Application of histogram analysis for the evaluation of vascular permeability in glioma by the K2 parameter obtained with the dynamic susceptibility contrast method: Comparisons with Ktrans obtained with the dynamic contrast enhance method and cerebral blood volume

Taoka, Toshiaki; Kawai, Hisashi; Nakane, Toshiki; Ochi, Tomoko; Miyasaka, Toshiteru; Sakamoto, Masahiko; Kichikawa, Kimihiko; Naganawa, Shinji

doi:10.1016/j.mri.2016.04.020

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…Alternatively, K2 obtained in DSC examinations is a coefficient used for leakage correction and can serve as a rough measure of permeability. Published studies have demonstrated that K2 can be used for differentiating between CNSL and HGG 3 and that K2 shows a similar tendency as K trans , 20 suggesting that the use of K2 in place of K trans is also a good option when a sufficient length of time cannot be devoted to examinations.…”

Section: Discussionmentioning

confidence: 99%

Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis

Murayama

Nishiyama²,

Hirose³

et al. 2018

MRMS

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Purpose:We evaluated the diagnostic performance of histogram analysis of data from a combination of dynamic susceptibility contrast (DSC)-MRI and dynamic contrast-enhanced (DCE)-MRI for quantitative differentiation between central nervous system lymphoma (CNSL) and high-grade glioma (HGG), with the aim of identifying useful perfusion parameters as objective radiological markers for differentiating between them.Methods:Eight lesions with CNSLs and 15 with HGGs who underwent MRI examination, including DCE and DSC-MRI, were enrolled in our retrospective study. DSC-MRI provides a corrected cerebral blood volume (cCBV), and DCE-MRI provides a volume transfer coefficient (Ktrans) for transfer from plasma to the extravascular extracellular space. Ktrans and cCBV were measured from a round region-of-interest in the slice of maximum size on the contrast-enhanced lesion. The differences in t values between CNSL and HGG for determining the most appropriate percentile of Ktrans and cCBV were investigated. The differences in Ktrans, cCBV, and Ktrans/cCBV between CNSL and HGG were investigated using histogram analysis. Receiver operating characteristic (ROC) analysis of Ktrans, cCBV, and Ktrans/cCBV ratio was performed.Results:The 30th percentile (C30) in Ktrans and 80th percentile (C80) in cCBV were the most appropriate percentiles for distinguishing between CNSL and HGG from the differences in t values. CNSL showed significantly lower C80 cCBV, significantly higher C30 Ktrans, and significantly higher C30 Ktrans/C80 cCBV than those of HGG. In ROC analysis, C30 Ktrans/C80 cCBV had the best discriminative value for differentiating between CNSL and HGG as compared to C30 Ktrans or C80 cCBV.Conclusion:The combination of Ktrans by DCE-MRI and cCBV by DSC-MRI was found to reveal the characteristics of vascularity and permeability of a lesion more precisely than either Ktrans or cCBV alone. Histogram analysis of these vascular microenvironments enabled quantitative differentiation between CNSL and HGG.

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Section: Discussionmentioning

confidence: 99%

Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis

Murayama

Nishiyama²,

Hirose³

et al. 2018

MRMS

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“…Although the K 1 , K 2 model of DSC‐MRI is widely used to measure true CBV ( K 1 index) without CA extravasation effects, numerous studies also attempted to evaluate vascular permeability ( K 2 index) of neurovascular disease, which requires the robust delineation of tissue region of intact BBB. Often, nonenhancing voxels with CA injection were selected to obtain

\overset{∆ R_{2}^{*}}{true}

curves of intact BBB from DSC‐MRI, because vessel permeability information cannot be obtained from anatomical/structural images.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, when the capacity for an increased CA dose is limited for DCE‐MRI, with its stronger transverse relaxations, particularly prominent at high field strengths and its longer acquisition times . Thus, we hypothesize that the estimation of the vessel wall leakage index K 2 of CA extravasation from DSC‐MRI (<4 min) at an elevated dose (0.3 mmol/kg) with an intermediate repetition time (TR) (300 msec) can successfully segment BBB‐damaged regions in a stroke model with weak BBB damage.…”

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confidence: 99%

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Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

Jin

Han

Stoyanova

et al. 2019

Magnetic Resonance Imaging

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Background: The manual segmentation of intact blood-brain barrier (BBB) regions in the stroke brain is cumbersome, due to the coexistence of infarction, large blood vessels, ventricles, and intact BBB regions, specifically in areas with weak signal enhancement following contrast agent injection. Hypothesis: That from dynamic susceptibility contrast (DSC)-MRI alone, without user intervention, regions of weak BBB damage can be segmented based on the leakage-related parameter K 2 and the extent of intact BBB regions, needed to estimate K 2 values, determined. Study Type: Feasibility. Animal Model: Ten female Sprague-Dawley rats (SD, 200-250g) underwent 1-hour middle carotid artery occlusion (MCAO) and 1-day reperfusion. Two SD rats underwent 1-hour MCAO with 3-day and 5-day reperfusion. Field Strength/Sequence: 7T; ADC and T 1 maps using diffusion-weighted echo planar imaging (EPI) and relaxation enhancement (RARE) with variable repetition time (TR), respectively. dynamic contrast-enhanced (DCE)-MRI using FLASH. DSC-MRI using gradient-echo EPI. Assessment: Constrained nonnegative matrix factorization (cNMF) was applied to the dynamic ΔR * 2 -curves of DSC-MRI (<4 min) in a BBB-disrupted rat model. Areas of voxels with intact BBB, classified by automated cNMF analyses, were then used in estimating K 1 and K 2 values, and compared with corresponding values from manually-derived areas. Statistical Tests: Mean AE standard deviation of ΔT 1 -differences between ischemic and healthy areas were displayed with unpaired Student's t-tests. Scatterplots were displayed with slopes and intercepts and Pearson's r values were evaluated between K 2 maps obtained with automatic (cNMF)-and manually-derived regions of interest (ROIs) of the intact BBB region. Results: Mildly BBB-damaged areas (indistinguishable from DCE-MRI (10 min) parameters) were automatically segmented. Areas of voxels with intact BBB, classified by automated cNMF, matched closely the corresponding, manually-derived areas when respective areas were used in estimating K 2 maps (Pearson's r = 0.97, 12 slices). Data Conclusion: Automatic segmentation of short DSC-MRI data alone successfully identified areas with intact and compromised BBB in the stroke brain and compared favorably with manual segmentation.

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“…Previously researches prefer to explore the variances of texture parameters between different types of diseased tissue. Loose et al (12) evaluated the Haralick textures on DCE-MRI data for the differentiation of Breast Tumor Lesions, Toshiaki et al (13) adopted the histogram analysis to the evaluation of vascular permeability in glioma. On the other hand, chemometric methods such as principal components analysis (PCA), support vector machine (SVM), linear discriminant analysis (LDA), have been employed to the discriminant analysis of different types of lesions (9,14,15), which aims to access the overall changes in multi-texture parameters.…”

Section: Introductionmentioning

confidence: 99%

Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging

Niu

Jiang

et al. 2018

Oncol Lett

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Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has become a powerful tool for the diagnosis of breast cancer in the clinical setting due to its high sensitivity and specificity. Pharmacokinetic parameters, including Ktrans and area under the curve (AUC), and texture features derived from DCE-MRI have been used to specify the characteristics inside tumors. In the present study, 56 patients (average age 45.3±11.1; range 25-69 years) with histopathologically proved breast tumors were analyzed using the pharmacokinetic parameters and texture features. Malignant tumors displayed higher Ktrans and AUC values than the benign, Ktrans exhibited a significantly difference between the malignant and benign tumors (P=0.001) compared with the AUC values (P=0.029); texture features from DCE-MRI images and pharmacokinetic parameter maps also showed a good diagnostic ability. Alongside the routine method, principal components analysis (PCA) and Fisher discriminant analysis (FDA) were employed on these texture features to differentiate the breast lesions automatically. The Factor-1 scores of PCA were used to divide the patients into two groups, and the diagnosing accuracies of the FDA method on the texture features from DCE-MRI images, Ktrans maps, AUC maps were 93, 98 and 98%, with a cross validation accuracies of 82, 77 and 77%, respectively. To conclude, pharmacokinetic parameters, texture features and the combined computer-assisted classification method were discussed. All method involved in this study may be a potential assisted tool for radiological analysis on breast.

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Application of histogram analysis for the evaluation of vascular permeability in glioma by the K2 parameter obtained with the dynamic susceptibility contrast method: Comparisons with Ktrans obtained with the dynamic contrast enhance method and cerebral blood volume

Cited by 14 publications

References 11 publications

Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis

Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis

Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

Texture features and pharmacokinetic parameters in differentiating benign and malignant breast lesions by dynamic contrast enhanced magnetic resonance imaging

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