Literature DB >> 28627984

Diagnostic accuracy of proton magnetic resonance spectroscopy and perfusion-weighted imaging in brain gliomas follow-up: a single institutional experience.

Monica Anselmi1, Alessia Catalucci2, Valentina Felli3, Valentina Vellucci3, Alessandra Di Sibio3, Giovanni Luca Gravina2, Mario Di Staso4, Ernesto Di Cesare3, Carlo Masciocchi1.   

Abstract

Objectives The objective of this study was to evaluate whether proton magnetic resonance spectroscopy and perfusion magnetic resonance imaging (MRI) are able to increase diagnostic accuracy in the follow-up of brain gliomas, identifying the progression of disease before it becomes evident in the standard MRI; also to evaluate which of the two techniques has the best diagnostic accuracy. Methods Eighty-three patients with cerebral glioma (50 high-grade gliomas (HGGs), 33 low-grade gliomas (LGGs)) were retrospectively enrolled. All patients underwent standard MRI, H spectroscopic and perfusion echo-planar imaging MRI. For spectroscopy variations of choline/creatine, choline/N-acetyl-aspartate ratio, and lipids and lactates peak were considered. For perfusion 2.0 was considered the cerebral blood volume cut-off for progression. The combination of functional parameters gave a multiparametric score (0-2) to predict outcome. Diagnostic performance was determined by the receiver operating characteristic curve, with sensitivity, specificity, positive predictive and negative predictive values. Results In patients with LGGs a combined score of at least 1 was the best predictor for progression (odds ratio (OR) 3.91) with 8.4 months median anticipation of diagnosis compared to standard MRI. The individual advanced magnetic resonance technique did not show a diagnostic accuracy comparable to the combination of the two. Overall diagnostic accuracy area under the curve (AUC) was 0.881. In patients with HGGs the multiparametric score did not improve diagnostic accuracy significantly. Perfusion MRI was the best predictor of progression (OR 3.65), with 6.7 months median anticipation of diagnosis. Overall diagnostic accuracy AUC was 0.897. Then spectroscopy and perfusion MRI are able to identify tumour progression during follow-up earlier than standard MRI. Conclusion In patients with LGGs the combination of the functional parameters seems to be the best method for diagnosis of progression. In patients with HGGs perfusion is the best diagnostic method.

Entities:  

Keywords:  1H-MRS; MRI; glioma; perfusion MRI; spectroscopy

Mesh:

Substances:

Year:  2017        PMID: 28627984      PMCID: PMC5480793          DOI: 10.1177/1971400916688354

Source DB:  PubMed          Journal:  Neuroradiol J        ISSN: 1971-4009


  29 in total

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Journal:  Radiology       Date:  2002-12       Impact factor: 11.105

2.  The present and future management of malignant brain tumours: surgery, radiotherapy, chemotherapy.

Authors:  R Rampling; A James; V Papanastassiou
Journal:  J Neurol Neurosurg Psychiatry       Date:  2004-06       Impact factor: 10.154

Review 3.  Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma.

Authors:  S Abdulla; J Saada; G Johnson; S Jefferies; T Ajithkumar
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4.  Taming glioblastoma: targeting angiogenesis.

Authors:  Eric T Wong; Steven Brem
Journal:  J Clin Oncol       Date:  2007-10-20       Impact factor: 44.544

5.  Cerebral blood volume measurements and proton MR spectroscopy in grading of oligodendroglial tumors.

Authors:  M Vittoria Spampinato; J Keith Smith; Lester Kwock; Matthew Ewend; John D Grimme; Daniel L A Camacho; Mauricio Castillo
Journal:  AJR Am J Roentgenol       Date:  2007-01       Impact factor: 3.959

Review 6.  Diagnosis and treatment in neuro-oncology: an oncological perspective.

Authors:  J H Rees
Journal:  Br J Radiol       Date:  2011-12       Impact factor: 3.039

Review 7.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

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Review 8.  Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas.

Authors:  Frederic G Dhermain; Peter Hau; Heinrich Lanfermann; Andreas H Jacobs; Martin J van den Bent
Journal:  Lancet Neurol       Date:  2010-08-10       Impact factor: 44.182

9.  Utility of multiparametric 3-T MRI for glioma characterization.

Authors:  Bhaswati Roy; Rakesh K Gupta; Andrew A Maudsley; Rishi Awasthi; Sulaiman Sheriff; Meng Gu; Nuzhat Husain; Sudipta Mohakud; Sanjay Behari; Chandra M Pandey; Ram K S Rathore; Daniel M Spielman; Jeffry R Alger
Journal:  Neuroradiology       Date:  2013-02-02       Impact factor: 2.804

10.  Role of advanced MR imaging modalities in diagnosing cerebral gliomas.

Authors:  T Scarabino; T Popolizio; F Trojsi; G Giannatempo; S Pollice; N Maggialetti; A Carriero; A Di Costanzo; G Tedeschi; U Salvolini
Journal:  Radiol Med       Date:  2008-12-11       Impact factor: 3.469

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Journal:  Neuroradiol J       Date:  2017-11-17

2.  Changes in metabolites in the brain of patients with fibromyalgia after treatment with an NMDA receptor antagonist.

Authors:  Nicolas Fayed; Barbara Oliván; Yolanda Lopez Del Hoyo; Eva Andrés; Mari Cruz Perez-Yus; Alicia Fayed; Luisa F Angel; Antoni Serrano-Blanco; Miquel Roca; Javier Garcia Campayo
Journal:  Neuroradiol J       Date:  2019-06-19

3.  Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression.

Authors:  Marguerite Müller; Oliver Winz; Robin Gutsche; Ralph T H Leijenaar; Martin Kocher; Christoph Lerche; Christian P Filss; Gabriele Stoffels; Eike Steidl; Elke Hattingen; Joachim P Steinbach; Gabriele D Maurer; Alexander Heinzel; Norbert Galldiks; Felix M Mottaghy; Karl-Josef Langen; Philipp Lohmann
Journal:  J Neurooncol       Date:  2022-07-19       Impact factor: 4.506

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