diagnostic value between these two performances.

Methods: A total of 28 patients (8 low-grade BSGs and 20 high-grade BSGs) with histological diagnosis of BSG. All victims underwent contrast-enhanced magnetic resonance imaging (MRI). We measured ADC histogram parameters (mean, median, SD, max, min, Kurtosis, Skewness, Entropy, Uniformity, and Variance) and calculated the ratios between tumour and normal brain parenchyma parameters in two methods. Independent samples test, Mann-Whitney U test, and ROC curve were used to determine each value's cut-off point, sensitivity, and specificity.

Results: Among the method of VOI placing the entire tumour, the values of ADC_min, rADC_mean, rADC_median, and rADC_min are significantly different between these two neoplasms with cut-off values (sensitivity %, specificity %) of 776 x10-6 m2/s (62.5%, 90%), 2.1765 (62.5%, 95%), 2.1588 (50%, 100%), 1.0535 (100%, 50%), respectively. On the other hand, the method of VOI placing the entire solid part of the tumour showed significantly different in ADC_mean, ADC_median, ADC_min, rADC_mean, rADC_median, rADC_min at the cut-off values (sensitivity%, specificity %) of 1491 x10-6 m2/s (62.5%, 95%), 1438.9 x10-6 m2/s (62.5%, 90%), 862.5 x10-6 m2/s (75%, 100%), 2,112 (62.5%, 95%), 1.9748 (62.5%, 90%), 1.3735 (87.5%, 90%), respectively.

Conclusions: The ADC histogram analysis is a promising approach to distinguishing low-grade BSG and high-grade BSG. The entire solid part VOI placement has a superior value compared to the whole tumour VOI placement. The rADC_mean showed the best performance in differentiating between these two entities, followed by ADC_min, rADC_mean, rADC_median, ADC_mean, and ADC_median.