Literature DB >> 29748203

Comparative Analysis of Diffusional Kurtosis Imaging, Diffusion Tensor Imaging, and Diffusion-Weighted Imaging in Grading and Assessing Cellular Proliferation of Meningiomas.

L Lin1, R Bhawana1, Y Xue2, Q Duan1, R Jiang1, H Chen3, X Chen4, B Sun1, H Lin1.   

Abstract

BACKGROUND AND
PURPOSE: An accurate evaluation of the World Health Organization grade and cellular proliferation is particularly important in meningiomas. Our aim was to prospectively evaluate and compare diffusional kurtosis imaging, DTI, and DWI metrics in determining the grade and cellular proliferation of meningiomas.
MATERIALS AND METHODS: Ninety-six consecutive patients with histopathologically confirmed meningiomas were included in this study. Mean kurtosis, radial kurtosis, axial kurtosis, fractional anisotropy, mean diffusivity, and ADC were semiautomatically obtained in the solid components of tumors. Each normalized diffusion value was compared between high-grade meningiomas and low-grade meningiomas using the Mann-Whitney U test. Receiver operating characteristic, multiple logistic regression, and Pearson correlation analysis were used for statistical evaluations.
RESULTS: Diffusional kurtosis imaging metrics (mean kurtosis, radial kurtosis, and axial kurtosis) were significantly higher in high-grade meningiomas than in low-grade meningiomas (P ≤ .001). Mean diffusivity and ADC were significantly lower in high-grade meningiomas than in low-grade meningiomas (P = .003 and .002). Mean kurtosis had significantly greater area the under curve values than mean diffusivity and fractional anisotropy in differentiating high-grade meningiomas from low-grade meningiomas (P = .038 and .002). Mean kurtosis was the only variable that could be used to independently differentiate high-grade meningiomas and low-grade meningiomas (P < .001). Significant correlations were found between the Ki-67 labeling index and kurtosis metrics (P < .001), as well as for mean diffusivity and ADC (P = .004, and .007).
CONCLUSIONS: Compared with other diffusion metrics, mean kurtosis may serve as an optimal parameter for evaluating and predicting the meningioma grade. Moreover, diffusion metrics may potentially reflect cellular proliferation.
© 2018 by American Journal of Neuroradiology.

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Year:  2018        PMID: 29748203     DOI: 10.3174/ajnr.A5662

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  9 in total

1.  Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.

Authors:  Shun Zhang; Gloria Chia-Yi Chiang; Jacquelyn Marion Knapp; Christina M Zecca; Diana He; Rohan Ramakrishna; Rajiv S Magge; David J Pisapia; Howard Alan Fine; Apostolos John Tsiouris; Yize Zhao; Linda A Heier; Yi Wang; Ilhami Kovanlikaya
Journal:  J Neuroradiol       Date:  2019-05-25       Impact factor: 3.447

2.  Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging.

Authors:  Ke Xiaoai; Zhou Qing; Han Lei; Zhou Junlin
Journal:  Neuroradiology       Date:  2020-01-29       Impact factor: 2.804

3.  T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

Authors:  Tiexin Cao; Rifeng Jiang; Lingmin Zheng; Rufei Zhang; Xiaodan Chen; Zongmeng Wang; Peirong Jiang; Yilin Chen; Tianjin Zhong; Hu Chen; PuYeh Wu; Yunjing Xue; Lin Lin
Journal:  Eur Radiol       Date:  2022-08-12       Impact factor: 7.034

4.  Neuroanatomical underpinning of diffusion kurtosis measurements in the cerebral cortex of healthy macaque brains.

Authors:  Tianjia Zhu; Qinmu Peng; Austin Ouyang; Hao Huang
Journal:  Magn Reson Med       Date:  2020-10-15       Impact factor: 4.668

5.  Apparent diffusion coefficient as a prognostic factor in clival chordoma.

Authors:  Hyeong-Cheol Oh; Chang-Ki Hong; Kyu-Sung Lee; Yoon Jin Cha; Sung Jun Ahn; Sang Hyun Suh; Hun Ho Park
Journal:  Sci Rep       Date:  2021-01-12       Impact factor: 4.379

6.  Apparent Diffusion Coefficient Values and Dynamic Contrast-Enhanced Magnetic Resonance Perfusion are Potential Predictors for Grading Meningiomas.

Authors:  Sri Andreani Utomo; Abdul Hafid Bajamal; Yuyun Yueniwati; Irfan Deny Sanjaya; Dyah Fauziah
Journal:  Int J Med Sci       Date:  2022-07-18       Impact factor: 3.642

7.  CT Hounsfield Unit Is a Good Predictor of Growth in Meningiomas.

Authors:  Satoshi Nakasu; Takeshi Onishi; Sawako Kitahara; Hisayuki Oowaki; Ken-Ichi Matsumura
Journal:  Neurol Med Chir (Tokyo)       Date:  2019-01-26       Impact factor: 1.742

8.  The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis.

Authors:  Jixin Luan; Mingzhen Wu; Xiaohui Wang; Lishan Qiao; Guifang Guo; Chuanchen Zhang
Journal:  Radiat Oncol       Date:  2020-08-24       Impact factor: 3.481

9.  Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project.

Authors:  Rafael Neto Henriques; Marta M Correia; Maurizio Marrale; Elizabeth Huber; John Kruper; Serge Koudoro; Jason D Yeatman; Eleftherios Garyfallidis; Ariel Rokem
Journal:  Front Hum Neurosci       Date:  2021-07-19       Impact factor: 3.169

  9 in total

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