Literature DB >> 27873040

Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review.

Amy Yao1, Margaret Pain2, Priti Balchandani2, Raj K Shrivastava2.   

Abstract

Tumor consistency is a critical factor that influences operative strategy and patient counseling. Magnetic resonance imaging (MRI) describes the concentration of water within living tissues and as such, is hypothesized to predict aspects of their biomechanical behavior. In meningiomas, MRI signal intensity has been used to predict the consistency of the tumor and its histopathological subtype, though its predictive capacity is debated in the literature. We performed a systematic review of the PubMed database since 1990 concerning MRI appearance and tumor consistency to assess whether or not MRI can be used reliably to predict tumor firmness. The inclusion criteria were case series and clinical studies that described attempts to correlate preoperative MRI findings with tumor consistency. The relationship between the pre-operative imaging characteristics, intraoperative findings, and World Health Organization (WHO) histopathological subtype is described. While T2 signal intensity and MR elastography provide a useful predictive measure of tumor consistency, other techniques have not been validated. T1-weighted imaging was not found to offer any diagnostic or predictive value. A quantitative assessment of T2 signal intensity more reliably predicts consistency than inherently variable qualitative analyses. Preoperative knowledge of tumor firmness affords the neurosurgeon substantial benefit when planning surgical techniques. Based upon our review of the literature, we currently recommend the use of T2-weighted MRI for predicting consistency, which has been shown to correlate well with analysis of tumor histological subtype. Development of standard measures of tumor consistency, standard MRI quantification metrics, and further exploration of MRI technique may improve the predictive ability of neuroimaging for meningiomas.

Entities:  

Keywords:  Magnetic resonance imaging; Meningioma; Pathology; Tumor consistency

Mesh:

Year:  2016        PMID: 27873040      PMCID: PMC5438899          DOI: 10.1007/s10143-016-0801-0

Source DB:  PubMed          Journal:  Neurosurg Rev        ISSN: 0344-5607            Impact factor:   3.042


  36 in total

1.  A scale of methodological quality for clinical studies of radiologic examinations.

Authors:  L Arrivé; R Renard; F Carrat; A Belkacem; H Dahan; P Le Hir; L Monnier-Cholley; J M Tubiana
Journal:  Radiology       Date:  2000-10       Impact factor: 11.105

2.  Diffusion-weighted imaging does not predict histological grading in meningiomas.

Authors:  Luca Santelli; Gaetano Ramondo; Alessandro Della Puppa; Mario Ermani; Renato Scienza; Domenico d'Avella; Renzo Manara
Journal:  Acta Neurochir (Wien)       Date:  2010-04-29       Impact factor: 2.216

3.  Meningiomas: MR and histopathologic features.

Authors:  A D Elster; V R Challa; T H Gilbert; D N Richardson; J C Contento
Journal:  Radiology       Date:  1989-03       Impact factor: 11.105

4.  Predictors of meningioma consistency: A study in 243 consecutive cases.

Authors:  Bunpot Sitthinamsuwan; Inthira Khampalikit; Sarun Nunta-aree; Prajak Srirabheebhat; Teerapol Witthiwej; Akkapong Nitising
Journal:  Acta Neurochir (Wien)       Date:  2012-06-29       Impact factor: 2.216

5.  Prediction of consistency of meningiomas with preoperative magnetic resonance imaging.

Authors:  N Yamaguchi; T Kawase; M Sagoh; T Ohira; H Shiga; S Toya
Journal:  Surg Neurol       Date:  1997-12

6.  Correlation of the relationships of brain-tumor interfaces, magnetic resonance imaging, and angiographic findings to predict cleavage of meningiomas.

Authors:  F Ildan; M Tuna; A P Göçer; B Boyar; H Bağdatoğlu; O Sen; S Haciyakupoģlu; H R Burgut
Journal:  J Neurosurg       Date:  1999-09       Impact factor: 5.115

7.  Higher-Resolution Magnetic Resonance Elastography in Meningiomas to Determine Intratumoral Consistency.

Authors:  Joshua D Hughes; Nikoo Fattahi; J Van Gompel; Arvin Arani; Fredric Meyer; Giuseppe Lanzino; Michael J Link; Richard Ehman; John Huston
Journal:  Neurosurgery       Date:  2015-10       Impact factor: 4.654

8.  Comparison of consistency of meningiomas and CT appearances.

Authors:  B Kendall; P Pullicino
Journal:  Neuroradiology       Date:  1979-10-31       Impact factor: 2.804

9.  Meningiomas: correlation between MRI characteristics and operative findings including consistency.

Authors:  Y Suzuki; T Sugimoto; M Shibuya; K Sugita; S J Patel
Journal:  Acta Neurochir (Wien)       Date:  1994       Impact factor: 2.216

10.  Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas.

Authors:  Rossana Romani; Wei-Jun Tang; Ying Mao; Dai-Jun Wang; Hai-Liang Tang; Feng-Ping Zhu; Xiao-Ming Che; Ye Gong; Kang Zheng; Ping Zhong; Shi-Qi Li; Wei-Min Bao; Christian Benner; Jing-Song Wu; Liang-Fu Zhou
Journal:  Acta Neurochir (Wien)       Date:  2014-07-08       Impact factor: 2.216

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  13 in total

1.  Tumor to Cerebellar Peduncle T2-Weighted Imaging Intensity Ratio Fails to Predict Pituitary Adenoma Consistency.

Authors:  Panagiotis Mastorakos; Gautam U Mehta; Ajay Chatrath; Shayan Moosa; Maria-Beatriz Lopes; Spencer C Payne; John A Jane
Journal:  J Neurol Surg B Skull Base       Date:  2018-08-24

Review 2.  Petroclival meningiomas: update of current treatment and consensus by the EANS skull base section.

Authors:  Lorenzo Giammattei; P di Russo; D Starnoni; T Passeri; M Bruneau; T R Meling; M Berhouma; G Cossu; J F Cornelius; D Paraskevopoulos; I Zazpe; E Jouanneau; L M Cavallo; V Benes; V Seifert; M Tatagiba; H W S Schroeder; T Goto; K Ohata; O Al-Mefty; T Fukushima; M Messerer; R T Daniel; S Froelich
Journal:  Acta Neurochir (Wien)       Date:  2021-03-19       Impact factor: 2.216

3.  Can an Imaging Marker of Consistency Predict Intraoperative Experience and Clinical Outcomes for Vestibular Schwannomas? A Retrospective Review.

Authors:  Robert J Macielak; Michael S Harris; Jameson K Mattingly; Varun S Shah; Luciano M Prevedello; Oliver F Adunka
Journal:  J Neurol Surg B Skull Base       Date:  2019-09-24

4.  The otologic approach in the management of posterior petrous surface meningiomas.

Authors:  Vittoria Sykopetrites; Abdelkader Taibah; Gianluca Piras; Anna Lisa Giannuzzi; Fernando Mancini; Mario Sanna
Journal:  Eur Arch Otorhinolaryngol       Date:  2022-06-29       Impact factor: 2.503

5.  Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

Authors:  Kai Roman Laukamp; Lenhard Pennig; Frank Thiele; Robert Reimer; Lukas Görtz; Georgy Shakirin; David Zopfs; Marco Timmer; Michael Perkuhn; Jan Borggrefe
Journal:  Clin Neuroradiol       Date:  2020-02-14       Impact factor: 3.649

Review 6.  Utility of preoperative meningioma consistency measurement with magnetic resonance elastography (MRE): a review.

Authors:  Alexander G Chartrain; Mehmet Kurt; Amy Yao; Rui Feng; Kambiz Nael; J Mocco; Joshua B Bederson; Priti Balchandani; Raj K Shrivastava
Journal:  Neurosurg Rev       Date:  2017-05-31       Impact factor: 3.042

7.  Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

Authors:  Kai Roman Laukamp; Frank Thiele; Georgy Shakirin; David Zopfs; Andrea Faymonville; Marco Timmer; David Maintz; Michael Perkuhn; Jan Borggrefe
Journal:  Eur Radiol       Date:  2018-06-25       Impact factor: 5.315

8.  New insights into the genomic landscape of meningiomas identified FGFR3 in a subset of patients with favorable prognoses.

Authors:  Aysha AlSahlawi; Rasha Aljelaify; Malak Abedalthagafi; Amna Magrashi; Mariam AlSaeed; Amal Almutairi; Fatimah Alqubaishi; Abdulellah Alturkistani; Abdullah AlObaid; Mohamed Abouelhoda; Latifa AlMubarak; Nada AlTassan
Journal:  Oncotarget       Date:  2019-09-17

Review 9.  REVIEW: MR elastography of brain tumors.

Authors:  Adomas Bunevicius; Katharina Schregel; Ralph Sinkus; Alexandra Golby; Samuel Patz
Journal:  Neuroimage Clin       Date:  2019-11-23       Impact factor: 4.881

10.  Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors.

Authors:  Markus Nilsson; Filip Szczepankiewicz; Jan Brabec; Marie Taylor; Carl-Fredrik Westin; Alexandra Golby; Danielle van Westen; Pia C Sundgren
Journal:  Magn Reson Med       Date:  2019-09-13       Impact factor: 4.668

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