Literature DB >> 26529294

Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis.

Alexey Surov1, Daniel T Ginat2, Eser Sanverdi3, C C Tchoyoson Lim4, Bahattin Hakyemez5, Akira Yogi6, Teresa Cabada7, Andreas Wienke8.   

Abstract

BACKGROUND: Meningioma is the most frequent intracranial tumor and is often an incidental finding on imaging. Some imaging-based scores were suggested for differentiating low- and high-grade meningiomas. The purpose of this work was to compare diffusion-weighted imaging findings of different meningiomas in a large multicenter study by using apparent diffusion coefficient (ADC) values for predicting tumor grade and proliferation potential.
METHODS: Data from 7 radiologic departments were acquired retrospectively. Overall, 389 patients were collected. All meningiomas were investigated by magnetic resonance imaging (1.5-T scanner) by using diffusion-weighted imaging (b values of 0 and 1000 s/mm(2)). The comparison of ADC values was performed by Mann-Whitney U test.
RESULTS: World Health Organization grade I was diagnosed in 271 cases (69.7%), grade II in 103 (26.5%), and grade III in 15 patients (3.9%). Grade I meningiomas showed statistically significant higher ADC values (1.05 ± 0.39 × 10(-3) mm(2)s(-1)) in comparison with grade II (0.77 ± 0.15 × 10(-3) mm(2)s(-1); P = 0.001) and grade III tumors (0.79 ± 0.21 × 10(-3) mm(2)s(-1); P = 0.01). An ADC value of <0.85 × 10(-3) mm(2)s(-1) was determined as the threshold in differentiating between grade I and grade II/III meningiomas (sensitivity, 72.9%; specificity, 73.1%; accuracy, 73.0%). Ki67 was associated with ADC (r = -0.63, P < 0.001). The optimal threshold for the ADC was (less than) 0.85 × 10(-3) mm(2)s(-1) for detecting tumors with high proliferation potential (Ki67 ≥5%).
CONCLUSIONS: The estimated threshold ADC value of 0.85 can differentiate grade I meningioma from grade II and III tumors. The same ADC value is helpful for detecting tumors with high proliferation potential.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  DWI; MRI; Meningioma

Mesh:

Year:  2015        PMID: 26529294     DOI: 10.1016/j.wneu.2015.10.049

Source DB:  PubMed          Journal:  World Neurosurg        ISSN: 1878-8750            Impact factor:   2.104


  25 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.  Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma.

Authors:  Stefan Schob; Anne Beeskow; Julia Dieckow; Hans-Jonas Meyer; Matthias Krause; Clara Frydrychowicz; Franz-Wolfgang Hirsch; Alexey Surov
Journal:  Childs Nerv Syst       Date:  2018-05-31       Impact factor: 1.475

3.  Correlation Between Aquaporin 4 Expression and Different DWI Parameters in Grade I Meningioma.

Authors:  Stefan Schob; Alexey Surov; Andreas Wienke; Hans Jonas Meyer; Rolf Peter Spielmann; Eckhard Fiedler
Journal:  Mol Imaging Biol       Date:  2017-02       Impact factor: 3.488

4.  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

5.  ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases-a Preliminary Study.

Authors:  Stefan Schob; Hans Jonas Meyer; Nikolaos Pazaitis; Dominik Schramm; Kristina Bremicker; Marc Exner; Anne Kathrin Höhn; Nikita Garnov; Alexey Surov
Journal:  Mol Imaging Biol       Date:  2017-12       Impact factor: 3.488

6.  Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

Authors:  Hao Yu; Xinrui Wen; Pingping Wu; Yueqin Chen; Tianyu Zou; Xianlong Wang; Shanshan Jiang; Jinyuan Zhou; Zhibo Wen
Journal:  Eur Radiol       Date:  2019-03-18       Impact factor: 5.315

7.  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

Review 8.  Variants of meningiomas: a review of imaging findings and clinical features.

Authors:  Akira Kunimatsu; Natsuko Kunimatsu; Kouhei Kamiya; Masaki Katsura; Harushi Mori; Kuni Ohtomo
Journal:  Jpn J Radiol       Date:  2016-05-02       Impact factor: 2.374

9.  Pretreatment Apparent Diffusion Coefficient Cannot Predict Histopathological Features and Response to Neoadjuvant Radiochemotherapy in Rectal Cancer: A Meta-Analysis.

Authors:  Alexey Surov; Maciej Pech; Maciej Powerski; Katja Woidacki; Andreas Wienke
Journal:  Dig Dis       Date:  2021-03-04       Impact factor: 2.404

10.  MRI predictors for brain invasion in meningiomas.

Authors:  Thomas Ong; Aditya Bharatha; Reema Alsufayan; Sunit Das; Amy Wei Lin
Journal:  Neuroradiol J       Date:  2020-09-14
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