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Li Z, Wang D, Ooi MB, Choudhary P, Ragunathan S, Karis JP, Pipe JG, Quarles CC, Stokes AM. A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection. Magn Reson Med 2024; 92:631-644. [PMID: 38469930 DOI: 10.1002/mrm.30077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE Perfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual-echo 3D spiral technique with a single-bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) data and overcome the limitations of current EPI-based techniques. METHODS A 3D spiral-based technique with dual-echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through-plane SENSE acceleration for improved speed and image quality, in-plane variable-density undersampling combined with a sliding-window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single-echo EPI and a dual-echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single-echo EPI collected during the second bolus injection. RESULTS Volunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single-echo and dual-echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single-echo dual-dose EPI, with the additional DCE information. CONCLUSION A 3D dual-echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single-bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC-EPI techniques for brain perfusion MRI.
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Affiliation(s)
- Zhiqiang Li
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Dinghui Wang
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Poonam Choudhary
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Sudarshan Ragunathan
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - John P Karis
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - James G Pipe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - C Chad Quarles
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ashley M Stokes
- Department of Neuroradiology, Barrow Neurological Institute, Phoenix, Arizona, USA
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2
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Ziegenfeuter J, Delbridge C, Bernhardt D, Gempt J, Schmidt-Graf F, Hedderich D, Griessmair M, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Metz MC, Wiestler B. Resolving spatial response heterogeneity in glioblastoma. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06782-y. [PMID: 38837060 DOI: 10.1007/s00259-024-06782-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. METHODS Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. RESULTS Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. CONCLUSION Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.
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Affiliation(s)
- Julian Ziegenfeuter
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany.
| | - Claire Delbridge
- Department of Pathology, Technical University of Munich, 81675, München, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Jens Gempt
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Friederike Schmidt-Graf
- Department of Neurology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Dennis Hedderich
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Michael Griessmair
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie Thomas
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Hanno S Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- TranslaTUM, Technical University of Munich, 81675, München, Germany
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3
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Pons-Escoda A, Naval-Baudin P, Viveros M, Flores-Casaperalta S, Martinez-Zalacaín I, Plans G, Vidal N, Cos M, Majos C. DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions. Neuroradiology 2024:10.1007/s00234-024-03385-0. [PMID: 38834877 DOI: 10.1007/s00234-024-03385-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/29/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE The presurgical discrimination of IDH-mutant astrocytoma grade 4 from IDH-wildtype glioblastoma is crucial for patient management, especially in younger adults, aiding in prognostic assessment, guiding molecular diagnostics and surgical planning, and identifying candidates for IDH-targeted trials. Despite its potential, the full capabilities of DSC-PWI remain underexplored. This research evaluates the differentiation ability of relative-cerebral-blood-volume (rCBV) percentile values for the enhancing and non-enhancing tumor regions compared to the more commonly used mean or maximum preselected rCBV values. METHODS This retrospective study, spanning 2016-2023, included patients under 55 years (age threshold based on World Health Organization recommendations) with grade 4 astrocytic tumors and known IDH status, who underwent presurgical MR with DSC-PWI. Enhancing and non-enhancing regions were 3D-segmented to calculate voxel-level rCBV, deriving mean, maximum, and percentile values. Statistical analyses were conducted using the Mann-Whitney U test and AUC-ROC. RESULTS The cohort consisted of 59 patients (mean age 46; 34 male): 11 astrocytoma-4 and 48 glioblastoma. While glioblastoma showed higher rCBV in enhancing regions, the differences were not significant. However, non-enhancing astrocytoma-4 regions displayed notably higher rCBV, particularly in lower percentiles. The 30th rCBV percentile for non-enhancing regions was 0.705 in astrocytoma-4, compared to 0.458 in glioblastoma (p = 0.001, AUC-ROC = 0.811), outperforming standard mean and maximum values. CONCLUSION Employing an automated percentile-based approach for rCBV selection enhances differentiation capabilities, with non-enhancing regions providing more insightful data. Elevated rCBV in lower percentiles of non-enhancing astrocytoma-4 is the most distinguishable characteristic and may indicate lowly vascularized infiltrated edema, contrasting with glioblastoma's pure edema.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain.
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Barcelona, Spain.
| | - Pablo Naval-Baudin
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
| | - Mildred Viveros
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | | | - Ignacio Martinez-Zalacaín
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
| | - Gerard Plans
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
- Neurosurgery Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Noemi Vidal
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
- Pathology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Carles Majos
- Radiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Neuro-oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain
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Bhandari A, Gu B, Kashkooli FM, Zhan W. Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy. J Control Release 2024; 370:721-746. [PMID: 38718876 DOI: 10.1016/j.jconrel.2024.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/11/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.
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Affiliation(s)
- Ajay Bhandari
- Biofluids Research Lab, Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India
| | - Boram Gu
- School of Chemical Engineering, Chonnam National University, Gwangju, Republic of Korea
| | | | - Wenbo Zhan
- School of Engineering, University of Aberdeen, Aberdeen, UK.
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Herings SDA, van den Elshout R, de Wit R, Mannil M, Ravesloot C, Scheenen TWJ, Arens A, van der Kolk A, Meijer FJA, Henssen DJHA. How to evaluate perfusion imaging in post-treatment glioma: a comparison of three different analysis methods. Neuroradiology 2024:10.1007/s00234-024-03374-3. [PMID: 38714545 DOI: 10.1007/s00234-024-03374-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/01/2024] [Indexed: 05/10/2024]
Abstract
INTRODUCTION Dynamic susceptibility contrast (DSC) perfusion weighted (PW)-MRI can aid in differentiating treatment related abnormalities (TRA) from tumor progression (TP) in post-treatment glioma patients. Common methods, like the 'hot spot', or visual approach suffer from oversimplification and subjectivity. Using perfusion of the complete lesion potentially offers an objective and accurate alternative. This study aims to compare the diagnostic value and assess the subjectivity of these techniques. METHODS 50 Glioma patients with enhancing lesions post-surgery and chemo-radiotherapy were retrospectively included. Outcome was determined by clinical/radiological follow-up or biopsy. Imaging analysis used the 'hot spot', volume of interest (VOI) and visual approach. Diagnostic accuracy was compared using receiving operator characteristics (ROC) curves for the VOI and 'hot spot' approach, visual assessment was analysed with contingency tables. Inter-operator agreement was determined with Cohens kappa and intra-class coefficient (ICC). RESULTS 29 Patients suffered from TP, 21 had TRA. The visual assessment showed poor to substantial inter-operator agreement (κ = -0.72 - 0.68). Reliability of the 'hot spot' placement was excellent (ICC = 0.89), while reference placement was variable (ICC = 0.54). The area under the ROC (AUROC) of the mean- and maximum relative cerebral blood volume (rCBV) (VOI-analysis) were 0.82 and 0.72, while the rCBV-ratio ('hot spot' analysis) was 0.69. The VOI-analysis had a more balanced sensitivity and specificity compared to visual assessment. CONCLUSIONS VOI analysis of DSC PW-MRI data holds greater diagnostic accuracy in single-moment differentiation of TP and TRA than 'hot spot' or visual analysis. This study underlines the subjectivity of visual placement and assessment.
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Affiliation(s)
- Siem D A Herings
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands.
| | - Rik van den Elshout
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Rebecca de Wit
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Manoj Mannil
- University Clinic for Radiology, Westfälische Wilhelms-University Muenster and University Hospital Muenster, Albert-Schweitzer-Campus 1, E48149, Muenster, Germany
| | - Cécile Ravesloot
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Tom W J Scheenen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Anne Arens
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Anja van der Kolk
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frederick J A Meijer
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
| | - Dylan J H A Henssen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboudumc Center of Expertise Neuro-Oncology, Nijmegen, The Netherlands
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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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7
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Kang KM, Song J, Choi Y, Park C, Park JE, Kim HS, Park SH, Park CK, Choi SH. MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement. Radiology 2024; 311:e233120. [PMID: 38713025 DOI: 10.1148/radiol.233120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background According to 2021 World Health Organization criteria, adult-type diffuse gliomas include glioblastoma, isocitrate dehydrogenase (IDH)-wildtype; oligodendroglioma, IDH-mutant and 1p/19q-codeleted; and astrocytoma, IDH-mutant, even when contrast enhancement is lacking. Purpose To develop and validate simple scoring systems for predicting IDH and subsequent 1p/19q codeletion status in gliomas without contrast enhancement using standard clinical MRI sequences. Materials and Methods This retrospective study included adult-type diffuse gliomas lacking contrast at contrast-enhanced MRI from two tertiary referral hospitals between January 2012 and April 2022 with diagnoses confirmed at pathology. IDH status was predicted primarily by using T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, followed by 1p/19q codeletion prediction. A visual rating of MRI features, apparent diffusion coefficient (ADC) ratio, and relative cerebral blood volume was measured. Scoring systems were developed through univariable and multivariable logistic regressions and underwent calibration and discrimination, including internal and external validation. Results For the internal validation cohort, 237 patients were included (mean age, 44.4 years ± 14.4 [SD]; 136 male patients; 193 patients in IDH prediction and 163 patients in 1p/19q prediction). For the external validation cohort, 35 patients were included (46.1 years ± 15.3; 20 male patients; 28 patients in IDH prediction and 24 patients in 1p/19q prediction). The T2-FLAIR mismatch sign demonstrated 100% specificity and 100% positive predictive value for IDH mutation. IDH status prediction scoring system for tumors without mismatch sign included age, ADC ratio, and morphologic characteristics, whereas 1p/19q codeletion prediction for IDH-mutant gliomas included ADC ratio, cortical involvement, and mismatch sign. For IDH status and 1p/19q codeletion prediction, bootstrap-corrected areas under the receiver operating characteristic curve were 0.86 (95% CI: 0.81, 0.90) and 0.73 (95% CI: 0.65, 0.81), respectively, whereas at external validation they were 0.99 (95% CI: 0.98, 1.0) and 0.88 (95% CI: 0.63, 1.0). Conclusion The T2-FLAIR mismatch sign and scoring systems using standard clinical MRI predicted IDH and 1p/19q codeletion status in gliomas lacking contrast enhancement. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Badve and Hodges in this issue.
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Affiliation(s)
- Koung Mi Kang
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Jiyoung Song
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Yunhee Choi
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Chanrim Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Ji Eun Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Ho Sung Kim
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Sung-Hye Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Chul-Kee Park
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
| | - Seung Hong Choi
- From the Department of Radiology (K.M.K., J.S., S.H.C.), Biomedical Research Institute (C.P., C.K.P.), Department of Pathology (S.H.P.), and Department of Neurosurgery (C.K.P.), Seoul National University Hospital, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (K.M.K., S.H.C.); Division of Medical Statistics, Medical Research Collaborating Center, Seoul, Republic of Korea (Y.C.); and Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea (J.E.P., H.S.K.)
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8
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Park JE, Kim HS, Kim N, Borra R, Mouridsen K, Hansen MB, Kim YH, Hong CK, Kim JH. Prediction of pseudoprogression in post-treatment glioblastoma using dynamic susceptibility contrast-derived oxygenation and microvascular transit time heterogeneity measures. Eur Radiol 2024; 34:3061-3073. [PMID: 37848773 DOI: 10.1007/s00330-023-10324-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 08/09/2023] [Accepted: 08/18/2023] [Indexed: 10/19/2023]
Abstract
OBJECTIVES To evaluate the added value of MR dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI)-derived tumour microvascular and oxygenation information with cerebral blood volume (CBV) to distinguish pseudoprogression from true progression (TP) in post-treatment glioblastoma. METHODS This retrospective single-institution study included patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and a newly developed or enlarging measurable contrast-enhancing mass within 12 weeks after concurrent chemoradiotherapy. CBV, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2) were obtained from DSC-PWI. Predictors were selected using univariable logistic regression, and performance was measured with adjusted diagnostic odds with tumour volume and area under the curve (AUC) of receiver operating characteristics analysis. RESULTS A total of 103 patients were included (mean age, 59.6 years; 59 women), with 67 cases of TP and 36 cases of pseudoprogression. Pseudoprogression exhibited higher CTH (4.0 vs. 3.4, p = .019) and higher OEF (12.7 vs. 10.7, p = .014) than TP, but a similar CBV (1.48 vs. 1.53, p = .13) and CMRO2 (7.7 vs. 7.3s, p = .598). Independent of tumour volume, both high CTH (adjusted odds ratio [OR] 1.52; 95% confidence interval [CI]: 1.11-2.09, p = .009) and high OEF (adjusted OR 1.17; 95% CI:1.03-1.33, p = .016) were predictors of pseudoprogression. The combination of CTH, OEF, and CBV yielded higher diagnostic performance (AUC 0.71) than CBV alone (AUC 0.65). CONCLUSION High intratumoural capillary transit heterogeneity and high oxygen extraction fraction derived from DSC-PWI have enhanced the diagnostic value of CBV in pseudoprogression of post-treatment IDH-wild type glioblastoma. CLINICAL RELEVANCE STATEMENT In the early post-treatment stage of glioblastoma, pseudoprogression exhibited both high oxygen extraction fraction and high capillary transit heterogeneity and these dynamic susceptibility contrast-perfusion weighted imaging derived parameters have added value in cerebral blood volume-based noninvasive differentiation of pseudoprogression from true progression. KEY POINTS • Capillary transit time heterogeneity and oxygen extraction fraction can be measured noninvasively through processing of dynamic susceptibility contrast imaging. • Pseudoprogression exhibited higher capillary transit time heterogeneity and higher oxygen extraction fraction than true progression. • A combination of cerebral blood volume, capillary transit time heterogeneity, and oxygen extraction fraction yielded the highest diagnostic performance (area under the curve 0.71).
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Affiliation(s)
- Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Korea.
| | | | - Ronald Borra
- Cercare Medical, Inge Lehmanns Gade 10, 8000 Aarhus C, Aarhus, Denmark
| | - Kim Mouridsen
- Cercare Medical, Inge Lehmanns Gade 10, 8000 Aarhus C, Aarhus, Denmark
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Seoul, Korea
| | - Mikkel Bo Hansen
- Cercare Medical, Inge Lehmanns Gade 10, 8000 Aarhus C, Aarhus, Denmark
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Seoul, Korea
| | - Young-Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Chang-Ki Hong
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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9
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Zhou S, Gao X, Park G, Yang X, Qi B, Lin M, Huang H, Bian Y, Hu H, Chen X, Wu RS, Liu B, Yue W, Lu C, Wang R, Bheemreddy P, Qin S, Lam A, Wear KA, Andre M, Kistler EB, Newell DW, Xu S. Transcranial volumetric imaging using a conformal ultrasound patch. Nature 2024; 629:810-818. [PMID: 38778234 DOI: 10.1038/s41586-024-07381-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/02/2024] [Indexed: 05/25/2024]
Abstract
Accurate and continuous monitoring of cerebral blood flow is valuable for clinical neurocritical care and fundamental neurovascular research. Transcranial Doppler (TCD) ultrasonography is a widely used non-invasive method for evaluating cerebral blood flow1, but the conventional rigid design severely limits the measurement accuracy of the complex three-dimensional (3D) vascular networks and the practicality for prolonged recording2. Here we report a conformal ultrasound patch for hands-free volumetric imaging and continuous monitoring of cerebral blood flow. The 2 MHz ultrasound waves reduce the attenuation and phase aberration caused by the skull, and the copper mesh shielding layer provides conformal contact to the skin while improving the signal-to-noise ratio by 5 dB. Ultrafast ultrasound imaging based on diverging waves can accurately render the circle of Willis in 3D and minimize human errors during examinations. Focused ultrasound waves allow the recording of blood flow spectra at selected locations continuously. The high accuracy of the conformal ultrasound patch was confirmed in comparison with a conventional TCD probe on 36 participants, showing a mean difference and standard deviation of difference as -1.51 ± 4.34 cm s-1, -0.84 ± 3.06 cm s-1 and -0.50 ± 2.55 cm s-1 for peak systolic velocity, mean flow velocity, and end diastolic velocity, respectively. The measurement success rate was 70.6%, compared with 75.3% for a conventional TCD probe. Furthermore, we demonstrate continuous blood flow spectra during different interventions and identify cascades of intracranial B waves during drowsiness within 4 h of recording.
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Affiliation(s)
- Sai Zhou
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Xiaoxiang Gao
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Geonho Park
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Xinyi Yang
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Baiyan Qi
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Muyang Lin
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Hao Huang
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Yizhou Bian
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Hongjie Hu
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Xiangjun Chen
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Ray S Wu
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Boyu Liu
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Wentong Yue
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Chengchangfeng Lu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ruotao Wang
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Pranavi Bheemreddy
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Siyu Qin
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Arthur Lam
- Department of Anesthesiology and Critical Care, University of California San Diego, La Jolla, CA, USA
| | - Keith A Wear
- U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Michael Andre
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Erik B Kistler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - David W Newell
- Department of Neurosurgery, Seattle Neuroscience Institute, Seattle, WA, USA
| | - Sheng Xu
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA.
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Radiology, University of California San Diego, La Jolla, CA, USA.
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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10
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Stumpo V, Sayin ES, Bellomo J, Sobczyk O, van Niftrik CHB, Sebök M, Weller M, Regli L, Kulcsár Z, Pangalu A, Bink A, Duffin J, Mikulis DD, Fisher JA, Fierstra J. Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study. Front Physiol 2024; 15:1238533. [PMID: 38725571 PMCID: PMC11079274 DOI: 10.3389/fphys.2024.1238533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 04/02/2024] [Indexed: 05/12/2024] Open
Abstract
Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion.
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Affiliation(s)
- Vittorio Stumpo
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Ece Su Sayin
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada
| | - Jacopo Bellomo
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Olivia Sobczyk
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada
- Department of Anesthesia and Pain Management, University Health Network, University of Toronto, Toronto, ON, Canada
| | | | - Martina Sebök
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Zsolt Kulcsár
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Athina Pangalu
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - James Duffin
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada
| | - David D. Mikulis
- Department of Anesthesia and Pain Management, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Joseph A. Fisher
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging and the Functional Neuroimaging Lab, University Health Network, Toronto, ON, Canada
| | - Jorn Fierstra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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11
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Tang L, Wu T, Hu R, Gu Q, Yang X, Mao H. Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI). Quant Imaging Med Surg 2024; 14:2774-2787. [PMID: 38617153 PMCID: PMC11007532 DOI: 10.21037/qims-23-1471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/04/2024] [Indexed: 04/16/2024]
Abstract
Background Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. Methods First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. Results The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. Conclusions This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.
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Affiliation(s)
- Leonardo Tang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Tianhe Wu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Quanquan Gu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
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12
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Wongsawaeng D, Schwartz D, Li X, Muldoon LL, Stoller J, Stateler C, Holland S, Szidonya L, Rooney WD, Wyatt C, Ambady P, Fu R, Neuwelt EA, Barajas RF. Comparison of dynamic susceptibility contrast (DSC) using gadolinium and iron-based contrast agents in high-grade glioma at high-field MRI. Neuroradiol J 2024:19714009241242596. [PMID: 38544404 DOI: 10.1177/19714009241242596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024] Open
Abstract
PURPOSE To compare DSC-MRI using Gadolinium (GBCA) and Ferumoxytol (FBCA) in high-grade glioma at 3T and 7T MRI field strengths. We hypothesized that using FBCA at 7T would enhance the performance of DSC, as measured by contrast-to-noise ratio (CNR). METHODS Ten patients (13 lesions) were assigned to 3T (6 patients, 6 lesions) or 7T (4 patients, 7 lesions). All lesions received 0.1 mmol/kg of GBCA on day 1. Ten lesions (4 at 3T and 6 at 7T) received a lower dose (0.6 mg/kg) of FBCA, followed by a higher dose (1.0-1.2 mg/kg), while 3 lesions (2 at 3T and 1 at 7T) received only a higher dose on Day 2. CBV maps with leakage correction for GBCA but not for FBCA were generated. The CNR and normalized CBV (nCBV) were analyzed on enhancing and non-enhancing high T2W lesions. RESULTS Regardless of FBCA dose, GBCA showed higher CNR than FBCA at 7T, which was significant for high-dose FBCA (p < .05). Comparable CNR between GBCA and high-dose FBCA was observed at 3T. There was a trend toward higher CNR for FBCA at 3T than 7T. GBCA also showed nCBV twice that of FBCA at both MRI field strengths with significance at 7T. CONCLUSION GBCA demonstrated higher image conspicuity, as measured by CNR, than FBCA on 7T. The stronger T2* weighting realized with higher magnetic field strength, combined with FBCA, likely results in more signal loss rather than enhanced performance on DSC. However, at clinical 3T, both GBCA and FBCA, particularly a dosage of 1.0-1.2 mg/kg (optimal for perfusion imaging), yielded comparable CNR.
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Affiliation(s)
- Doonyaporn Wongsawaeng
- Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand
| | - Daniel Schwartz
- Advanced Imaging Research Center, Oregon Health and Science University, USA
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health and Science University, USA
| | - Leslie L Muldoon
- Department of Neurology, Oregon Health & Science University, USA
| | - Jared Stoller
- Department of Radiology, Oregon Health & Science University, USA
| | | | - Samantha Holland
- Department of Neurology, Oregon Health & Science University, USA
| | - Laszlo Szidonya
- Department of Radiology, Oregon Health & Science University, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health and Science University, USA
| | - Cory Wyatt
- Department of Radiology, Oregon Health & Science University, USA
| | | | - Rongwei Fu
- School of Public Health, Oregon Health & Science University, USA
| | - Edward A Neuwelt
- Department of Neurology, Oregon Health & Science University, USA
- Department of Neurosurgery, Oregon Health & Science University, USA
| | - Ramon F Barajas
- Advanced Imaging Research Center, Oregon Health and Science University, USA
- Department of Radiology, Oregon Health & Science University, USA
- Knight Cancer Institute, Oregon Health & Science University, USA
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13
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Garcia-Ruiz A, Pons-Escoda A, Grussu F, Naval-Baudin P, Monreal-Aguero C, Hermann G, Karunamuni R, Ligero M, Lopez-Rueda A, Oleaga L, Berbís MÁ, Cabrera-Zubizarreta A, Martin-Noguerol T, Luna A, Seibert TM, Majos C, Perez-Lopez R. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell Rep Med 2024; 5:101464. [PMID: 38471504 PMCID: PMC10983037 DOI: 10.1016/j.xcrm.2024.101464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/16/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024]
Abstract
Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.
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Affiliation(s)
- Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Albert Pons-Escoda
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain
| | | | - Gretchen Hermann
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Roshan Karunamuni
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | | | - Laura Oleaga
- Radiology Department, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - M Álvaro Berbís
- Radiology Department, HT Medica, Hospital San Juan de Dios, 14012 Cordoba, Spain
| | | | | | - Antonio Luna
- Radiology Department, HT Medica, 23008 Jaen, Spain
| | - Tyler M Seibert
- Radiation Medicine Department and Applied Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Radiology Department, University of California, San Diego, La Jolla, CA 92093, USA; Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA
| | - Carlos Majos
- Radiology Department, Bellvitge University Hospital, 08907 Barcelona, Spain; Neuro-Oncology Unit, Institut d'Investigacio Biomedica de Bellvitge (IDIBELL), 08907 Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain.
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14
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Fan H, Luo Y, Gu F, Tian B, Xiong Y, Wu G, Nie X, Yu J, Tong J, Liao X. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging 2024; 24:36. [PMID: 38486342 PMCID: PMC10938723 DOI: 10.1186/s40644-024-00682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
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Affiliation(s)
- Haiqing Fan
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yilin Luo
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Fang Gu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Bin Tian
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yongqin Xiong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Guipeng Wu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Nie
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Jing Yu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Juan Tong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Liao
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China.
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15
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Yüzkan S, Mutlu S, Karagülle M, Şam Özdemir M, Özgül H, Arıkan MA, Koçak B. Reproducibility of rCBV in glioblastomas using T2*-weighted perfusion MRI: an evaluation of sampling, normalization, and experience. Diagn Interv Radiol 2024; 30:124-134. [PMID: 37789677 PMCID: PMC10916530 DOI: 10.4274/dir.2023.232442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/17/2023] [Indexed: 10/05/2023]
Abstract
PURPOSE The reproducibility of relative cerebral blood volume (rCBV) measurements among readers with different levels of experience is a concern. This study aimed to investigate the inter-reader reproducibility of rCBV measurement of glioblastomas using the hotspot method in dynamic susceptibility contrast perfusion magnetic resonance imaging (DSC-MRI) with various strategies. METHODS In this institutional review board-approved single-center study, 30 patients with glioblastoma were retrospectively evaluated with DSC-MRI at a 3.0 Tesla scanner. Three groups of reviewers, including neuroradiologists, general radiologists, and radiology residents, calculated the rCBV based on the number of regions of interest (ROIs) and reference areas. For statistical analysis of feature reproducibility, the intraclass correlation coefficient (ICC) and Bland-Altman plots were used. Analyses were made among individuals, reader groups, reader-group pooling, and a population that contained all of them. RESULTS For individuals, the highest inter-reader reproducibility was observed between neuroradiologists [ICC: 0.527; 95% confidence interval (CI): 0.21-0.74] and between residents (ICC: 0.513; 95% CI: 0.20-0.73). There was poor reproducibility in the analyses of individuals with different levels of experience (ICC range: 0.296-0.335) and in reader-wise and group-wise pooling (ICC range: 0.296-0.335 and 0.397-0.427, respectively). However, an increase in ICC values was observed when five ROIs were used. In an analysis of all strategies, the ICC for the centrum semiovale was significantly higher than that for contralateral white matter (P < 0.001). CONCLUSION The inter-reader reproducibility of rCBV measurement was poor to moderate regardless of whether it was calculated by neuroradiologists, general radiologists, or residents, which may indicate the need for automated methods. Choosing five ROIs and using the centrum semiovale as a reference area may increase reliability for all users.
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Affiliation(s)
- Sabahattin Yüzkan
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Samet Mutlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Karagülle
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Merve Şam Özdemir
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Hamit Özgül
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Ali Arıkan
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Burak Koçak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
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Shalom ES, Khan A, Van Loo S, Sourbron SP. Current status in spatiotemporal analysis of contrast-based perfusion MRI. Magn Reson Med 2024; 91:1136-1148. [PMID: 37929645 PMCID: PMC10962600 DOI: 10.1002/mrm.29906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.
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Affiliation(s)
- Eve S. Shalom
- School of Physics and AstronomyUniversity of LeedsLeedsUK
- Division of Clinical MedicineUniversity of SheffieldSheffieldUK
| | - Amirul Khan
- School of Civil EngineeringUniversity of LeedsLeedsUK
| | - Sven Van Loo
- School of Physics and AstronomyUniversity of LeedsLeedsUK
- Department of Applied PhysicsGhent UniversityGhentBelgium
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17
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Prah MA, Schmainda KM. Practical guidance to identify and troubleshoot suboptimal DSC-MRI results. FRONTIERS IN RADIOLOGY 2024; 4:1307586. [PMID: 38445104 PMCID: PMC10913595 DOI: 10.3389/fradi.2024.1307586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.
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Affiliation(s)
- Melissa A. Prah
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI,United States
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18
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Naval-Baudin P, Pons-Escoda A, Camins À, Arroyo P, Viveros M, Castell J, Cos M, Martínez-Yélamos A, Martínez-Yélamos S, Majós C. Deeply 3D-T1-TFE hypointense voxels are characteristic of phase-rim lesions in multiple sclerosis. Eur Radiol 2024; 34:1337-1345. [PMID: 37278854 PMCID: PMC10853299 DOI: 10.1007/s00330-023-09784-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 06/07/2023]
Abstract
OBJECTIVES The development of new drugs for the treatment of progressive multiple sclerosis (MS) highlights the need for new prognostic biomarkers. Phase-rim lesions (PRLs) have been proposed as markers of progressive disease but are difficult to identify and quantify. Previous studies have identified T1-hypointensity in PRLs. The aim of this study was to compare the intensity profiles of PRLs and non-PRL white-matter lesions (nPR-WMLs) on three-dimensional T1-weighted turbo field echo (3DT1TFE) MRI. We then evaluated the performance of a derived metric as a surrogate for PRLs as potential markers for risk of disease progression. METHODS This study enrolled a cohort of relapsing-remitting (n = 10) and secondary progressive MS (n = 10) patients for whom 3 T MRI was available. PRLs and nPR-WMLs were segmented, and voxel-wise normalized T1-intensity histograms were analyzed. The lesions were divided equally into training and test datasets, and the fifth-percentile (p5)-normalized T1-intensity of each lesion was compared between groups and used for classification prediction. RESULTS Voxel-wise histogram analysis showed a unimodal histogram for nPR-WMLs and a bimodal histogram for PRLs with a large peak in the hypointense limit. Lesion-wise analysis included 1075 nPR-WMLs and 39 PRLs. The p5 intensity of PRLs was significantly lower than that of nPR-WMLs. The T1 intensity-based PRL classifier had a sensitivity of 0.526 and specificity of 0.959. CONCLUSIONS Profound hypointensity on 3DT1TFE MRI is characteristic of PRLs and rare in other white-matter lesions. Given the widespread availability of T1-weighted imaging, this feature might serve as a surrogate biomarker for smoldering inflammation. CLINICAL RELEVANCE STATEMENT Quantitative analysis of 3DT1TFE may detect deeply hypointense voxels in multiple sclerosis lesions, which are highly specific to PRLs. This could serve as a specific indicator of smoldering inflammation in MS, aiding in early detection of disease progression. KEY POINTS • Phase-rim lesions (PRLs) in multiple sclerosis present a characteristic T1-hypointensity on 3DT1TFE MRI. • Intensity-normalized 3DT1TFE can be used to systematically identify and quantify these deeply hypointense foci. • Deep T1-hypointensity may act as an easily detectable, surrogate marker for PRLs.
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Affiliation(s)
- Pablo Naval-Baudin
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain.
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain.
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain.
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain.
| | - Albert Pons-Escoda
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
| | - Àngels Camins
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
| | - Pablo Arroyo
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
| | - Mildred Viveros
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Josep Castell
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Mònica Cos
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Antonio Martínez-Yélamos
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
- Multiple Sclerosis Unit, Department of Neurology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Sergio Martínez-Yélamos
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
- Multiple Sclerosis Unit, Department of Neurology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Carles Majós
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
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Yamashita K, Togao O, Kikuchi K, Kuga D, Sangatsuda Y, Fujioka Y, Yoshimoto K, Ishigami K. The cortical high-flow sign of oligodendroglioma, IDH-mutant and 1p/19q-codeleted: comparison between arterial spin labeling and dynamic susceptibility contrast methods. Neuroradiology 2024; 66:187-192. [PMID: 38127124 DOI: 10.1007/s00234-023-03267-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023]
Abstract
PURPOSE The cortical high-flow sign with the non-enhancing area was reportedly found to be more frequent with oligodendroglioma, IDH-mutant and 1p/19q codeleted (ODG IDHm-codel) than with IDH-wildtype or astrocytoma, IDH-mutant on arterial spin labeling (ASL) in diffuse gliomas. This study aimed to compare the identification rate of the cortical high-flow sign on ASL in patients with ODG IDHm-codel to that on dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI). METHODS Participants consisted of 32 adult ODG IDHm-codel patients with pathologically confirmed. Subtraction images were generated from paired control and label images on ASL. For DSC, dynamic T2*-weighted perfusion weighted images were obtained after pre-bolus of gadolinium-based contrast agent. Regional cerebral blood flow/volume maps were generated based on the concentration-time curve and arterial input function. Tumor-affecting cortices without contrast enhancement on conventional MR imaging were targeted. The identification rate of the cortical high-flow sign was compared between ASL and DSC using the Pearson's Chi-Square test. RESULTS Frequency of the cortical high-flow sign was significantly higher on ASL (18/32, 56.3%; p < 0.001) than on DSC (5/32, 15.6%). All cases with the positive cortical high-flow sign on DSC were identified on ASL. CONCLUSION ASL effectively identifies the cortical high-flow sign in ODG IDHm-codel, surpassing DSC in identification rates.
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Affiliation(s)
- Koji Yamashita
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kazufumi Kikuchi
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yuhei Sangatsuda
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yutaka Fujioka
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Koji Yoshimoto
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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20
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Bammer R, Amukotuwa SA. Dynamic Susceptibility Contrast Perfusion, Part 2: Deployment With and Without Contrast Leakage Present. Magn Reson Imaging Clin N Am 2024; 32:25-45. [PMID: 38007281 DOI: 10.1016/j.mric.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
A thorough description of perfusion analysis and basic DSC MR acquisition concepts has been described in the companion article to this article, which the interested reader may also find useful. DSC MR imaging requires an MR imaging pulse sequence that is sensitive to magnetic susceptibility changes to register the contrast concentration changes when GBCA passes through the capillary bed. Any pulse sequence that has T2∗-weighting can be used to pick up these changes, provided that the sequence is fast enough to acquire an image of that slice of tissue at least every 1 to 2 second.
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Affiliation(s)
- Roland Bammer
- Department of Radiology and Radiological Sciences, Monash University, Clayton, VIC, Australia; Monash Imaging, Monash Health, Clayton, VIC, Australia.
| | - Shalini A Amukotuwa
- Department of Radiology and Radiological Sciences, Monash University, Clayton, VIC, Australia; Monash Imaging, Monash Health, Clayton, VIC, Australia
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21
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Martinez-Zalacain I, Castell J, Camins A, Vidal N, Bruna J, Cos M, Perez-Lopez R, Oleaga L, Warnert E, Smits M, Majos C. Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles. Eur Radiol 2024:10.1007/s00330-024-10611-z. [PMID: 38282078 DOI: 10.1007/s00330-024-10611-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/13/2023] [Accepted: 01/01/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVE Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation. METHODS Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation. RESULTS The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation. CONCLUSIONS Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes. CLINICAL RELEVANCE STATEMENT The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial. KEY POINTS • The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain.
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain.
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain.
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain.
| | - Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Ignacio Martinez-Zalacain
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Josep Castell
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Angels Camins
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Noemi Vidal
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Pathology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Jordi Bruna
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Laura Oleaga
- Radiology Department, Hospital Clinic de Barcelona, Villarroel 170, 08036, Barcelona, Spain
| | - Esther Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
| | - Carles Majos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
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Lambrecht S, Liu D, Dzaye O, Kamson DO, Reis J, Liebig T, Holdhoff M, Van Zijl P, Qin Q, Lin DDM. Velocity-Selective Arterial Spin Labeling Perfusion in Monitoring High Grade Gliomas Following Therapy: Clinical Feasibility at 1.5T and Comparison with Dynamic Susceptibility Contrast Perfusion. Brain Sci 2024; 14:126. [PMID: 38391701 PMCID: PMC10886779 DOI: 10.3390/brainsci14020126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/19/2024] [Accepted: 01/20/2024] [Indexed: 02/24/2024] Open
Abstract
MR perfusion imaging is important in the clinical evaluation of primary brain tumors, particularly in differentiating between true progression and treatment-induced change. The utility of velocity-selective ASL (VSASL) compared to the more commonly utilized DSC perfusion technique was assessed in routine clinical surveillance MR exams of 28 patients with high-grade gliomas at 1.5T. Using RANO criteria, patients were assigned to two groups, one with detectable residual/recurrent tumor ("RT", n = 9), and the other with no detectable residual/recurrent tumor ("NRT", n = 19). An ROI was drawn to encompass the largest dimension of the lesion with measures normalized against normal gray matter to yield rCBF and tSNR from VSASL, as well as rCBF and leakage-corrected relative CBV (lc-rCBV) from DSC. VSASL (rCBF and tSNR) and DSC (rCBF and lc-rCBV) metrics were significantly higher in the RT group than the NRT group allowing adequate discrimination (p < 0.05, Mann-Whitney test). Lin's concordance analyses showed moderate to excellent concordance between the two methods, with a stronger, moderate correlation between VSASL rCBF and DSC lc-rCBV (r = 0.57, p = 0.002; Pearson's correlation). These results suggest that VSASL is clinically feasible at 1.5T and has the potential to offer a noninvasive alternative to DSC perfusion in monitoring high-grade gliomas following therapy.
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Affiliation(s)
- Sebastian Lambrecht
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Institute of Neuroradiology, University Hospital LMU Munich, 81377 Munich, Germany
| | - Dapeng Liu
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
| | - Omar Dzaye
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - David O Kamson
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jonas Reis
- Institute of Neuroradiology, University Hospital LMU Munich, 81377 Munich, Germany
| | - Thomas Liebig
- Institute of Neuroradiology, University Hospital LMU Munich, 81377 Munich, Germany
| | - Matthias Holdhoff
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Peter Van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
| | - Qin Qin
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
| | - Doris D M Lin
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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Elschot EP, Backes WH, van den Kerkhof M, Postma AA, Kroon AA, Jansen JFA. Cerebral Microvascular Perfusion Assessed in Elderly Adults by Spin-Echo Dynamic Susceptibility Contrast MRI at 7 Tesla. Tomography 2024; 10:181-192. [PMID: 38250960 PMCID: PMC10819808 DOI: 10.3390/tomography10010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/15/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024] Open
Abstract
Perfusion measures of the total vasculature are commonly derived with gradient-echo (GE) dynamic susceptibility contrast (DSC) MR images, which are acquired during the early passes of a contrast agent. Alternatively, spin-echo (SE) DSC can be used to achieve specific sensitivity to the capillary signal. For an improved contrast-to-noise ratio, ultra-high-field MRI makes this technique more appealing to study cerebral microvascular physiology. Therefore, this study assessed the applicability of SE-DSC MRI at 7 T. Forty-one elderly adults underwent 7 T MRI using a multi-slice SE-EPI DSC sequence. The cerebral blood volume (CBV) and cerebral blood flow (CBF) were determined in the cortical grey matter (CGM) and white matter (WM) and compared to values from the literature. The relation of CBV and CBF with age and sex was investigated. Higher CBV and CBF values were found in CGM compared to WM, whereby the CGM-to-WM ratios depended on the amount of largest vessels excluded from the analysis. CBF was negatively associated with age in the CGM, while no significant association was found with CBV. Both CBV and CBF were higher in women compared to men in both CGM and WM. The current study verifies the possibility of quantifying cerebral microvascular perfusion with SE-DSC MRI at 7 T.
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Affiliation(s)
- Elles P. Elschot
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (E.P.E.)
- MHeNs School for Mental Health and Neuroscience, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (E.P.E.)
- MHeNs School for Mental Health and Neuroscience, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Marieke van den Kerkhof
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (E.P.E.)
- MHeNs School for Mental Health and Neuroscience, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Alida A. Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (E.P.E.)
- MHeNs School for Mental Health and Neuroscience, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Abraham A. Kroon
- CARIM School for Cardiovascular Diseases, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Center+, P. Debyelaan 25, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; (E.P.E.)
- MHeNs School for Mental Health and Neuroscience, Maastricht University, Minderbroedersberg 4-6, P.O. Box 616, 6200 MD Maastricht, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, P.O. Box 513, 5612 AP Eindhoven, The Netherlands
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24
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Patel KS, Yao J, Cho NS, Sanvito F, Tessema K, Alvarado A, Dudley L, Rodriguez F, Everson R, Cloughesy TF, Salamon N, Liau LM, Kornblum HI, Ellingson BM. pH-Weighted amine chemical exchange saturation transfer echo planar imaging visualizes infiltrating glioblastoma cells. Neuro Oncol 2024; 26:115-126. [PMID: 37591790 PMCID: PMC10768991 DOI: 10.1093/neuonc/noad150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Given the invasive nature of glioblastoma, tumor cells exist beyond the contrast-enhancing (CE) region targeted during treatment. However, areas of non-enhancing (NE) tumors are difficult to visualize and delineate from edematous tissue. Amine chemical exchange saturation transfer echo planar imaging (CEST-EPI) is a pH-sensitive molecular magnetic resonance imaging technique that was evaluated in its ability to identify infiltrating NE tumors and prognosticate survival. METHODS In this prospective study, CEST-EPI was obtained in 30 patients and areas with elevated CEST contrast ("CEST+" based on the asymmetry in magnetization transfer ratio: MTRasym at 3 ppm) within NE regions were quantitated. Median MTRasym at 3 ppm and volume of CEST + NE regions were correlated with progression-free survival (PFS). In 20 samples from 14 patients, image-guided biopsies of these areas were obtained to correlate MTRasym at 3 ppm to tumor and non-tumor cell burden using immunohistochemistry. RESULTS In 15 newly diagnosed and 15 recurrent glioblastoma, higher median MTRasym at 3ppm within CEST + NE regions (P = .007; P = .0326) and higher volumes of CEST + NE tumor (P = .020; P < .001) were associated with decreased PFS. CE recurrence occurred in areas of preoperative CEST + NE regions in 95.4% of patients. MTRasym at 3 ppm was correlated with presence of tumor, cell density, %Ki-67 positivity, and %CD31 positivity (P = .001; P < .001; P < .001; P = .001). CONCLUSIONS pH-weighted amine CEST-EPI allows for visualization of NE tumor, likely through surrounding acidification of the tumor microenvironment. The magnitude and volume of CEST + NE tumor correlates with tumor cell density, degree of proliferating or "active" tumor, and PFS.
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Affiliation(s)
- Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Kaleab Tessema
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Alvaro Alvarado
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Lindsey Dudley
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Fausto Rodriguez
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Richard Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Harley I Kornblum
- The Intellectual and Developmental Disabilities Research Center and Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Benjamin M Ellingson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, California, USA
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25
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Tseng CH, Jaspers J, Romero AM, Wielopolski P, Smits M, van Osch MJP, Vos F. Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI. NMR IN BIOMEDICINE 2024; 37:e5038. [PMID: 37712359 DOI: 10.1002/nbm.5038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/02/2023] [Accepted: 08/23/2023] [Indexed: 09/16/2023]
Abstract
The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measuredR 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.
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Affiliation(s)
- Chih-Hsien Tseng
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
| | - Jaap Jaspers
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Alejandra Mendez Romero
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Piotr Wielopolski
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marion Smits
- Medical Delta, Delft, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Brain Tumour Center, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Matthias J P van Osch
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frans Vos
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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26
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Sanvito F, Kaufmann TJ, Cloughesy TF, Wen PY, Ellingson BM. Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration. FRONTIERS IN RADIOLOGY 2023; 3:1267615. [PMID: 38152383 PMCID: PMC10751345 DOI: 10.3389/fradi.2023.1267615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/24/2023] [Indexed: 12/29/2023]
Abstract
Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, MA, United States
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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27
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Pons-Escoda A, Garcia-Ruiz A, Garcia-Hidalgo C, Gil-Solsona R, Naval-Baudin P, Martin-Noguerol T, Fernandez-Coello A, Flores-Casaperalta S, Fernandez-Viñas M, Gago-Ferrero P, Oleaga L, Perez-Lopez R, Majos C. MR dynamic-susceptibility-contrast perfusion metrics in the presurgical discrimination of adult solitary intra-axial cerebellar tumors. Eur Radiol 2023; 33:9120-9129. [PMID: 37439938 DOI: 10.1007/s00330-023-09892-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/16/2023] [Accepted: 05/30/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVES Adult solitary intra-axial cerebellar tumors are uncommon. Their presurgical differentiation based on neuroimaging is crucial, since management differs substantially. Comprehensive full assessment of MR dynamic-susceptibility-contrast perfusion-weighted imaging (DSC-PWI) may reveal key differences between entities. This study aims to provide new insights on perfusion patterns of these tumors and to explore the potential of DSC-PWI in their presurgical discrimination. METHODS Adult patients with a solitary cerebellar tumor on presurgical MR and confirmed histological diagnosis of metastasis, medulloblastoma, hemangioblastoma, or pilocytic astrocytoma were retrospectively retrieved (2008-2023). Volumetric segmentation of tumors and normal-appearing white matter (for normalization) was semi-automatically performed on CE-T1WI and coregistered with DSC-PWI. Mean normalized values per patient tumor-mask of relative cerebral blood volume (rCBV), percentage of signal recovery (PSR), peak height (PH), and normalized time-intensity curves (nTIC) were extracted. Statistical comparisons were done. Then, the dataset was split into training (75%) and test (25%) cohorts and a classifier was created considering nTIC, rCBV, PSR, and PH in the model. RESULTS Sixty-eight patients (31 metastases, 13 medulloblastomas, 13 hemangioblastomas, and 11 pilocytic astrocytomas) were included. Relevant differences between tumor types' nTICs were demonstrated. Hemangioblastoma showed the highest rCBV and PH, pilocytic astrocytoma the highest PSR. All parameters showed significant differences on the Kruskal-Wallis tests (p < 0.001). The classifier yielded an accuracy of 98% (47/48) in the training and 85% (17/20) in the test sets. CONCLUSIONS Intra-axial cerebellar tumors in adults have singular and significantly different DSC-PWI signatures. The combination of perfusion metrics through data-analysis rendered excellent accuracies in discriminating these entities. CLINICAL RELEVANCE STATEMENT In this study, the authors constructed a classifier for the non-invasive imaging presurgical diagnosis of adult intra-axial cerebellar tumors. The resultant tool can be a support for decision-making in the clinical practice and enables optimal personalized patient management. KEY POINTS • Adult intra-axial cerebellar tumors exhibit specific, singular, and statistically significant different MR dynamic-susceptibility-contrast perfusion-weighted imaging (DSC-PWI) signatures. • Data-analysis, applied to MR DSC-PWI, could provide added value in the presurgical diagnosis of solitary cerebellar metastasis, medulloblastoma, hemangioblastoma, and pilocytic astrocytoma. • A classifier based on DSC-PWI metrics yields excellent accuracy rates and could be used as a support tool for radiologic diagnosis with clinician-friendly displays.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Hospital Universitari de Bellvitge, Feixa Llarga SN, 08907, L'Hospitalet de Llobregat, Barcelona, Spain.
- Neuro-Oncology Unit, Institut d'Investigació Biomedica de Bellvitge, IDIBELL, Barcelona, Spain.
| | | | | | - Ruben Gil-Solsona
- Institut de Diagnostic Ambiental i Estudis de l'Aigua (IDAEA) - CSIC, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Hospital Universitari de Bellvitge, Feixa Llarga SN, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
| | | | - Alejandro Fernandez-Coello
- Neuro-Oncology Unit, Institut d'Investigació Biomedica de Bellvitge, IDIBELL, Barcelona, Spain
- Neurosurgery Department, Hospital Universitari de Bellvitge, Barcelona, Spain
- Biomedical Research Networking Centers of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Susanie Flores-Casaperalta
- Radiology Department, Hospital Universitari de Bellvitge, Feixa Llarga SN, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Montserrat Fernandez-Viñas
- Radiology Department, Hospital Universitari de Bellvitge, Feixa Llarga SN, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Pablo Gago-Ferrero
- Institut de Diagnostic Ambiental i Estudis de l'Aigua (IDAEA) - CSIC, Barcelona, Spain
| | - Laura Oleaga
- Radiology Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | | | - Carles Majos
- Radiology Department, Hospital Universitari de Bellvitge, Feixa Llarga SN, 08907, L'Hospitalet de Llobregat, Barcelona, Spain
- Neuro-Oncology Unit, Institut d'Investigació Biomedica de Bellvitge, IDIBELL, Barcelona, Spain
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28
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Wang F, Zhou X, Chen R, Kang J, Yang X, Lin J, Liu F, Cao D, Xing Z. Improved performance of non-preloaded and high flip-angle dynamic susceptibility contrast perfusion-weighted imaging sequences in the presurgical differentiation of brain lymphoma and glioblastoma. Eur Radiol 2023; 33:8800-8808. [PMID: 37439934 DOI: 10.1007/s00330-023-09917-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 03/26/2023] [Accepted: 05/08/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVE This study aimed to compare the accuracy of relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from high flip-angle dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) sequences with and without contrast agent (CA) preload for presurgical discrimination of brain glioblastoma and lymphoma. METHODS Consecutive 336 patients (glioblastoma, 236; PCNSL, 100) were included. All the patients underwent DSC-PWI on 3.0-T magnetic resonance units before surgery. The rCBV and PSR with preloaded and non-preloaded CA were measured. The means of the continuous variables were compared using Welch's t-test. The diagnostic accuracies of the individual parameters were compared using the receiver operating characteristic curve analysis. RESULTS The rCBV was higher with preloaded CA than with non-preloaded CA (glioblastoma, 10.20 vs. 8.90, p = 0.020; PCNSL, 3.88 vs. 3.27, p = 0.020). The PSR was lower with preloaded CA than with non-preloaded CA (glioblastoma, 0.59 vs. 0.90; PCNSL, 0.70 vs. 1.63; all p < 0.001). Regarding the differentiation of glioblastoma and PCNSL, the AUC of rCBV with preloaded CA was indistinguishable from that of non-preloaded CA (0.940 vs. 0.949, p = 0.703), whereas the area under the curve of PSR with preloaded CA was lower than non-preloaded CA (0.529 vs. 0.884, p < 0.001). CONCLUSION With preloaded CA, diagnostic performance in differentiating glioblastoma and PCNSL did not improve for rCBV and it was decreased for PSR. Therefore, high flip-angle non-preload DSC-PWI sequences offer excellent accuracy and may be of choice sequence for presurgical discrimination of brain lymphoma and glioblastoma. CLINICAL RELEVANCE STATEMENT High flip-angle DSC-PWI using non-preloaded CA may be an excellent diagnostic method for distinguishing glioblastoma from PCNSL. KEY POINTS • Differentiating primary central nervous system lymphoma and glioblastoma accurately is critical for their management. • DSC-PWI sequences optimised for the most accurate CBV calculations may not be the optimal sequences for presurgical brain tumour diagnosis as they could be masquerading leakage phenomena that may provide interesting information in terms of differential diagnosis. • High flip-angle non-preloaded DSC-PWI sequences render the best accuracy in the presurgical differentiation of brain lymphoma and glioblastoma.
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Affiliation(s)
- Feng Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Xiaofang Zhou
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Ruiquan Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Jie Kang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Xinyi Yang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Jinzhu Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China
| | - Fang Liu
- Department of Hyperbaric Oxygen, The First Affiliated Hospital of Fujian Medical University, Fuzhou, People's Republic of China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
- Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
| | - Zhen Xing
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, Fujian, 350005, People's Republic of China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People's Republic of China.
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Pons-Escoda A. "Everything Everywhere All at Once": Unraveling perfusion, permeability, and leakage effects in neurooncology with a single-dose, single-acquisition dual-echo DSC. Eur Radiol 2023:10.1007/s00330-023-10277-z. [PMID: 37917358 DOI: 10.1007/s00330-023-10277-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/16/2023] [Accepted: 08/23/2023] [Indexed: 11/04/2023]
Affiliation(s)
- Albert Pons-Escoda
- Neuroradiology Department, Hospital Universitari de Bellvitge, Barcelona, Spain.
- Neuro-Oncology Unit, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Barcelona, Spain.
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30
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Pons-Escoda A, Smits M. Dynamic-susceptibility-contrast perfusion-weighted-imaging (DSC-PWI) in brain tumors: a brief up-to-date overview for clinical neuroradiologists. Eur Radiol 2023; 33:8026-8030. [PMID: 37178200 DOI: 10.1007/s00330-023-09729-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/26/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023]
Affiliation(s)
- Albert Pons-Escoda
- Department of Radiology, Hospital Universitari de Bellvitge, Barcelona, Spain.
- Neurooncology Unit, Institut d'Investigacio Biomedica de Bellvitge - IDIBELL, Barcelona, Spain.
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Brain Tumour Centre, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
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Álvarez-Torres MDM, López-Cerdán A, Andreu Z, de la Iglesia Vayá M, Fuster-Garcia E, García-García F, García-Gómez JM. Vascular differences between IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 at imaging and transcriptomic levels. NMR IN BIOMEDICINE 2023; 36:e5004. [PMID: 37482922 DOI: 10.1002/nbm.5004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/31/2023] [Accepted: 06/23/2023] [Indexed: 07/25/2023]
Abstract
Global agreement in central nervous system (CNS) tumor classification is essential for predicting patient prognosis and determining the correct course of treatment, as well as for stratifying patients for clinical trials at international level. The last update by the World Health Organization of CNS tumor classification and grading in 2021 considered, for the first time, IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 as different tumors. Mutations in the genes isocitrate dehydrogenase (IDH) 1 and 2 occur early and, importantly, contribute to gliomagenesis. IDH mutation produces a metabolic reprogramming of tumor cells, thus affecting the processes of hypoxia and vascularity, resulting in a clear advantage for those patients who present with IDH-mutated astrocytomas. Despite the clinical relevance of IDH mutation, current protocols do not include full sequencing for every patient. Alternative biomarkers could be useful and complementary to obtain a more reliable classification. In this sense, magnetic resonance imaging (MRI)-perfusion biomarkers, such as relative cerebral blood volume and flow, could be useful from the moment of presurgery, without incurring additional financial costs or requiring extra effort. The main purpose of this work is to analyze the vascular and hemodynamic differences between IDH-wildtype glioblastoma and IDH-mutant astrocytoma. To achieve this, we evaluate and validate the association between dynamic susceptibility contrast-MRI perfusion biomarkers and IDH mutation status. In addition, to gain a deeper understanding of the vascular differences in astrocytomas depending on the IDH mutation, we analyze the transcriptomic bases of the vascular differences.
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Affiliation(s)
- María Del Mar Álvarez-Torres
- Biomedical Data Science Laboratory, ITACA (Instituto de Información y Tecnología de las Comunicaciones), Universitat Politècnica de València, Valencia, Spain
| | - Adolfo López-Cerdán
- Unidad Mixta de Imagen Biomédica FISABIO-CIPF (Centro Investigación Príncipe Felipe), Valencia, Spain
| | - Zoraida Andreu
- Foundation Valencian Institute of Oncology (FIVO), Valencia, Spain
| | - Maria de la Iglesia Vayá
- Unidad Mixta de Imagen Biomédica FISABIO-CIPF (Centro Investigación Príncipe Felipe), Valencia, Spain
| | - Elies Fuster-Garcia
- Biomedical Data Science Laboratory, ITACA (Instituto de Información y Tecnología de las Comunicaciones), Universitat Politècnica de València, Valencia, Spain
| | | | - Juan M García-Gómez
- Biomedical Data Science Laboratory, ITACA (Instituto de Información y Tecnología de las Comunicaciones), Universitat Politècnica de València, Valencia, Spain
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Sanvito F, Raymond C, Cho NS, Yao J, Hagiwara A, Orpilla J, Liau LM, Everson RG, Nghiemphu PL, Lai A, Prins R, Salamon N, Cloughesy TF, Ellingson BM. Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI. Eur Radiol 2023; 34:10.1007/s00330-023-10215-z. [PMID: 37882836 PMCID: PMC11045669 DOI: 10.1007/s00330-023-10215-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/05/2023] [Accepted: 07/27/2023] [Indexed: 10/27/2023]
Abstract
OBJECTIVE To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection. MATERIALS AND METHODS Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression). RESULTS In IDH wild-type gliomas (IDHwt-i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09-0.14). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%. CONCLUSIONS Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR1,ss), with a single contrast injection. CLINICAL RELEVANCE STATEMENT Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture. KEY POINTS • Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.
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Affiliation(s)
- Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Viale Camillo Golgi 19, 27100, Pavia, Italy
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
- Department of Radiology, Juntendo University School of Medicine, Bunkyo City, 2-Chōme-1-1 Hongō, Tokyo, 113-8421, Japan
| | - Joey Orpilla
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Robert Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, CA, 90024, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, 7400 Boelter Hall, Los Angeles, CA, 90095, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, 885 Tiverton Dr, Los Angeles, CA, 90095, USA.
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Nierobisch N, Ludovichetti R, Kadali K, Fierstra J, Hüllner M, Michels L, Achangwa NR, Alcaide-Leon P, Weller M, Kulcsar Z, Hainc N. Comparison of clinically available dynamic susceptibility contrast post processing software to differentiate progression from pseudoprogression in post-treatment high grade glioma. Eur J Radiol 2023; 167:111076. [PMID: 37666072 DOI: 10.1016/j.ejrad.2023.111076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/16/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
INTRODUCTION The purpose of this retrospective study was to compare two, widely available software packages for calculation of Dynamic Susceptibility Contrast (DSC) perfusion MRI normalized relative Cerebral Blood Volume (rCBV) values to differentiate tumor progression from pseudoprogression in treated high-grade glioma patients. MATERIAL AND METHODS rCBV maps processed by Siemens Syngo.via (Siemens Healthineers) and Olea Sphere (Olea Medical) software packages were co-registered to contrast-enhanced T1 (T1-CE). Regions of interest based on T1-CE were transferred to the rCBV maps. rCBV was calculated using mean values and normalized using contralateral normal- appearing white matter. The Wilcoxon test was performed to assess for significant differences, and software-specific optimal rCBV cutoff values were determined using the Youden index. Interrater reliability was evaluated for two raters using the intraclass correlation coefficient. RESULTS 41 patients (18 females; median age = 59 years; range 21-77 years) with 49 new or size-increasing post-treatment contrast-enhancing lesions were included (tumor progression = 40 lesions; pseudoprogression = 9 lesions). Optimal rCBV cutoffs of 1.31 (Syngo.via) and 2.40 (Olea) were significantly different, with an AUC of 0.74 and 0.78, respectively. Interrater reliability was 0.85. DISCUSSION We demonstrate that different clinically available MRI DSC-perfusion software packages generate significantly different rCBV cutoff values for the differentiation of tumor progression from pseudoprogression in standard-of-care treated high grade gliomas. Physicians may want to determine the unique value of their perfusion software packages on an institutional level in order to maximize diagnostic accuracy when faced with this clinical challenge. Furthermore, combined with implementation of current DSC-perfusion recommendations, multi-center comparability will be improved.
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Affiliation(s)
- Nathalie Nierobisch
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Riccardo Ludovichetti
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | | | - Jorn Fierstra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Ngwe Rawlings Achangwa
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Paula Alcaide-Leon
- Department of Medical Imaging, University of Toronto, Toronto, Canada; Joint Department of Medical Imaging, University Health Network, Toronto, Canada
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Zsolt Kulcsar
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland
| | - Nicolin Hainc
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
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Phillips KA, Kamson DO, Schiff D. Disease Assessments in Patients with Glioblastoma. Curr Oncol Rep 2023; 25:1057-1069. [PMID: 37470973 DOI: 10.1007/s11912-023-01440-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2023] [Indexed: 07/21/2023]
Abstract
PURPOSE OF REVIEW The neuro-oncology team faces a unique challenge when assessing treatment response in patients diagnosed with glioblastoma. Magnetic resonance imaging (MRI) remains the standard imaging modality for measuring therapeutic response in both clinical practice and clinical trials. However, even for the neuroradiologist, MRI interpretations are not straightforward because of tumor heterogeneity, as evidenced by varying degrees of enhancement, infiltrating tumor patterns, cellular densities, and vasogenic edema. The situation is even more perplexing following therapy since treatment-related changes can mimic viable tumor. Additionally, antiangiogenic therapies can dramatically decrease contrast enhancement giving the false impression of decreasing tumor burden. Over the past few decades, several approaches have emerged to augment and improve visual interpretation of glioblastoma response to therapeutics. Herein, we summarize the state of the art for evaluating the response of glioblastoma to standard therapies and investigational agents as well as challenges and future directions for assessing treatment response in neuro-oncology. RECENT FINDINGS Monitoring glioblastoma responses to standard therapy and novel agents has been fraught with many challenges and limitations over the past decade. Excitingly, new promising methods are emerging to help address these challenges. Recently, the Response Assessment in Neuro-Oncology (RANO) working group proposed an updated response criteria (RANO 2.0) for the evaluation of all grades of glial tumors regardless of IDH status or therapies being evaluated. In addition, advanced neuroimaging techniques, such as histogram analysis, parametric response maps, morphometric segmentation, radio pharmacodynamics approaches, and the integrating of amino acid radiotracers in the tumor evaluation algorithm may help resolve equivocal lesion interpretations without operative intervention. Moreover, the introduction of other techniques, such as liquid biopsy and artificial intelligence could complement conventional visual assessment of glioblastoma response to therapies. Neuro-oncology has evolved over the past decade and has achieved significant milestones, including the establishment of new standards of care, emerging therapeutic options, and novel clinical, translational, and basic research. More recently, the integration of histopathology with molecular features for tumor classification has marked an important paradigm shift in brain tumor diagnosis. In a similar manner, treatment response monitoring in neuro-oncology has made considerable progress. While most techniques are still in their inception, there is an emerging body of evidence for clinical application. Further research will be critically important for the development of impactful breakthroughs in this area of the field.
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Affiliation(s)
- Kester A Phillips
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment at Swedish Neuroscience Institute, 550 17Th Ave Suite 540, Seattle, WA, 98122, USA
| | - David O Kamson
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, 201 North Broadway, Skip Viragh Outpatient Cancer Building, 9Th Floor, Room 9177, Mailbox #3, Baltimore, MD, 21218, USA
| | - David Schiff
- Division of Neuro-Oncology, University of Virginia Health System, 1300 Jefferson Park Avenue, West Complex, Room 6225, Charlottesville, VA, 22903, USA.
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Jun Y, Park YW, Shin H, Shin Y, Lee JR, Han K, Ahn SS, Lim SM, Hwang D, Lee SK. Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning. Eur Radiol 2023; 33:6124-6133. [PMID: 37052658 DOI: 10.1007/s00330-023-09590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/30/2022] [Accepted: 02/09/2023] [Indexed: 04/14/2023]
Abstract
OBJECTIVES To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation. METHODS In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model. RESULTS On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675-0.690 and accuracies of 65.6-68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading. CONCLUSIONS An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation. KEY POINTS • The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.
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Affiliation(s)
- Yohan Jun
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Hyungseob Shin
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yejee Shin
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Jeong Ryong Lee
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Soo Mee Lim
- Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea
| | - Dosik Hwang
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
- School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
- Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul, Korea.
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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Koh ES, Gan HK, Senko C, Francis RJ, Ebert M, Lee ST, Lau E, Khasraw M, Nowak AK, Bailey DL, Moffat BA, Fitt G, Hicks RJ, Coffey R, Verhaak R, Walsh KM, Barnes EH, De Abreu Lourenco R, Rosenthal M, Adda L, Foroudi F, Lasocki A, Moore A, Thomas PA, Roach P, Back M, Leonard R, Scott AM. [ 18F]-fluoroethyl-L-tyrosine (FET) in glioblastoma (FIG) TROG 18.06 study: protocol for a prospective, multicentre PET/CT trial. BMJ Open 2023; 13:e071327. [PMID: 37541751 PMCID: PMC10407346 DOI: 10.1136/bmjopen-2022-071327] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/08/2023] [Indexed: 08/06/2023] Open
Abstract
INTRODUCTION Glioblastoma is the most common aggressive primary central nervous system cancer in adults characterised by uniformly poor survival. Despite maximal safe resection and postoperative radiotherapy with concurrent and adjuvant temozolomide-based chemotherapy, tumours inevitably recur. Imaging with O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) has the potential to impact adjuvant radiotherapy (RT) planning, distinguish between treatment-induced pseudoprogression versus tumour progression as well as prognostication. METHODS AND ANALYSIS The FET-PET in Glioblastoma (FIG) study is a prospective, multicentre, non-randomised, phase II study across 10 Australian sites and will enrol up to 210 adults aged ≥18 years with newly diagnosed glioblastoma. FET-PET will be performed at up to three time points: (1) following initial surgery and prior to commencement of chemoradiation (FET-PET1); (2) 4 weeks following concurrent chemoradiation (FET-PET2); and (3) within 14 days of suspected clinical and/or radiological progression on MRI (performed at the time of clinical suspicion of tumour recurrence) (FET-PET3). The co-primary outcomes are: (1) to investigate how FET-PET versus standard MRI impacts RT volume delineation and (2) to determine the accuracy and management impact of FET-PET in distinguishing pseudoprogression from true tumour progression. The secondary outcomes are: (1) to investigate the relationships between FET-PET parameters (including dynamic uptake, tumour to background ratio, metabolic tumour volume) and progression-free survival and overall survival; (2) to assess the change in blood and tissue biomarkers determined by serum assay when comparing FET-PET data acquired prior to chemoradiation with other prognostic markers, looking at the relationships of FET-PET versus MRI-determined site/s of progressive disease post chemotherapy treatment with MRI and FET-PET imaging; and (3) to estimate the health economic impact of incorporating FET-PET into glioblastoma management and in the assessment of post-treatment pseudoprogression or recurrence/true progression. Exploratory outcomes include the correlation of multimodal imaging, blood and tumour biomarker analyses with patterns of failure and survival. ETHICS AND DISSEMINATION The study protocol V.2.0 dated 20 November 2020 has been approved by a lead Human Research Ethics Committee (Austin Health, Victoria). Other clinical sites will provide oversight through local governance processes, including obtaining informed consent from suitable participants. The study will be conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. Results of the FIG study (TROG 18.06) will be disseminated via relevant scientific and consumer forums and peer-reviewed publications. TRIAL REGISTRATION NUMBER ANZCTR ACTRN12619001735145.
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Affiliation(s)
- Eng-Siew Koh
- Radiation Oncology, Liverpool Hospital, Liverpool, New South Wales, Australia
- South West Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Hui K Gan
- Austin Health, Department of Medical Oncology, Melbourne, Victoria, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia
- Tumour Targeting Program, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Clare Senko
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia
- Tumour Targeting Program, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - Roslyn J Francis
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- Medical School, The University of Western Australia, Crawley, Western Australia, Australia
| | - Martin Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
| | - Sze Ting Lee
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia
- Tumour Targeting Program, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia
| | - Eddie Lau
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia
- Department of Radiology, University of Melbourne, Melbourne, Victoria, Australia
- Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
| | - Mustafa Khasraw
- Department of Neurosurgery and Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, North Carolina, USA
| | - Anna K Nowak
- Medical School, The University of Western Australia, Crawley, Western Australia, Australia
- Department of Medical Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
| | - Dale L Bailey
- Faculty of Medicine & Health, University of Sydney, Camperdown, New South Wales, Australia
- Department of Nuclear Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Bradford A Moffat
- Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Melbourne, Victoria, Australia
| | - Greg Fitt
- Department of Radiology, University of Melbourne, Melbourne, Victoria, Australia
- Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
| | - Rodney J Hicks
- Department of Radiology, University of Melbourne, Melbourne, Victoria, Australia
- Centre for Cancer Imaging, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
| | - Robert Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Roel Verhaak
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurosurgery, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Kyle M Walsh
- Department of Neurosurgery and Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, North Carolina, USA
| | - Elizabeth H Barnes
- NHMRC Clinical Trials Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Richard De Abreu Lourenco
- Centre for Health Economics Research and Evaluation, University of Technology Sydney, Broadway, New South Wales, Australia
| | - Mark Rosenthal
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Lucas Adda
- The Cooperative Trials Group for Neuro-Oncology (COGNO) Consumer Advisor Panel, National Health and Medical Research Council (NHMRC) Clinical Trials Centre (CTC), University of Sydney, Sydney, New South Wales, Australia
| | - Farshad Foroudi
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia
- Department of Radiation Oncology, Austin Health, Melbourne, Victoria, Australia
| | - Arian Lasocki
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Alisha Moore
- Trans Tasman Radiation Oncology Group (TROG), Newcastle, New South Wales, Australia
| | - Paul A Thomas
- Department of Nuclear Medicine, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Paul Roach
- Department of Nuclear Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia
- The University of Sydney, Camperdown, New South Wales, Australia
| | - Michael Back
- Department of Radiation Oncology, Royal North Shore Hospital, St Leonards, New South Wales, Australia
- Faculty of Medicine & Health, University of Sydney, Sydney, New South Wales, Australia
| | - Robyn Leonard
- NHMRC Clinical Trials Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Andrew M Scott
- School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia
- Tumour Targeting Program, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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Fernández-Rodicio S, Ferro-Costas G, Sampedro-Viana A, Bazarra-Barreiros M, Ferreirós A, López-Arias E, Pérez-Mato M, Ouro A, Pumar JM, Mosqueira AJ, Alonso-Alonso ML, Castillo J, Hervella P, Iglesias-Rey R. Perfusion-weighted software written in Python for DSC-MRI analysis. Front Neuroinform 2023; 17:1202156. [PMID: 37593674 PMCID: PMC10431979 DOI: 10.3389/fninf.2023.1202156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 06/27/2023] [Indexed: 08/19/2023] Open
Abstract
Introduction Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes. Methods The DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood-brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature. Results A total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland-Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF. Conclusion An open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented.
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Affiliation(s)
- Sabela Fernández-Rodicio
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | | | - Ana Sampedro-Viana
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Marcos Bazarra-Barreiros
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | | | - Esteban López-Arias
- Translational Stroke Laboratory (TREAT), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - María Pérez-Mato
- Neurological Sciences and Cerebrovascular Research Laboratory, Department of Neurology and Stroke Center, La Paz University Hospital, Neuroscience Area of IdiPAZ Health Research Institute, Universidad Autónoma de Madrid, Madrid, Spain
| | - Alberto Ouro
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - José M. Pumar
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- Department of Neuroradiology, Hospital Clínico Universitario, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Antonio J. Mosqueira
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- Department of Neuroradiology, Hospital Clínico Universitario, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - María Luz Alonso-Alonso
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - José Castillo
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Pablo Hervella
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Ramón Iglesias-Rey
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
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Ferreri AJM, Calimeri T, Cwynarski K, Dietrich J, Grommes C, Hoang-Xuan K, Hu LS, Illerhaus G, Nayak L, Ponzoni M, Batchelor TT. Primary central nervous system lymphoma. Nat Rev Dis Primers 2023; 9:29. [PMID: 37322012 PMCID: PMC10637780 DOI: 10.1038/s41572-023-00439-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2023] [Indexed: 06/17/2023]
Abstract
Primary central nervous system lymphoma (PCNSL) is a diffuse large B cell lymphoma in which the brain, spinal cord, leptomeninges and/or eyes are exclusive sites of disease. Pathophysiology is incompletely understood, although a central role seems to comprise immunoglobulins binding to self-proteins expressed in the central nervous system (CNS) and alterations of genes involved in B cell receptor, Toll-like receptor and NF-κB signalling. Other factors such as T cells, macrophages or microglia, endothelial cells, chemokines, and interleukins, probably also have important roles. Clinical presentation varies depending on the involved regions of the CNS. Standard of care includes methotrexate-based polychemotherapy followed by age-tailored thiotepa-based conditioned autologous stem cell transplantation and, in patients unsuitable for such treatment, consolidation with whole-brain radiotherapy or single-drug maintenance. Personalized treatment, primary radiotherapy and only supportive care should be considered in unfit, frail patients. Despite available treatments, 15-25% of patients do not respond to chemotherapy and 25-50% relapse after initial response. Relapse rates are higher in older patients, although the prognosis of patients experiencing relapse is poor independent of age. Further research is needed to identify diagnostic biomarkers, treatments with higher efficacy and less neurotoxicity, strategies to improve the penetration of drugs into the CNS, and roles of other therapies such as immunotherapies and adoptive cell therapies.
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Affiliation(s)
| | - Teresa Calimeri
- Lymphoma Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Kate Cwynarski
- Department of Haematology, University College Hospital, London, UK
| | - Jorg Dietrich
- Cancer and Neurotoxicity Clinic and Brain Repair Research Program, Massachusetts General Hospital Cancer Center, Boston, MA, USA
| | - Christian Grommes
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Khê Hoang-Xuan
- APHP, Groupe Hospitalier Salpêtrière, Sorbonne Université, IHU, ICM, Service de Neurologie 2, Paris, France
| | - Leland S Hu
- Department of Radiology, Neuroradiology Division, Mayo Clinic, Phoenix, AZ, USA
| | - Gerald Illerhaus
- Clinic of Hematology, Oncology and Palliative Care, Klinikum Stuttgart, Stuttgart, Germany
| | - Lakshmi Nayak
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Maurilio Ponzoni
- Pathology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Ateneo Vita-Salute San Raffaele, Milan, Italy
| | - Tracy T Batchelor
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Hirschler L, Sollmann N, Schmitz‐Abecassis B, Pinto J, Arzanforoosh F, Barkhof F, Booth T, Calvo‐Imirizaldu M, Cassia G, Chmelik M, Clement P, Ercan E, Fernández‐Seara MA, Furtner J, Fuster‐Garcia E, Grech‐Sollars M, Guven NT, Hatay GH, Karami G, Keil VC, Kim M, Koekkoek JAF, Kukran S, Mancini L, Nechifor RE, Özcan A, Ozturk‐Isik E, Piskin S, Schmainda K, Svensson SF, Tseng C, Unnikrishnan S, Vos F, Warnert E, Zhao MY, Jancalek R, Nunes T, Emblem KE, Smits M, Petr J, Hangel G. Advanced MR Techniques for Preoperative Glioma Characterization: Part 1. J Magn Reson Imaging 2023; 57:1655-1675. [PMID: 36866773 PMCID: PMC10946498 DOI: 10.1002/jmri.28662] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
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: 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Lydiane Hirschler
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional RadiologyUniversity Hospital UlmUlmGermany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Bárbara Schmitz‐Abecassis
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Medical Delta FoundationDelftThe Netherlands
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Thomas Booth
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of NeuroradiologyKing's College Hospital NHS Foundation TrustLondonUK
| | | | | | - Marek Chmelik
- Department of Technical Disciplines in Medicine, Faculty of Health CareUniversity of PrešovPrešovSlovakia
| | - Patricia Clement
- Department of Diagnostic SciencesGhent UniversityGhentBelgium
- Department of Medical ImagingGhent University HospitalGhentBelgium
| | - Ece Ercan
- Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Julia Furtner
- Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Research Center of Medical Image Analysis and Artificial IntelligenceDanube Private UniversityKrems an der DonauAustria
| | - Elies Fuster‐Garcia
- Biomedical Data Science Laboratory, Instituto Universitario de Tecnologías de la Información y ComunicacionesUniversitat Politècnica de ValènciaValenciaSpain
| | - Matthew Grech‐Sollars
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Nazmiye Tugay Guven
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Gokce Hale Hatay
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Golestan Karami
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Vera C. Keil
- Department of Radiology & Nuclear MedicineAmsterdam UMC, Vrije UniversiteitAmsterdamThe Netherlands
- Cancer Center AmsterdamAmsterdamThe Netherlands
| | - Mina Kim
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
| | - Johan A. F. Koekkoek
- Department of NeurologyLeiden University Medical CenterLeidenThe Netherlands
- Department of NeurologyHaaglanden Medical CenterThe HagueThe Netherlands
| | - Simran Kukran
- Department of BioengineeringImperial College LondonLondonUK
- Department of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, Institute of NeurologyUniversity College LondonLondonUK
| | - Ruben Emanuel Nechifor
- Department of Clinical Psychology and PsychotherapyInternational Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes‐Bolyai UniversityCluj‐NapocaRomania
| | - Alpay Özcan
- Electrical and Electronics Engineering DepartmentBogazici University IstanbulIstanbulTurkey
| | - Esin Ozturk‐Isik
- Institute of Biomedical EngineeringBogazici University IstanbulIstanbulTurkey
| | - Senol Piskin
- Department of Mechanical Engineering, Faculty of Natural Sciences and EngineeringIstinye University IstanbulIstanbulTurkey
| | - Kathleen Schmainda
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Siri F. Svensson
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
- Department of PhysicsUniversity of OsloOsloNorway
| | - Chih‐Hsien Tseng
- Medical Delta FoundationDelftThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Saritha Unnikrishnan
- Faculty of Engineering and DesignAtlantic Technological University (ATU) SligoSligoIreland
- Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), ATU SligoSligoIreland
| | - Frans Vos
- Medical Delta FoundationDelftThe Netherlands
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Esther Warnert
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
- Stanford Cardiovascular InstituteStanford UniversityStanfordCaliforniaUSA
| | - Radim Jancalek
- Department of NeurosurgerySt. Anne's University Hospital, BrnoBrnoCzech Republic
- Faculty of Medicine, Masaryk UniversityBrnoCzech Republic
| | - Teresa Nunes
- Department of NeuroradiologyHospital Garcia de OrtaAlmadaPortugal
| | - Kyrre E. Emblem
- Department of Physics and Computational RadiologyOslo University HospitalOsloNorway
| | - Marion Smits
- Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
- Department of Radiology & Nuclear MedicineErasmus MCRotterdamThe Netherlands
- Brain Tumour CentreErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Gilbert Hangel
- Department of NeurosurgeryMedical University of ViennaViennaAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
- Christian Doppler Laboratory for MR Imaging BiomarkersViennaAustria
- Medical Imaging ClusterMedical University of ViennaViennaAustria
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Siakallis L, Topriceanu CC, Panovska-Griffiths J, Bisdas S. The role of DSC MR perfusion in predicting IDH mutation and 1p19q codeletion status in gliomas: meta-analysis and technical considerations. Neuroradiology 2023:10.1007/s00234-023-03154-5. [PMID: 37173578 DOI: 10.1007/s00234-023-03154-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
PURPOSE Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status are important for managing glioma patients. However, current practice dictates invasive tissue sampling for histomolecular classification. We investigated the current value of dynamic susceptibility contrast (DSC) MR perfusion imaging as a tool for the non-invasive identification of these biomarkers. METHODS A systematic search of PubMed, Medline, and Embase up to 2023 was performed, and meta-analyses were conducted. We removed studies employing machine learning models or using multiparametric imaging. We used random-effects standardized mean difference (SMD) and bivariate sensitivity-specificity meta-analyses, calculated the area under the hierarchical summary receiver operating characteristic curve (AUC) and performed meta-regressions using technical acquisition parameters (e.g., time to echo [TE], repetition time [TR]) as moderators to explore sources of heterogeneity. For all estimates, 95% confidence intervals (CIs) are provided. RESULTS Sixteen eligible manuscripts comprising 1819 patients were included in the quantitative analyses. IDH mutant (IDHm) gliomas had lower rCBV values compared to their wild-type (IDHwt) counterparts. The highest SMD was observed for rCBVmean, rCBVmax, and rCBV 75th percentile (SMD≈ - 0.8, 95% CI ≈ [- 1.2, - 0.5]). In meta-regression, shorter TEs, shorter TRs, and smaller slice thicknesses were linked to higher absolute SMDs. When discriminating IDHm from IDHwt, the highest pooled specificity was observed for rCBVmean (82% [72, 89]), and the highest pooled sensitivity (i.e., 92% [86, 93]) and AUC (i.e., 0.91) for rCBV 10th percentile. In the bivariate meta-regression, shorter TEs and smaller slice gaps were linked to higher pooled sensitivities. In IDHm, 1p19q codeletion was associated with higher rCBVmean (SMD = 0.9 [0.2, 1.5]) and rCBV 90th percentile (SMD = 0.9 [0.1, 1.7]) values. CONCLUSIONS Identification of vascular signatures predictive of IDH and 1p19q status is a novel promising application of DSC perfusion. Standardization of acquisition protocols and post-processing of DSC perfusion maps are warranted before widespread use in clinical practice.
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Affiliation(s)
- Loizos Siakallis
- University College London (UCL) Queen Square Institute of Neurology, London, UK.
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals (UCLH) NHS Foundation Trust, London, UK.
| | - Constantin-Cristian Topriceanu
- University College London (UCL) Queen Square Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals (UCLH) NHS Foundation Trust, London, UK
- UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen's College, University of Oxford, Oxford, UK
| | - Sotirios Bisdas
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals (UCLH) NHS Foundation Trust, London, UK
- Department of Brain Repair & Rehabilitation, Queen Square Institute of Neurology, University College London, London, UK
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Elschot EP, Backes WH, de Jong JJA, Drenthen GS, Wong SM, Staals J, Postma AA, Rouhl RPW, van Oostenbrugge RJ, Jansen JFA. Assessment of the clinical feasibility of detecting subtle blood-brain barrier leakage in cerebral small vessel disease using dynamic susceptibility contrast MRI. Magn Reson Imaging 2023; 102:55-61. [PMID: 37137345 DOI: 10.1016/j.mri.2023.04.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023]
Abstract
PURPOSE Cerebral small vessel disease (cSVD) involves several pathologies affecting the small vessels, including blood-brain barrier (BBB) impairment. Dynamic susceptibility contrast (DSC) MRI is sensitive to both blood perfusion and BBB leakage, and correction methods may be crucial for obtaining reliable perfusion measures. These methods might also be applicable to detect BBB leakage itself. This study investigated to what extent DSC-MRI can measure subtle BBB leakage in a clinical feasibility setting. METHODS In vivo DCE and DSC data were collected from fifteen cSVD patients (71 (±10) years, 6F/9M) and twelve elderly controls (71 (±10) years, 4F/8M). DSC-derived leakage fractions were obtained using the Boxerman-Schmainda-Weisskoff method (K2). K2 was compared with the DCE-derived leakage rate Ki, obtained from Patlak analysis. Subsequently, differences were assessed between white matter hyperintensities (WMH), cortical gray matter (CGM), and normal-appearing white matter (NAWM). Additionally, computer simulations were performed to assess the sensitivity of DSC-MRI to BBB leakage. RESULTS K2 showed significant differences between tissue regions (P < 0.001 for CGM-NAWM and CGM-WMH, and P = 0.001 for NAWM-WMH). Conversely, according to the computer simulations the DSC sensitivity was insufficient to measure subtle BBB leakage, as the K2 values were below the derived limit of quantification (4∙10-3 min-1). As expected, Ki was elevated in the WMH compared to CGM and NAWM (P < 0.001). CONCLUSIONS Although clinical DSC-MRI seems capable to detect subtle BBB leakage differences between WMH and normal-appearing brain tissue it is not recommended. K2 as a direct measure for subtle BBB leakage remains ambiguous as its signal effects are due to mixed T1- and T2∗-weighting. Further research is warranted to better disentangle perfusion from leakage effects.
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Affiliation(s)
- Elles P Elschot
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Walter H Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands; School for Cardiovascular Diseases (CARIM), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Joost J A de Jong
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Gerhard S Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Sau May Wong
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands
| | - Julie Staals
- Department of Neurology, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Cardiovascular Diseases (CARIM), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Alida A Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Rob P W Rouhl
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands; Department of Neurology, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+, Heeze and Maastricht, the Netherlands
| | - Robert J van Oostenbrugge
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands; Department of Neurology, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Cardiovascular Diseases (CARIM), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands
| | - Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Minderbroedersberg 4-6, Maastricht, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands; Academic Center for Epileptology Kempenhaeghe/MUMC+, Heeze and Maastricht, the Netherlands.
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Venugopal K, Arzanforoosh F, van Dorth D, Smits M, van Osch MJP, Hernandez-Tamames JA, Warnert EAH, Poot DHJ. MR Vascular Fingerprinting with Hybrid Gradient–Spin Echo Dynamic Susceptibility Contrast MRI for Characterization of Microvasculature in Gliomas. Cancers (Basel) 2023; 15:cancers15072180. [PMID: 37046841 PMCID: PMC10093700 DOI: 10.3390/cancers15072180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Characterization of tumor microvasculature is important in tumor assessment and studying treatment response. This is possible by acquiring vascular biomarkers with magnetic resonance imaging (MRI) based on dynamic susceptibility contrast (DSC). We propose magnetic resonance vascular fingerprinting (MRVF) for hybrid echo planar imaging (HEPI) acquired during the first passage of the contrast agent (CA). The proposed approach was evaluated in patients with gliomas, and we simultaneously estimated vessel radius and relative cerebral blood volume. These parameters were also compared to the respective values estimated using the previously introduced vessel size imaging (VSI) technique. The results of both methods were found to be consistent. MRVF was also found to be robust to noise in the estimation of the parameters. DSC-HEPI-based MRVF provides characterization of microvasculature in gliomas with a short acquisition time and can be further improved in several ways to increase our understanding of tumor physiology.
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Affiliation(s)
- Krishnapriya Venugopal
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Fatemeh Arzanforoosh
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Daniëlle van Dorth
- C. J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Matthias J. P. van Osch
- C. J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Juan A. Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3015 GD Rotterdam, The Netherlands
- Department of Medical Imaging, Faculty of Applied Physics, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Esther A. H. Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Dirk H. J. Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3015 GD Rotterdam, The Netherlands
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Cho NS, Hagiwara A, Sanvito F, Ellingson BM. A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors. Neuroradiology 2023; 65:559-568. [PMID: 36301349 PMCID: PMC9905164 DOI: 10.1007/s00234-022-03072-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/14/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods. METHODS Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated. RESULTS Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar. CONCLUSION The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.
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Affiliation(s)
- Nicholas S Cho
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Francesco Sanvito
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Benjamin M Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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Powell SJ, Withey SB, Sun Y, Grist JT, Novak J, MacPherson L, Abernethy L, Pizer B, Grundy R, Morgan PS, Jaspan T, Bailey S, Mitra D, Auer DP, Avula S, Arvanitis TN, Peet A. Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data. Br J Radiol 2023; 96:20201465. [PMID: 36802769 PMCID: PMC10161906 DOI: 10.1259/bjr.20201465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVE Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. METHODS 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. RESULTS Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. CONCLUSION The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. ADVANCES IN KNOWLEDGE A new automated quality control method was developed, which trained machine learning classifiers using QR results.
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Affiliation(s)
- Stephen J Powell
- Physical Sciences for Health CDT, University of Birmingham, Birmingham, United Kingdom.,Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Stephanie B Withey
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom.,RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Yu Sun
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - James T Grist
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jan Novak
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom.,Department of Psychology, Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Lesley MacPherson
- Radiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Laurence Abernethy
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Barry Pizer
- Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Richard Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom
| | - Paul S Morgan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom.,Medical Physics, Nottingham University Hospitals, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Nottingham, United Kingdom
| | - Tim Jaspan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom.,Radiology, Nottingham University Hospitals, Nottingham, United Kingdom
| | - Simon Bailey
- Sir James Spence Institute of Child Health, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Dipayan Mitra
- Neuroradiology, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Dorothee P Auer
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Shivaram Avula
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, United Kingdom
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom.,Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom
| | - Andrew Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.,Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom
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Jordan JT, Gerstner ER. Imaging of Brain Tumors. Continuum (Minneap Minn) 2023; 29:171-193. [PMID: 36795877 DOI: 10.1212/con.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
OBJECTIVE This article focuses on neuroimaging as an essential tool for diagnosing brain tumors and monitoring response to treatment. LATEST DEVELOPMENTS Neuroimaging is useful at all stages of brain tumor care. Technologic advances have improved the clinical diagnostic capability of neuroimaging as a vital complement to history, examination, and pathologic assessment. Presurgical evaluations are enriched by novel imaging techniques, through improved differential diagnosis and better surgical planning using functional MRI (fMRI) and diffusion tensor imaging. The common clinical challenge of differentiating tumor progression from treatment-related inflammatory change is aided by novel uses of perfusion imaging, susceptibility-weighted imaging (SWI), spectroscopy, and new positron emission tomography (PET) tracers. ESSENTIAL POINTS Using the most up-to-date imaging techniques will facilitate high-quality clinical practice in the care of patients with brain tumors.
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Bressler I, Ben Bashat D, Buchsweiler Y, Aizenstein O, Limon D, Bokestein F, Blumenthal TD, Nevo U, Artzi M. Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma. MAGMA (NEW YORK, N.Y.) 2023; 36:33-42. [PMID: 36287282 DOI: 10.1007/s10334-022-01045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome. RESULTS Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases. CONCLUSION The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.
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Affiliation(s)
- Idan Bressler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yuval Buchsweiler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Orna Aizenstein
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Division of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dror Limon
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Division of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Felix Bokestein
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - T Deborah Blumenthal
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Uri Nevo
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Moran Artzi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. .,Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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Steidl E, Filipski K, Hattingen E, Steinbach JP, Maurer GD. Longitudinal study on MRI and neuropathological findings: Neither DSC-perfusion derived rCBVmax nor vessel densities correlate between newly diagnosed and progressive glioblastoma. PLoS One 2023; 18:e0274400. [PMID: 36724187 PMCID: PMC9891512 DOI: 10.1371/journal.pone.0274400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/26/2022] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION When evaluating MRIs for glioblastoma progression, previous scans are usually included into the review. Nowadays dynamic susceptibility contrast (DSC)-perfusion is an essential component in MR-diagnostics of gliomas, since the extent of hyperperfusion upon first diagnosis correlates with gene expression and survival. We aimed to investigate if this initial perfusion signature also characterizes the glioblastoma at time of progression. If so, DSC-perfusion data from the initial diagnosis could be of diagnostic benefit in follow-up assessments. METHODS We retrospectively identified 65 patients with isocitrate dehydrogenase wildtype glioblastoma who had received technically identical DSC-perfusion measurements at initial diagnosis and at time of first progression. We determined maximum relative cerebral blood volume values (rCBVmax) by standardized re-evaluation of the data including leakage correction. In addition, the corresponding tissue samples from 24 patients were examined histologically for the maximum vessel density within the tumor. Differences (paired t-test/ Wilcoxon matched pairs test) and correlations (Spearman) between the measurements at both timepoints were calculated. RESULTS The rCBVmax was consistently lower at time of progression compared to rCBVmax at time of first diagnosis (p < .001). There was no correlation between the rCBVmax values at both timepoints (r = .12). These findings were reflected in the histological examination, with a lower vessel density in progressive glioblastoma (p = .01) and no correlation between the two timepoints (r = -.07). CONCLUSION Our results suggest that the extent of hyperperfusion in glioblastoma at first diagnosis is not a sustaining tumor characteristic. Hence, the rCBVmax at initial diagnosis should be disregarded when reviewing MRIs for glioblastoma progression.
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Affiliation(s)
- Eike Steidl
- Institute of Neuroradiology, Goethe University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- * E-mail:
| | - Katharina Filipski
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Neurology (Edinger Institute), Goethe University Hospital, Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Elke Hattingen
- Institute of Neuroradiology, Goethe University Hospital, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Joachim P. Steinbach
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
- Dr. Senckenberg Institute of Neurooncology, Goethe University Hospital, Frankfurt am Main, Germany
| | - Gabriele D. Maurer
- Dr. Senckenberg Institute of Neurooncology, Goethe University Hospital, Frankfurt am Main, Germany
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Li H, Wang C, Yu X, Luo Y, Wang H. Measurement of Cerebral Oxygen Extraction Fraction Using Quantitative BOLD Approach: A Review. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:101-118. [PMID: 36939794 PMCID: PMC9883382 DOI: 10.1007/s43657-022-00081-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/29/2022] [Accepted: 10/11/2022] [Indexed: 12/12/2022]
Abstract
Quantification of brain oxygenation and metabolism, both of which are indicators of the level of brain activity, plays a vital role in understanding the cerebral perfusion and the pathophysiology of brain disorders. Magnetic resonance imaging (MRI), a widely used clinical imaging technique, which is very sensitive to magnetic susceptibility, has the possibility of substituting positron emission tomography (PET) in measuring oxygen metabolism. This review mainly focuses on the quantitative blood oxygenation level-dependent (qBOLD) method for the evaluation of oxygen extraction fraction (OEF) in the brain. Here, we review the theoretic basis of qBOLD, as well as existing acquisition and quantification methods. Some published clinical studies are also presented, and the pros and cons of qBOLD method are discussed as well.
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Affiliation(s)
- Hongwei Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433 China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
| | - Xuchen Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433 China
| | - Yu Luo
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200434 China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 220 Handan Road, Yangpu District, Shanghai, 200433 China
- Human Phenome Institute, Fudan University, Shanghai, 201203 China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, 200433 China
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Anil A, Stokes AM, Chao R, Hu LS, Alhilali L, Karis JP, Bell LC, Quarles CC. Identification of single-dose, dual-echo based CBV threshold for fractional tumor burden mapping in recurrent glioblastoma. Front Oncol 2023; 13:1046629. [PMID: 36733305 PMCID: PMC9887158 DOI: 10.3389/fonc.2023.1046629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
Background Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is widely used to distinguish high grade glioma recurrence from post treatment radiation effects (PTRE). Application of rCBV thresholds yield maps to distinguish between regional tumor burden and PTRE, a biomarker termed the fractional tumor burden (FTB). FTB is generally measured using conventional double-dose, single-echo DSC-MRI protocols; recently, a single-dose, dual-echo DSC-MRI protocol was clinically validated by direct comparison to the conventional double-dose, single-echo protocol. As the single-dose, dual-echo acquisition enables reduction in the contrast agent dose and provides greater pulse sequence parameter flexibility, there is a compelling need to establish dual-echo DSC-MRI based FTB mapping. In this study, we determine the optimum standardized rCBV threshold for the single-dose, dual-echo protocol to generate FTB maps that best match those derived from the reference standard, double-dose, single-echo protocol. Methods The study consisted of 23 high grade glioma patients undergoing perfusion scans to confirm suspected tumor recurrence. We sequentially acquired single dose, dual-echo and double dose, single-echo DSC-MRI data. For both protocols, we generated leakage-corrected standardized rCBV maps. Standardized rCBV (sRCBV) thresholds of 1.0 and 1.75 were used to compute single-echo FTB maps as the reference for delineating PTRE (sRCBV < 1.0), tumor with moderate angiogenesis (1.0 < sRCBV < 1.75), and tumor with high angiogenesis (sRCBV > 1.75) regions. To assess the sRCBV agreement between acquisition protocols, the concordance correlation coefficient (CCC) was computed between the mean tumor sRCBV values across the patients. A receiver operating characteristics (ROC) analysis was performed to determine the optimum dual-echo sRCBV threshold. The sensitivity, specificity, and accuracy were compared between the obtained optimized threshold (1.64) and the standard reference threshold (1.75) for the dual-echo sRCBV threshold. Results The mean tumor sRCBV values across the patients showed a strong correlation (CCC = 0.96) between the two protocols. The ROC analysis showed maximum accuracy at thresholds of 1.0 (delineate PTRE from tumor) and 1.64 (differentiate aggressive tumors). The reference threshold (1.75) and the obtained optimized threshold (1.64) yielded similar accuracy, with slight differences in sensitivity and specificity which were not statistically significant (1.75 threshold: Sensitivity = 81.94%; Specificity: 87.23%; Accuracy: 84.58% and 1.64 threshold: Sensitivity = 84.48%; Specificity: 84.97%; Accuracy: 84.73%). Conclusions The optimal sRCBV threshold for single-dose, dual-echo protocol was found to be 1.0 and 1.64 for distinguishing tumor recurrence from PTRE; however, minimal differences were observed when using the standard threshold (1.75) as the upper threshold, suggesting that the standard threshold could be used for both protocols. While the prior study validated the agreement of the mean sRCBV values between the protocols, this study confirmed that their voxel-wise agreement is suitable for reliable FTB mapping. Dual-echo DSC-MRI acquisitions enable robust single-dose sRCBV and FTB mapping, provide pulse sequence parameter flexibility and should improve reproducibility by mitigating variations in preload dose and incubation time.
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Affiliation(s)
- Aliya Anil
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Renee Chao
- Division of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neuroimaging Institute, Phoenix, AZ, United States
| | - Leland S. Hu
- Department of Radiology, Division of Neuroradiology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Lea Alhilali
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, United States
| | - John P. Karis
- Neuroradiology, Southwest Neuroimaging at Barrow Neurological Institute, Phoenix, AZ, United States
| | - Laura C. Bell
- Early Clinical Development, Genentech, San Francisco, CA, United States
| | - C. Chad Quarles
- Cancer System Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States,*Correspondence: C. Chad Quarles,
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