Literature DB >> 29619517

Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis.

Chong Hyun Suh1, Ho Sung Kim2, Seung Chai Jung1, Choong Gon Choi1, Sang Joon Kim1.   

Abstract

OBJECTIVES: Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.
METHODS: A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.
RESULTS: Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%).
CONCLUSIONS: Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis. KEY POINTS: • Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.

Entities:  

Keywords:  Glioblastoma; Glioma; Magnetic resonance imaging; Metastasis; Perfusion

Mesh:

Substances:

Year:  2018        PMID: 29619517     DOI: 10.1007/s00330-018-5335-0

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  78 in total

1.  Association of choline levels and tumor perfusion in brain metastases assessed with proton MR spectroscopy and dynamic susceptibility contrast-enhanced perfusion weighted MRI.

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2.  Usefulness of quantitative peritumoural perfusion and proton spectroscopic magnetic resonance imaging evaluation in differentiating brain gliomas from solitary brain metastases.

Authors:  Gianvincenzo Sparacia; Judith A Gadde; Alberto Iaia; Benedetta Sparacia; Massimo Midiri
Journal:  Neuroradiol J       Date:  2016-03-17

3.  Differentiation among glioblastoma multiforme, solitary metastatic tumor, and lymphoma using whole-tumor histogram analysis of the normalized cerebral blood volume in enhancing and perienhancing lesions.

Authors:  J H Ma; H S Kim; N-J Rim; S-H Kim; K-G Cho
Journal:  AJNR Am J Neuroradiol       Date:  2010-06-25       Impact factor: 3.825

4.  Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases.

Authors:  Nathalie Mouthuy; Guy Cosnard; Jorge Abarca-Quinones; Nicolas Michoux
Journal:  J Neuroradiol       Date:  2011-12-22       Impact factor: 3.447

5.  Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques.

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6.  Elevated peritumoural rCBV values as a mean to differentiate metastases from high-grade gliomas.

Authors:  Stella Blasel; Alina Jurcoane; Kea Franz; Gerald Morawe; Stefanie Pellikan; Elke Hattingen
Journal:  Acta Neurochir (Wien)       Date:  2010-08-27       Impact factor: 2.216

7.  Combination of high-resolution susceptibility-weighted imaging and the apparent diffusion coefficient: added value to brain tumour imaging and clinical feasibility of non-contrast MRI at 3 T.

Authors:  S M Park; H S Kim; G-H Jahng; C-W Ryu; S Y Kim
Journal:  Br J Radiol       Date:  2009-08-18       Impact factor: 3.039

8.  Dynamic contrast-enhanced susceptibility-weighted perfusion imaging of intracranial tumors: a study using a 3T MR scanner.

Authors:  Senem Sentürk; Kader Karli Oğuz; Ayşenur Cila
Journal:  Diagn Interv Radiol       Date:  2009-03       Impact factor: 2.630

9.  Vascular permeability factor in brain metastases: correlation with vasogenic brain edema and tumor angiogenesis.

Authors:  J Strugar; D Rothbart; W Harrington; G R Criscuolo
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  17 in total

1.  Discrimination between Glioblastoma and Solitary Brain Metastasis: Comparison of Inflow-Based Vascular-Space-Occupancy and Dynamic Susceptibility Contrast MR Imaging.

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Review 2.  Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis.

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3.  Novel sphingomyelin biomarkers for brain glioma and associated regulation research on the PI3K/Akt signaling pathway.

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Review 4.  MRI biomarkers in neuro-oncology.

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5.  Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.

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6.  Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model.

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Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-11-26       Impact factor: 9.236

7.  Treatment approach and survival from glioblastoma: results from a population-based retrospective cohort study from Western Norway.

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Review 8.  GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma.

Authors:  Patricia Clement; Thomas Booth; Fran Borovečki; Kyrre E Emblem; Patrícia Figueiredo; Lydiane Hirschler; Radim Jančálek; Vera C Keil; Camille Maumet; Yelda Özsunar; Cyril Pernet; Jan Petr; Joana Pinto; Marion Smits; Esther A H Warnert
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9.  Physiological MRI Biomarkers in the Differentiation Between Glioblastomas and Solitary Brain Metastases.

Authors:  Elisabeth Heynold; Max Zimmermann; Nirjhar Hore; Michael Buchfelder; Arnd Doerfler; Andreas Stadlbauer; Natalia Kremenevski
Journal:  Mol Imaging Biol       Date:  2021-04-23       Impact factor: 3.488

10.  Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis.

Authors:  Alix de Causans; Alexandre Carré; Alexandre Roux; Arnault Tauziède-Espariat; Samy Ammari; Edouard Dezamis; Frederic Dhermain; Sylvain Reuzé; Eric Deutsch; Catherine Oppenheim; Pascale Varlet; Johan Pallud; Myriam Edjlali; Charlotte Robert
Journal:  Front Oncol       Date:  2021-07-13       Impact factor: 6.244

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