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      Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.

      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 5 , 16 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 27 , 14 , 12 , 28 , 29 , 30 , 31 , 32 , 33 , 26 , 34 , 35 , 36 , 27 , 37 , 38 , 39 , 40 , 6 , 41 , 42 , 43 , 6 , 11 , 41 , 11 , 44 , 45 , 46 , 47 , 48 , 49 , 6 , 11 , 50 , 51 , 39
      Journal of magnetic resonance imaging : JMRI
      Wiley
      GliMR 2.0, brain, contrasts, glioma, level of clinical validation, preoperative

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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: 3. TECHNICAL EFFICACY: Stage 2.

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          Most cited references120

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

          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

            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

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                Author and article information

                Journal
                J Magn Reson Imaging
                Journal of magnetic resonance imaging : JMRI
                Wiley
                1522-2586
                1053-1807
                Jun 2023
                : 57
                : 6
                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.
                Article
                10.1002/jmri.28663
                36912262
                e832455b-8d66-4bf2-9759-c282723c93b2
                History

                contrasts,brain,GliMR 2.0,preoperative,level of clinical validation,glioma

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