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Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Part 1
Authors: Hirschler, Lydiane; Sollmann, Nico; Schmitz‐Abecassis, Bárbara; Pinto, Joana; Arzanforoosh, Fatemehsadat; Barkhof, Frederik; Booth, Thomas; +34 Authors

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Abstract

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast‐enhanced MRI, arterial spin labeling, diffusion‐weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility‐weighted imaging, MRI‐PET, MR elastography, and MR‐based radiomics applications.Evidence Level: 3Technical Efficacy: Stage 2

Countries
United Kingdom, Belgium, Germany, Netherlands, Netherlands
Keywords

Technology and Engineering, Magnetic Resonance Spectroscopy, Brain Neoplasms, brain, contrasts, 0, Glioma, preoperative, Magnetic Resonance Imaging, GliMR 2, Diffusion Magnetic Resonance Imaging, glioma, Medicine and Health Sciences, Humans, Radiology, Nuclear Medicine and imaging, level of clinical validation

106 references, page 1 of 11

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2. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol 2021; 23:1231-1251.

3. Horbinski C, Berger T, Packer RJ, Wen PY. Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours. Nat Rev Neurol 2022;18:515-529.

4. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: A summary. Acta Neuropathol 2016;131:803-820.

5. Ellingson BM, Bendszus M, Boxerman J, et al. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro Oncol 2015;17:1188-1198. [OpenAIRE]

6. Smits M. MRI biomarkers in neuro-oncology. Nat Rev Neurol 2021;17: 486-500. [OpenAIRE]

Villanueva-Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Neurosurgery 2017;81:397-415. [OpenAIRE]

Eckel-Passow JE, Lachance DH, Molinaro AM, et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 2015;372:2499-2508.

Wijnenga MMJ, French PJ, Dubbink HJ, et al. The impact of surgery in molecularly defined low-grade glioma: An integrated clinical, radiological, and molecular analysis. Neuro Oncol 2018;20:103-112.

Clement P, Booth T, Borovecˇ ki F, et al. GliMR: Cross-border collaborations to promote advanced MRI biomarkers for glioma. J Med Biol Eng 2020;41:1-11.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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NIH| New treatment monitoring biomarkers for brain tumors using multiparametric MRI with machine learning
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01CA255123-02
  • Funding stream: NATIONAL CANCER INSTITUTE
,
NWO| A non-invasive characterisation of active multiple sclerosis lesions through chemical exchange saturation transfer (CEST) imaging
Project
  • Funder: Netherlands Organisation for Scientific Research (NWO) (NWO)
  • Project Code: 16862
iis
,
EC| ImPRESS
Project
ImPRESS
Imaging Perfusion Restrictions from Extracellular Solid Stress
  • Funder: European Commission (EC)
  • Project Code: 758657
  • Funding stream: H2020 | ERC | ERC-STG
Validated by funder | sysimport:actionset
,
NIH| Establishing the clinical utility of a consensus DSC-MRI Protocol
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01CA264992-01A1
  • Funding stream: NATIONAL CANCER INSTITUTE
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