Literature DB >> 28882867

Diagnostic Accuracy of Neuroimaging to Delineate Diffuse Gliomas within the Brain: A Meta-Analysis.

N Verburg1, F W A Hoefnagels1, F Barkhof2,3, R Boellaard2, S Goldman4, J Guo5, J J Heimans6, O S Hoekstra2, R Jain7, M Kinoshita8, P J W Pouwels9, S J Price10, J C Reijneveld6, A Stadlbauer11, W P Vandertop1, P Wesseling12,13, A H Zwinderman14, P C De Witt Hamer15.   

Abstract

BACKGROUND: Brain imaging in diffuse glioma is used for diagnosis, treatment planning, and follow-up.
PURPOSE: In this meta-analysis, we address the diagnostic accuracy of imaging to delineate diffuse glioma. DATA SOURCES: We systematically searched studies of adults with diffuse gliomas and correlation of imaging with histopathology. STUDY SELECTION: Study inclusion was based on quality criteria. Individual patient data were used, if available. DATA ANALYSIS: A hierarchic summary receiver operating characteristic method was applied. Low- and high-grade gliomas were analyzed in subgroups. DATA SYNTHESIS: Sixty-one studies described 3532 samples in 1309 patients. The mean Standard for Reporting of Diagnostic Accuracy score (13/25) indicated suboptimal reporting quality. For diffuse gliomas as a whole, the diagnostic accuracy was best with T2-weighted imaging, measured as area under the curve, false-positive rate, true-positive rate, and diagnostic odds ratio of 95.6%, 3.3%, 82%, and 152. For low-grade gliomas, the diagnostic accuracy of T2-weighted imaging as a reference was 89.0%, 0.4%, 44.7%, and 205; and for high-grade gliomas, with T1-weighted gadolinium-enhanced MR imaging as a reference, it was 80.7%, 16.8%, 73.3%, and 14.8. In high-grade gliomas, MR spectroscopy (85.7%, 35.0%, 85.7%, and 12.4) and 11C methionine-PET (85.1%, 38.7%, 93.7%, and 26.6) performed better than the reference imaging. LIMITATIONS: True-negative samples were underrepresented in these data, so false-positive rates are probably less reliable than true-positive rates. Multimodality imaging data were unavailable.
CONCLUSIONS: The diagnostic accuracy of commonly used imaging is better for delineation of low-grade gliomas than high-grade gliomas on the basis of limited evidence. Improvement is indicated from advanced techniques, such as MR spectroscopy and PET.
© 2017 by American Journal of Neuroradiology.

Entities:  

Mesh:

Year:  2017        PMID: 28882867     DOI: 10.3174/ajnr.A5368

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  17 in total

Review 1.  Paediatric gliomas: diagnosis, molecular biology and management.

Authors:  Alexandros Blionas; Dimitrios Giakoumettis; Alexia Klonou; Eleftherios Neromyliotis; Ploutarchos Karydakis; Marios S Themistocleous
Journal:  Ann Transl Med       Date:  2018-06

2.  Predictors and early survival outcomes of maximal resection in WHO grade II 1p/19q-codeleted oligodendrogliomas.

Authors:  Maya Harary; Vasileios K Kavouridis; Matthew Torre; Hasan A Zaidi; Ugonma N Chukwueke; David A Reardon; Timothy R Smith; J Bryan Iorgulescu
Journal:  Neuro Oncol       Date:  2020-03-05       Impact factor: 12.300

3.  High-resolution metabolic mapping of gliomas via patch-based super-resolution magnetic resonance spectroscopic imaging at 7T.

Authors:  Gilbert Hangel; Saurabh Jain; Elisabeth Springer; Eva Hečková; Bernhard Strasser; Michal Považan; Stephan Gruber; Georg Widhalm; Barbara Kiesel; Julia Furtner; Matthias Preusser; Thomas Roetzer; Siegfried Trattnig; Diana M Sima; Dirk Smeets; Wolfgang Bogner
Journal:  Neuroimage       Date:  2019-02-14       Impact factor: 6.556

Review 4.  Application of 7T MRS to High-Grade Gliomas.

Authors:  L McCarthy; G Verma; G Hangel; A Neal; B A Moffat; J P Stockmann; O C Andronesi; P Balchandani; C G Hadjipanayis
Journal:  AJNR Am J Neuroradiol       Date:  2022-05-26       Impact factor: 4.966

5.  Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.

Authors:  Max Blokker; Philip C de Witt Hamer; Pieter Wesseling; Marie Louise Groot; Mitko Veta
Journal:  Sci Rep       Date:  2022-07-05       Impact factor: 4.996

6.  Inter-rater agreement in glioma segmentations on longitudinal MRI.

Authors:  M Visser; D M J Müller; R J M van Duijn; M Smits; N Verburg; E J Hendriks; R J A Nabuurs; J C J Bot; R S Eijgelaar; M Witte; M B van Herk; F Barkhof; P C de Witt Hamer; J C de Munck
Journal:  Neuroimage Clin       Date:  2019-02-22       Impact factor: 4.881

Review 7.  Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview.

Authors:  Anna Falk Delgado; Danielle Van Westen; Markus Nilsson; Linda Knutsson; Pia C Sundgren; Elna-Marie Larsson; Alberto Falk Delgado
Journal:  Insights Imaging       Date:  2019-08-23

8.  Diagnosing brain tumours by routine blood tests using machine learning.

Authors:  Simon Podnar; Matjaž Kukar; Gregor Gunčar; Mateja Notar; Nina Gošnjak; Marko Notar
Journal:  Sci Rep       Date:  2019-10-09       Impact factor: 4.379

9.  Earliest radiological progression in glioblastoma by multidisciplinary consensus review.

Authors:  Roelant S Eijgelaar; Anna M E Bruynzeel; Frank J Lagerwaard; Domenique M J Müller; Freek R Teunissen; Frederik Barkhof; Marcel van Herk; Philip C De Witt Hamer; Marnix G Witte
Journal:  J Neurooncol       Date:  2018-05-18       Impact factor: 4.130

Review 10.  Diagnostic Performance and Prognostic Value of PET/CT with Different Tracers for Brain Tumors: A Systematic Review of Published Meta-Analyses.

Authors:  Giorgio Treglia; Barbara Muoio; Gianluca Trevisi; Maria Vittoria Mattoli; Domenico Albano; Francesco Bertagna; Luca Giovanella
Journal:  Int J Mol Sci       Date:  2019-09-20       Impact factor: 5.923

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