Advanced MR Techniques for Preoperative Glioma Characterization: Part 2

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Abstract

Preoperative clinical 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. 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 second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility‐weighted imaging (SWI), MRI‐PET, MR elastography (MRE), and MR‐based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast‐enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion‐weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF).

Evidence Level

Technical Efficacy

Stage 2.

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Most cited references 120

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The 2021 WHO Classification of Tumors of the Central Nervous System: a summary

David N Louis, Arie Perry, Pieter Wesseling … (2021)

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg, Martin Vallières, Mahmoud A. Abdalah … (2020)

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser … (2020)

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Author and article information

Contributors

Gilbert Hangel:

ORCID: https://orcid.org/0000-0002-3986-3159

gilbert.hangel@meduniwien.ac.at

Journal

Journal ID (nlm-ta): J Magn Reson Imaging

Journal ID (iso-abbrev): J Magn Reson Imaging

Journal ID (doi): 10.1002/(ISSN)1522-2586

Journal ID (publisher-id): JMRI

Title: Journal of Magnetic Resonance Imaging

Publisher: John Wiley & Sons, Inc. (Hoboken, USA )

ISSN (Print): 1053-1807

ISSN (Electronic): 1522-2586

Publication date (Electronic): 13 March 2023

Publication date (Print): June 2023

Volume: 57

Issue: 6 ( doiID: 10.1002/jmri.v57.6 )

Pages: 1676-1695

Affiliations

[ ¹ ] Department of Neurosurgery Medical University of Vienna Vienna Austria

[ ² ] High Field MR Centre, Department of Biomedical Imaging and Image‐guided Therapy Medical University of Vienna Vienna Austria

[ ³ ] Christian Doppler Laboratory for MR Imaging Biomarkers Vienna Austria

[ ⁴ ] Medical Imaging Cluster Medical University of Vienna Vienna Austria

[ ⁵ ] Department of Radiology Leiden University Medical Center Leiden the Netherlands

[ ⁶ ] Medical Delta Foundation Delft the Netherlands

[ ⁷ ] Department of Diagnostic and Interventional Radiology University Hospital Ulm Ulm Germany

[ ⁸ ] Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar Technical University of Munich Munich Germany

[ ⁹ ] TUM‐Neuroimaging Center, Klinikum rechts der Isar Technical University of Munich Munich Germany

[ ¹⁰ ] Institute of Biomedical Engineering, Department of Engineering Science University of Oxford Oxford UK

[ ¹¹ ] Department of Radiology & Nuclear Medicine Erasmus MC Rotterdam Netherlands

[ ¹² ] Department of Radiology & Nuclear Medicine Amsterdam UMC, Vrije Universiteit Amsterdam Netherlands

[ ¹³ ] Queen Square Institute of Neurology and Centre for Medical Image Computing University College London London UK

[ ¹⁴ ] School of Biomedical Engineering and Imaging Sciences King's College London London UK

[ ¹⁵ ] Department of Neuroradiology King's College Hospital NHS Foundation Trust London UK

[ ¹⁶ ] Department of Radiology Clínica Universidad de Navarra Pamplona Spain

[ ¹⁷ ] Rede D'Or São Luiz Hospital Santa Luzia Brazil

[ ¹⁸ ] Department of Technical Disciplines in Medicine, Faculty of Health Care University of Prešov Prešov Slovakia

[ ¹⁹ ] Department of Diagnostic Sciences Ghent University Ghent Belgium

[ ²⁰ ] Department of Medical Imaging Ghent University Hospital Ghent Belgium

[ ²¹ ] IdiSNA, Instituto de Investigación Sanitaria de Navarra Pamplona Spain

[ ²² ] Department of Biomedical Imaging and Image‐guided Therapy Medical University of Vienna Vienna Austria

[ ²³ ] Research Center of Medical Image Analysis and Artificial Intelligence Danube Private University Austria

[ ²⁴ ] Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y Comunicaciones Universitat Politècnica de València Valencia Spain

[ ²⁵ ] Centre for Medical Image Computing, Department of Computer Science University College London London UK

[ ²⁶ ] Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery University College London Hospitals NHS Foundation Trust London UK

[ ²⁷ ] Institute of Biomedical Engineering Bogazici University Istanbul Istanbul Turkey

[ ²⁸ ] Cancer Center Amsterdam Amsterdam Netherlands

[ ²⁹ ] Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of Neuroinflammation University College London London UK

[ ³⁰ ] Department of Neurology Leiden University Medical Center Leiden the Netherlands

[ ³¹ ] Department of Neurology Haaglanden Medical Center Netherlands

[ ³² ] Department of Bioengineering Imperial College London London UK

[ ³³ ] Department of Radiotherapy and Imaging Institute of Cancer Research UK

[ ³⁴ ] Department of Brain Repair and Rehabilitation, Institute of Neurology University College London London UK

[ ³⁵ ] Department of Clinical Psychology and Psychotherapy, International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health Babes‐Bolyai University Romania

[ ³⁶ ] Electrical and Electronics Engineering Department Bogazici University Istanbul Istanbul Turkey

[ ³⁷ ] Department of Mechanical Engineering, Faculty of Natural Sciences and Engineering Istinye University Istanbul Istanbul Turkey

[ ³⁸ ] Department of Biophysics Medical College of Wisconsin Milwaukee Wisconsin USA

[ ³⁹ ] Department of Physics and Computational Radiology Oslo University Hospital Oslo Norway

[ ⁴⁰ ] Department of Physics University of Oslo Oslo Norway

[ ⁴¹ ] Department of Imaging Physics Delft University of Technology Delft the Netherlands

[ ⁴² ] Faculty of Engineering and Design Atlantic Technological University (ATU) Sligo Sligo Ireland

[ ⁴³ ] Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU Sligo Sligo Ireland

[ ⁴⁴ ] Department of Radiology Stanford University Stanford California USA

[ ⁴⁵ ] Stanford Cardiovascular Institute Stanford University Stanford California USA

[ ⁴⁶ ] Department of Neurosurgery St. Anne's University Hospital Brno Czechia

[ ⁴⁷ ] Faculty of Medicine Masaryk University Brno Czechia

[ ⁴⁸ ] Department of Neuroradiology Hospital Garcia de Orta Almada Portugal

[ ⁴⁹ ] C.J. Gorter MRI Center, Department of Radiology Leiden University Medical Center Leiden the Netherlands

[ ⁵⁰ ] Brain Tumour Centre Erasmus MC Cancer Institute Rotterdam the Netherlands

[ ⁵¹ ] Helmholtz‐Zentrum Dresden‐Rossendorf Institute of Radiopharmaceutical Cancer Research Dresden Germany

Author notes

[*] [* ] Address reprint requests to: G.H., Medical University of Vienna, Währinger Gürtel 18‐20, 1090 Vienna, Austria. E‐mail: gilbert.hangel@ 123456meduniwien.ac.at

Author information

Gilbert Hangel https://orcid.org/0000-0002-3986-3159

Nico Sollmann https://orcid.org/0000-0002-8120-2223

Marta Calvo‐Imirizaldu https://orcid.org/0000-0002-9063-401X

Maria A. Fernández‐Seara https://orcid.org/0000-0001-8536-6295

Elies Fuster‐Garcia https://orcid.org/0000-0002-0716-8960

Matthew Grech‐Sollars https://orcid.org/0000-0003-3881-4870

Laura Mancini https://orcid.org/0000-0002-4596-3551

Ruben Emanuel Nechifor https://orcid.org/0000-0002-0681-9522

Alpay Özcan https://orcid.org/0000-0002-3037-2812

Esin Ozturk‐Isik https://orcid.org/0000-0002-8997-878X

Siri F. Svensson https://orcid.org/0000-0002-6282-0341

Esther Warnert https://orcid.org/0000-0002-3196-1416

Moss Y. Zhao https://orcid.org/0000-0002-0210-7739

Radim Jancalek https://orcid.org/0000-0001-6834-6567

Marion Smits https://orcid.org/0000-0001-5563-2871

Jan Petr https://orcid.org/0000-0002-3201-6002

Article

Publisher ID: JMRI28663

DOI: 10.1002/jmri.28663

PMC ID: 10947037

PubMed ID: 36912262

SO-VID: e832455b-8d66-4bf2-9759-c282723c93b2

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 08 February 2023

Date received : 23 December 2022

Date accepted : 09 February 2023

Page count

Figures: 8, Tables: 2, Pages: 20, Words: 14230

Funding

Funded by: Agencia Estatal de Investigación , doi 10.13039/501100011033;

Award ID: PID2021‐127110OA‐I00

Funded by: American Heart Association , doi 10.13039/100000968;

Award ID: 826254

Funded by: Austrian Science Fund , doi 10.13039/501100002428;

Award ID: KLI 1089

Award ID: KLI 646

Funded by: Center for Scientific Review , doi 10.13039/100005440;

Award ID: CA255123

Award ID: R01 CA264992

Award ID: U01 CA176110

Funded by: Comprehensive Cancer Center grant of the Medical University of Vienna, year 2021

Funded by: European Cooperation in Science and Technology , doi 10.13039/501100000921;

Award ID: CA18206

Funded by: European Research Agency, Marie Sklodowska Curie Fellowship, Horizon 2020

Funded by: H2020 European Research Council , doi 10.13039/100010663;

Award ID: 758657‐ImPRESS

Funded by: HollandPTC‐Varian

Award ID: 2018017

Funded by: INTER‐EXCELLENCE, subprogram INTER‐COST of the Ministry of Education, Youth and Sports CZ

Award ID: LTC20027

Funded by: Ministerio de Ciencia e Innovación , doi 10.13039/501100004837;

Award ID: PI18/00084

Funded by: MS Society of the United Kingdom

Award ID: 125

Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek , doi 10.13039/501100003246;

Award ID: VICI project number 016.160.351

Award ID: Veni project number 16862

Award ID: Veni project number 91619121

Funded by: Norwegian Cancer Society and the Research Council of Norway, FRIPRO Grant Agreements

Award ID: 261984

Award ID: 303249

Funded by: South‐Eastern Norway Regional Health Authority

Award ID: 2016102

Award ID: 2017073

Award ID: 2013069

Funded by: The Turkish Directorate of Strategy and Budget under the TAM

Award ID: 2007K12‐873

Funded by: Türkiye Bilimsel ve Teknolojik Araştirma Kurumu , doi 10.13039/501100004410;

Award ID: 216S432

Funded by: Wellcome Trust , doi 10.13039/100010269;

Award ID: 203148/A/16/Z

Custom metadata

source-schema-version-number 2.0

cover-date June 2023

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:18.03.2024

ScienceOpen disciplines: Radiology & Imaging

Keywords: glioma,brain,preoperative,contrasts,glimr 2.0,level of clinical validation

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: glioma, brain, preoperative, contrasts, glimr 2.0, level of clinical validation

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2

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Abstract

Evidence Level

Technical Efficacy

Related collections

Probing cerebral hemodynamics with BOLD fMRI

Most cited references 120

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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