Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters

Bohara, Manisha; Nakajo, Masanori; Kamimura, Kiyohisa; Yoneyama, T.; Fukukura, Yoshihiko; Kiyao, Yutaro; Yonezawa, Hajime; Higa, Nayuta; Kirishima, Mari; Yamada, Takashi

doi:10.1016/j.acra.2019.04.012

Cited by 27 publications

(10 citation statements)

References 43 publications

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Section: Discussionsupporting

confidence: 93%

“…Besides, the skewness and variance of MD could differentiate high-from low-grade meningiomas. These results were partially in line with those of previous studies in brain tumors (28,29). Qi et al (28) reported that the skewness of MD in high-grade gliomas was significantly higher than those of low-grade gliomas.…”

Section: Discussionsupporting

confidence: 90%

“…Manisha et al. (29) also showed that the variance of ADC in high-grade meningiomas was higher than those of low-grade meningiomas. In histogram analysis, kurtosis, skewness, and variance separately measure the peakedness, asymmetry, and variations of the distribution.…”

Section: Discussionmentioning

confidence: 97%

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Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

Chen

Lin

et al. 2020

Acta Radiol

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Background Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. Purpose To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. Material and Methods A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann–Whitney U test, Kruskal–Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. Results Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( P < 0.05). Conclusion The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 90%

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Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

Chen

Lin

et al. 2020

Acta Radiol

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“…IVIM has gained great interest in the clinical routine as it does not require an exogenous contrast agent to be injected and provides perfusion and diffusion information simultaneously, by fitting the signal to a biexponential model 2 . Indeed, IVIM shows great potential in many applications in neuro‐oncology and the study of neurological and neurovascular diseases, including the diagnosis and decision‐making in acute strokes, 3 the differentiation, 4 prognosis, 5 and progression of gliomas, as well as differentiation of meningiomas 6,7 . IVIM parameters have been also recently investigated as biomarkers in targeted therapies for treatment response assessment and early prediction of progression.…”

Section: Introductionmentioning

confidence: 99%

Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

Paganelli

Zampini²,

Morelli

et al. 2023

J Applied Clin Med Phys

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To define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model. Methods: Simulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal bvalues set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one. Results: The total relative error on simulations decreased of about 40% when adopting the optimal set of 13 b-values (0 10 20 40 50 60 200 300 400 1200 1300 1400 1500 s/mm 2 ), showing significant differences and increased precision on D and f estimates with respect to simulations with a non-optimized b-values set. Similarly, in vivo acquisitions demonstrated a dependency of IVIM parameters on the b-values array, with differences between the optimal set of b-values and a clinical non-optimized acquisition. IVIM parameters were compatible to literature values, with D (0.679/0.701 [0.022/0.008] ⋅10 −3 mm 2 /s), f (5.49/5.80 [0.70/1.14] %), and D* (8.25/7.67 [0.92/0.83] ⋅10 −3 mm 2 /s) median [interquartile range] estimates for white matter/gray matter in volunteers and D (0.

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“…Therefore, identifying high-grade meningiomas based on precontrast MRI is crucial under these circumstances. Some studies have explored the value of non-contrast advanced MRI, such as amide proton transfer imaging ( 6 ) or intra-voxel incoherent motion imaging ( 7 ), in identifying high-grade meningiomas and have achieved good performance. However, these advanced MRI techniques may not always be available, especially for primary healthcare institutions.…”

Section: Introductionmentioning

confidence: 99%

Predicting the grade of meningiomas by clinical–radiological features: A comparison of precontrast and postcontrast MRI

Yuan

Liu

et al. 2022

Front. Oncol.

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ObjectivesPostcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningiomas. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aimed to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI.MethodsFrom January 2019 to October 2021, a total of 231 meningioma patients (development cohort n = 137, low grade/high grade, 85/52; external validation cohort n = 94, low-grade/high-grade, 60/34) were retrospectively included. Fourteen types of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomograms, based on full MRI and precontrast MRI. Their distinguishing performances were validated and compared using the external validation cohort.ResultsOne demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, and peritumoral edema), and one postcontrast MRI feature (absence of a dural tail sign) were independent predictive factors for high-grade meningiomas. The area under the receiver operating characteristic (ROC) curve (AUC) values of the two distinguishing models (precontrast–postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898 and in the validation cohort were 0.922 and 0.878. DeLong’s test showed no statistical difference between the AUC values of the two distinguishing models (p = 0.101).ConclusionsAn accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.

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Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters

Cited by 27 publications

References 43 publications

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

Predicting the grade of meningiomas by clinical–radiological features: A comparison of precontrast and postcontrast MRI

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