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

Abstract

The purpose of this study is to examine the usefulness of histogram analysis combined with vessel size index (VSI) magnetic resonance imaging for the specific characterization of brain tumor microvasculature in a panel of six volume-matched glioma xenografts. Using a simple descriptive histogram analysis, significant differences of the mean tumoral VSI (P 5 0.0035 for 9L, P 5 0.008 for glioma mix, P 5 0.05 for C6), the 75th VSI percentile (P 5 0.003-0.075) as well as the 25th and median blood volume (BV) percentiles were found in murine gliomas compared to their contralateral healthy brain. Using a segmented histogram analysis, dilatation of already existing vessels in murine gliomas and development of new small caliber vessels in human glioblastomas were suggested. Most gliomas showed a higher proportion of pixels with BV below 1% (glioma mix [21% vs 1%], Glioblastoma 2 (GBM2) [9% vs 3.7%]) and a smaller proportion of pixels with BV in the range 1.7-6.3% (65 vs 90% for glioma mix, 80 vs 85% in GBM2) relative to their contralateral part. In glioblastomas, VSI and BV distributions were similar to normal brain distributions and in agreement with immunohistochemical findings. The histogram analysis of VSI and BV heterogeneity in experimental brain tumors allowed detection of microregional differences in gliomas from different origins. Magn Reson Med 65:778-789,

MRI Evaluation of Non-Necrotic T2-Hyperintense Foci in Pediatric Diffuse Intrinsic Pontine Glioma

“…Histogram-derived parameters such as skewness, kurtosis, and percentiles have been found to be useful in differentiating between types of gliomas. 19 Other investigators found that rCBV histograms correspond with glioma grades, 20 and ADC histograms can stratify progression-free survival in GBM. 21,22 A similar technique has been used in DIPG to demonstrate significant intratumoral and interpatient mean diffusivity heterogeneity, 23 and shorter overall survival was found to be associated with increased ADC histogram skewness.…”

Histogram analysis of stretched‐exponential and monoexponential diffusion‐weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma

“…The histogram analysis not only describes statistical information but also provides a quantitative methodology for analyzing the nonsignificant changes upon imaging pixels data of tumor . For instance, some parameters of histogram analysis may be potential predictors of response to treatment as well as promising parameters for distinguishing tumor subtypes . During the past several decades, histogram approaches in diffusion MRI of PCa have drawn some researchers' attention .…”

Improving tumour heterogeneity MRI assessment with histograms

“…With increasing advances in both high-resolution MRI and signal processing methods, histogram analysis of cancer MRI is more and more used. This methodology showed its usefulness for investigating the distributions of various tumour parameters such as permeability in dynamic contrast-enhanced MRI (DCE-MRI) ( Hayes et al , 2002 ; Padhani, 2002 ; Peng et al , 2012 ), vessel size index and blood volumes (VSI, CBV) ( Just, 2011 ; Burrell et al , 2012 ) and apparent diffusion coefficient (ADC) in diffusion MRI ( Downey et al , 2013 ).…”