Literature DB >> 22727725

Correlating apparent diffusion coefficients with histopathologic findings on meningiomas.

Bo Yin1, Li Liu, Bi Yun Zhang, Yu Xin Li, Yuan Li, Dao Ying Geng.   

Abstract

PURPOSE: To determine whether the apparent diffusion coefficient (ADC) correlates with histopathologic findings and whether ADC values can be used to differentiate benign from atypical/malignant meningiomas.
MATERIALS AND METHODS: MR images were reviewed retrospectively in 138 patients with meningiomas treated between September 1997 and July 2003. The ADC values were measured in the lesions and peritumoral edema, and the normalized ADC (NADC) ratios were calculated using the formula NADC=ADC of the tumor/ADC of the normal white matter. The ADC findings were compared with the histopathologic findings after resection using the World Health Organization criteria (2007).
RESULTS: Meningiomas were histologically graded as malignant (9%), atypical (14%) and benign (77%). Of the 138 meningiomas, 32 (23%) were atypical (n=19) or malignant (n=13), whereas 106 (77%) were typical. The mean ADC values were statistically different between typical and atypical/malignant meningiomas (0.97 ± 0.21 × 10(-3)mm(2)/s vs 0.85 ± 0.17 × 10(-3)mm(2)/s). The mean NADC ratios were also significantly lower in the atypical/malignant group (1.09 ± 0.23) than in the benign group (1.24 ± 0.25; P=0.002<0.05). The mean ADC values and NADC ratios did not differ significantly among fibrous, meningothelial, transitional and atypical tumors (P>0.05). The mean ADC values and NADC ratios were higher in the angiomatous and secretory subgroups than in the fibrous, meningothelial, transitional, atypical and malignant subgroups (P<0.05). The ADC values and NADC ratios were the lowest in the malignant subgroup, and the difference between atypical and malignant meningiomas was statistically significant (P<0.05).
CONCLUSIONS: Meningioma subgroups displayed different ADC values from each other. Thus, ADC values may provide a useful supplement to the information obtained from conventional contrast-enhanced MR imaging, enhancing the ability of medical professionals to differentiate among the subgroups of meningiomas.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22727725     DOI: 10.1016/j.ejrad.2012.06.002

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  14 in total

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Authors:  K M O'Connor; G Barest; T Moritani; O Sakai; A Mian
Journal:  Br J Radiol       Date:  2013-10-28       Impact factor: 3.039

2.  The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma.

Authors:  Kerim Aslan; Hediye Pinar Gunbey; Leman Tomak; Lutfi Incesu
Journal:  Br J Radiol       Date:  2018-05-31       Impact factor: 3.039

3.  Reticular Appearance on Gadolinium-enhanced T1- and Diffusion-weighted MRI, and Low Apparent Diffusion Coefficient Values in Microcystic Meningioma Cysts.

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4.  Cerebellopontine angle tumors in young children, displaying cranial nerve deficits, and restricted diffusion on diffusion-weighted imaging: a new clinical triad for atypical teratoid/rhabdoid tumors.

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Journal:  Childs Nerv Syst       Date:  2017-03-22       Impact factor: 1.475

Review 5.  Uncommon Cranial Meningioma: Key Imaging Features on Conventional and Advanced Imaging.

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Journal:  Clin Neuroradiol       Date:  2017-05-02       Impact factor: 3.649

6.  Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas.

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Journal:  Neurosurgery       Date:  2021-10-13       Impact factor: 5.315

7.  Imaging and diagnostic advances for intracranial meningiomas.

Authors:  Raymond Y Huang; Wenya Linda Bi; Brent Griffith; Timothy J Kaufmann; Christian la Fougère; Nils Ole Schmidt; Jöerg C Tonn; Michael A Vogelbaum; Patrick Y Wen; Kenneth Aldape; Farshad Nassiri; Gelareh Zadeh; Ian F Dunn
Journal:  Neuro Oncol       Date:  2019-01-14       Impact factor: 12.300

8.  The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest.

Authors:  Yiping Lu; Li Liu; Shihai Luan; Ji Xiong; Daoying Geng; Bo Yin
Journal:  Eur Radiol       Date:  2018-08-07       Impact factor: 5.315

9.  Differentiating intracranial solitary fibrous tumor/hemangiopericytoma from meningioma using diffusion-weighted imaging and susceptibility-weighted imaging.

Authors:  Tanhui Chen; Bingqing Jiang; Yingyan Zheng; Dejun She; Hua Zhang; Zhen Xing; Dairong Cao
Journal:  Neuroradiology       Date:  2019-10-31       Impact factor: 2.804

10.  Metastatic meningioma presenting as a malignant soft tissue tumour.

Authors:  Catherine McCarthy; Monika Hofer; Marianna Vlychou; Robar Khundkar; Paul Critchley; Simon Cudlip; Olaf Ansorge; Nick A Athanasou
Journal:  Clin Sarcoma Res       Date:  2016-12-30
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