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Davies-Jenkins CW, Zöllner HJ, Simicic D, Alcicek S, Edden RAE, Oeltzschner G. Data-driven determination of 1H-MRS basis set composition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.11.612503. [PMID: 39314430 PMCID: PMC11419043 DOI: 10.1101/2024.09.11.612503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Purpose Metabolite amplitude estimates derived from linear combination modeling of MR spectra depend upon the precise list of constituent metabolite basis functions used (the "basis set"). The absence of clear consensus on the "ideal" composition or objective criteria to determine the suitability of a particular basis set contributes to the poor reproducibility of MRS. In this proof-of-concept study, we demonstrate a novel, data-driven approach for deciding the basis-set composition using Bayesian information criteria (BIC). Methods We have developed an algorithm that iteratively adds metabolites to the basis set using iterative modeling, informed by BIC scores. We investigated two quantitative "stopping conditions", referred to as max-BIC and zero-amplitude, and whether to optimize the selection of basis set on a per-spectrum basis or at the group level. The algorithm was tested using two groups of synthetic in-vivo-like spectra representing healthy brain and tumor spectra, respectively, and the derived basis sets (and metabolite amplitude estimates) were compared to the ground truth. Results All derived basis sets correctly identified high-concentration metabolites and provided reasonable fits of the spectra. At the single-spectrum level, the two stopping conditions derived the underlying basis set with 77-87% accuracy. When optimizing across a group, basis set determination accuracy improved to 84-92%. Conclusion Data-driven determination of the basis set composition is feasible. With refinement, this approach could provide a valuable data-driven way to derive or refine basis sets, reducing the operator bias of MRS analyses, enhancing the objectivity of quantitative analyses, and increasing the clinical viability of MRS.
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Affiliation(s)
- Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Seyma Alcicek
- Institute of Neuroradiology, University Hospital Frankfurt, Goethe University, Frankfurt/Main, Germany
- University Cancer Center Frankfurt (UCT), Frankfurt/Main, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt/Main, Germany
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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Zhou M, Nie Z, Zhao J, Xiao Y, Hong X, Wang Y, Dong C, Lin AP, Lei Z. Optimization and validation of echo times of point-resolved spectroscopy for cystathionine detection in gliomas. Cancer Imaging 2024; 24:118. [PMID: 39223589 PMCID: PMC11367870 DOI: 10.1186/s40644-024-00764-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: 04/07/2024] [Accepted: 08/22/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Cystathionine accumulates selectively in 1p/19q-codeleted gliomas, and can serve as a possible noninvasive biomarker. This study aims to optimize the echo time (TE) of point-resolved spectroscopy (PRESS) for cystathionine detection in gliomas, and evaluate the diagnostic accuracy of PRESS for 1p/19q-codeletion identification. METHODS The TE of PRESS was optimized with numerical and phantom analysis to better resolve cystathionine from the overlapping aspartate multiplets. The optimized and 97 ms TE PRESS were then applied to 84 prospectively enrolled patients suspected of glioma or glioma recurrence to examine the influence of aspartate on cystathionine quantification by fitting the spectra with and without aspartate. The diagnostic performance of PRESS for 1p/19q-codeleted gliomas were assessed. RESULTS The TE of PRESS was optimized as (TE1, TE2) = (17 ms, 28 ms). The spectral pattern of cystathionine and aspartate were consistent between calculation and phantom. The mean concentrations of cystathionine in vivo fitting without aspartate were significantly higher than those fitting with full basis-set for 97 ms TE PRESS (1.97 ± 2.01 mM vs. 1.55 ± 1.95 mM, p < 0.01), but not significantly different for 45 ms method (0.801 ± 1.217 mM and 0.796 ± 1.217 mM, p = 0.494). The cystathionine concentrations of 45 ms approach was better correlated with those of edited MRS than 97 ms counterparts (r = 0.68 vs. 0.49, both p < 0.01). The sensitivity and specificity for discriminating 1p/19q-codeleted gliomas were 66.7% and 73.7% for 45 ms method, and 44.4% and 52.5% for 97 ms method, respectively. CONCLUSION The 45 ms TE PRESS yields more precise cystathionine estimates than the 97 ms method, and is anticipated to facilitate noninvasive diagnosis of 1p/19q-codeleted gliomas, and treatment response monitoring in those patients. Medium diagnostic performance of PRESS for 1p/19q-codeleted gliomas were observed, and warrants further investigations.
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Affiliation(s)
- Min Zhou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhuang Nie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jie Zhao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yao Xiao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Xiaohua Hong
- Tumor Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuhui Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chengjun Dong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Alexander P Lin
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ziqiao Lei
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Aamir M, Namoun A, Munir S, Aljohani N, Alanazi MH, Alsahafi Y, Alotibi F. Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network. Diagnostics (Basel) 2024; 14:1714. [PMID: 39202202 PMCID: PMC11353951 DOI: 10.3390/diagnostics14161714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Revised: 08/01/2024] [Accepted: 08/02/2024] [Indexed: 09/03/2024] Open
Abstract
Brain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years. This research introduces a hyperparametric convolutional neural network (CNN) model to identify brain tumors, with significant practical implications. By fine-tuning the hyperparameters of the CNN model, we optimize feature extraction and systematically reduce model complexity, thereby enhancing the accuracy of brain tumor diagnosis. The critical hyperparameters include batch size, layer counts, learning rate, activation functions, pooling strategies, padding, and filter size. The hyperparameter-tuned CNN model was trained on three different brain MRI datasets available at Kaggle, producing outstanding performance scores, with an average value of 97% for accuracy, precision, recall, and F1-score. Our optimized model is effective, as demonstrated by our methodical comparisons with state-of-the-art approaches. Our hyperparameter modifications enhanced the model performance and strengthened its capacity for generalization, giving medical practitioners a more accurate and effective tool for making crucial judgments regarding brain tumor diagnosis. Our model is a significant step in the right direction toward trustworthy and accurate medical diagnosis, with practical implications for improving patient outcomes.
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Affiliation(s)
- Muhammad Aamir
- Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan; (M.A.); (S.M.)
- Department of Computer Science, Superior University Lahore, Lahore 54000, Pakistan
| | - Abdallah Namoun
- AI Centre, Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia;
| | - Sehrish Munir
- Department of Computer Science, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan; (M.A.); (S.M.)
| | - Nasser Aljohani
- AI Centre, Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia;
| | - Meshari Huwaytim Alanazi
- Computer Science Department, College of Sciences, Northern Border University, Arar 73213, Saudi Arabia
| | - Yaser Alsahafi
- School of Information Technology, University of Jeddah, Jeddah 23218, Saudi Arabia;
| | - Faris Alotibi
- College of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi Arabia;
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Hájek M, Flögel U, S Tavares AA, Nichelli L, Kennerley A, Kahn T, Futterer JJ, Firsiori A, Grüll H, Saha N, Couñago F, Aydogan DB, Caligiuri ME, Faber C, Bell LC, Figueiredo P, Vilanova JC, Santini F, Mekle R, Waiczies S. MR beyond diagnostics at the ESMRMB annual meeting: MR theranostics and intervention. MAGMA (NEW YORK, N.Y.) 2024; 37:323-328. [PMID: 38865057 PMCID: PMC11316697 DOI: 10.1007/s10334-024-01176-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 06/13/2024]
Affiliation(s)
- Milan Hájek
- Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Ulrich Flögel
- Experimental Cardiovascular Imaging, Institute for Molecular Cardiology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Adriana A S Tavares
- Centre for Cardiovascular Sciences and Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Lucia Nichelli
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute, ICM, Paris, France
- Department of Neuroradiology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Aneurin Kennerley
- Department of Sports and Exercise Science, Institute of Sport, Manchester Metropolitan University, Manchester, UK
- Department of Biology, University of York, York, UK
| | - Thomas Kahn
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Jurgen J Futterer
- Minimally Invasive Image-Guided Intervention Center (MAGIC), Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
| | - Aikaterini Firsiori
- Unit of Diagnostic and Interventional Neuroradiology, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - Holger Grüll
- Institute of Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany
| | - Nandita Saha
- Max-Delbrück-Centrum Für Molekulare Medizin (MDC), Berlin Ultrahigh Field Facility, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Felipe Couñago
- Department of Radiation Oncology, Hospital Universitario San Francisco de Asís, Hospital Universitario Vithas La Milagrosa, GenesisCare, 28010, Madrid, Spain
| | - Dogu Baran Aydogan
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Università Degli Studi "Magna Graecia", Catanzaro, Italy
| | - Cornelius Faber
- Translational Research Imaging Center (TRIC), Clinic of Radiology, University of Münster, Münster, Germany
| | - Laura C Bell
- Early Clinical Development, Genentech Inc., South San Francisco, USA
| | - Patrícia Figueiredo
- Institute for Systems and Robotics, ISR-Lisboa, Lisbon, Portugal
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Joan C Vilanova
- Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging (IDI) Girona, University of Girona, 17004, Girona, Spain
| | - Francesco Santini
- Department of Radiology, University Hospital of Basel, Basel, Switzerland
- Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Ralf Mekle
- Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sonia Waiczies
- Max-Delbrück-Centrum Für Molekulare Medizin (MDC), Berlin Ultrahigh Field Facility, Berlin, Germany.
- Experimental and Clinical Research Center (ECRC), A Joint Cooperation Between the Charité Medical Faculty and the MDC, Berlin, Germany.
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Liu C, Wang J, Shen J, Chen X, Ji N, Yue S. Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy. PNAS NEXUS 2024; 3:pgae208. [PMID: 38860145 PMCID: PMC11164103 DOI: 10.1093/pnasnexus/pgae208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/17/2024] [Indexed: 06/12/2024]
Abstract
Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.
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Affiliation(s)
- Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Jiejun Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Xueyuan Road 37, Beijing 100191, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, South Fourth Ring West Road 119, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Xueyuan Road 37, Beijing 100191, China
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Özütemiz C, White M, Elvendahl W, Eryaman Y, Marjańska M, Metzger GJ, Patriat R, Kulesa J, Harel N, Watanabe Y, Grant A, Genovese G, Cayci Z. Use of a Commercial 7-T MRI Scanner for Clinical Brain Imaging: Indications, Protocols, Challenges, and Solutions-A Single-Center Experience. AJR Am J Roentgenol 2023; 221:788-804. [PMID: 37377363 PMCID: PMC10825876 DOI: 10.2214/ajr.23.29342] [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] [Indexed: 06/29/2023]
Abstract
The first commercially available 7-T MRI scanner (Magnetom Terra) was approved by the FDA in 2017 for clinical imaging of the brain and knee. After initial protocol development and sequence optimization efforts in volunteers, the 7-T system, in combination with an FDA-approved 1-channel transmit/32-channel receive array head coil, can now be routinely used for clinical brain MRI examinations. The ultrahigh field strength of 7-T MRI has the advantages of improved spatial resolution, increased SNR, and increased CNR but also introduces an array of new technical challenges. The purpose of this article is to describe an institutional experience with the use of the commercially available 7-T MRI scanner for routine clinical brain imaging. Specific clinical indications for which 7-T MRI may be useful for brain imaging include brain tumor evaluation with possible perfusion imaging and/or spectroscopy, radiotherapy planning; evaluation of multiple sclerosis and other demyelinating diseases, evaluation of Parkinson disease and guidance of deep brain stimulator placement, high-detail intracranial MRA and vessel wall imaging, evaluation of pituitary pathology, and evaluation of epilepsy. Detailed protocols, including sequence parameters, for these various indications are presented, and implementation challenges (including artifacts, safety, and side effects) and potential solutions are explored.
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Affiliation(s)
- Can Özütemiz
- Department of Radiology, University of Minnesota, 420 Delaware St SE, MMC 292, Minneapolis, MN 55455
| | - Matthew White
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
- Center for Clinical Imaging Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Wendy Elvendahl
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
- Center for Clinical Imaging Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Yigitcan Eryaman
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Gregory J Metzger
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Rémi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Jeramy Kulesa
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Yoichi Watanabe
- Department of Radiation Oncology, University of Minnesota, Minneapolis, MN
| | - Andrea Grant
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Zuzan Cayci
- Department of Radiology, University of Minnesota, 420 Delaware St SE, MMC 292, Minneapolis, MN 55455
- Center for Clinical Imaging Research, Department of Radiology, University of Minnesota, Minneapolis, MN
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7
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Lin AP. Cystathionine: A Spectroscopic Biomarker for 1p/19q Codeleted Gliomas. Radiology 2023; 308:e232100. [PMID: 37668521 DOI: 10.1148/radiol.232100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Affiliation(s)
- Alexander P Lin
- From the Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Ave, BLI236C, Boston, MA 02115
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Branzoli F, Liserre R, Deelchand DK, Poliani PL, Bielle F, Nichelli L, Sanson M, Lehéricy S, Marjańska M. Neurochemical Differences between 1p/19q Codeleted and Noncodeleted IDH-mutant Gliomas by in Vivo MR Spectroscopy. Radiology 2023; 308:e223255. [PMID: 37668523 PMCID: PMC10546286 DOI: 10.1148/radiol.223255] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 06/19/2023] [Accepted: 06/29/2023] [Indexed: 09/06/2023]
Abstract
Background Noninvasive identification of glioma subtypes is important for optimizing treatment strategies. Purpose To compare the in vivo neurochemical profiles between isocitrate dehydrogenase (IDH) 1-mutant 1p/19q codeleted gliomas and their noncodeleted counterparts measured by MR spectroscopy at 3.0 T with a point-resolved spectroscopy (PRESS) sequence optimized for D-2-hydroxyglutarate (2HG) detection. Materials and Methods Adults with IDH1-mutant gliomas were retrospectively included for this study from two university hospitals (inclusion period: January 2015 to July 2016 and September 2019 to June 2021, respectively) based on availability of 1p/19q codeletion status and a PRESS acquisition optimized for 2HG detection (echo time, 97 msec) at 3.0 T before any treatment. Spectral analysis was performed using LCModel and a simulated basis set. Metabolite quantification was performed using the water signal as a reference and correcting for water and metabolite longitudinal and transverse relaxation time constants. Concentration ratios were computed using total creatine (tCr) and total choline. A two-tailed unpaired t test was used to compare metabolite concentrations obtained in codeleted versus noncodeleted gliomas, accounting for multiple comparisons. Results Thirty-one adults (mean age, 39 years ± 8 [SD]; 19 male) were included, and 19 metabolites were quantified. Cystathionine concentration was higher in codeleted (n = 13) than noncodeleted (n = 18) gliomas when quantification was performed using the water signal or tCr as references (2.33 mM ± 0.98 vs 0.93 mM ± 0.94, and 0.34 mM ± 0.14 vs 0.14 mM ± 0.14, respectively; both P < .001). The sensitivity and specificity of PRESS to detect codeletion by means of cystathionine quantification were 92% and 61%, respectively. Other metabolites did not show evidence of a difference between groups (P > .05). Conclusion Higher cystathionine levels were detected in IDH1-mutant 1p/19q codeleted gliomas than in their noncodeleted counterparts with use of a PRESS sequence optimized for 2HG detection. Of 19 metabolites quantified, only cystathionine showed evidence of a difference in concentration between groups. Clinical trial registry no. NCT01703962 © RSNA, 2023 See also the editorial by Lin in this issue.
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Affiliation(s)
- Francesca Branzoli
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Roberto Liserre
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Dinesh K. Deelchand
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Pietro Luigi Poliani
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Franck Bielle
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Lucia Nichelli
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Marc Sanson
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Stéphane Lehéricy
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
| | - Małgorzata Marjańska
- From the Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris Brain Institute–L’Institut du Cerveau et de la Moelle Épinière (ICM), 47 boulevard de l’Hôpital, 75013 Paris, France (F. Branzoli, L.N., M.S., S.L.); Center for Neuroimaging Research (CENIR), L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (F. Branzoli, S.L.); Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy (R.L.); Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn (D.K.D., M.M.); Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy (P.L.P.); Laboratory R Escourolle (F. Bielle), Department of Neuroradiology (L.N., S.L.), and Department of Neurology 2 (M.S.), University Hospital La Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France; and Onconeurotek Tumor Bank, L’Institut du Cerveau et de la Moelle Épinière (ICM), Paris, France (M.S.)
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9
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Gudmundson AT, Koo A, Virovka A, Amirault AL, Soo M, Cho JH, Oeltzschner G, Edden RAE, Stark CEL. Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease. Anal Biochem 2023; 676:115227. [PMID: 37423487 PMCID: PMC10561665 DOI: 10.1016/j.ab.2023.115227] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023]
Abstract
Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.
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Affiliation(s)
- Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Annie Koo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Anna Virovka
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Alyssa L Amirault
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Madelene Soo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jocelyn H Cho
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA.
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10
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Li E, Qiao H, Sun J, Ma Q, Lin L, He Y, Li S, Mao X, Zhang X, Liao B. Cuproptosis-related gene expression is associated with immune infiltration and CD47/CD24 expression in glioblastoma, and a risk score based on these genes can predict the survival and prognosis of patients. Front Oncol 2023; 13:1011476. [PMID: 37546426 PMCID: PMC10399623 DOI: 10.3389/fonc.2023.1011476] [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: 08/04/2022] [Accepted: 06/27/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Glioblastoma (GBM) is the most invasive type of glioma, is insensitive to radiotherapy and chemotherapy, and has high proliferation and invasive ability, with a 5-year survival rate of <5%. Cuproptosis-related genes (CRGs) have been successfully used to predict the prognosis of many types of tumors. However, the relationship between cuproptosis and GBM remains unclear. Methods Here, we sought to identify CRGs in GBM and elucidate their role in the tumor immune microenvironment and prognosis. To that aim, changes in CRGs in The Cancer Genome Atlas (TCGA) transcriptional and Gene Expression Omnibus (GEO) datasets (GEO4290 and GEO15824) were characterized, and the expression patterns of these genes were analyzed. Results A risk score based on CRG expression characteristics could predict the survival and prognosis of patients with GBM and was significantly associated with immune infiltration levels and the expression of CD47 and CD24, which are immune checkpoints of the "don't eat me "signal. Furthermore, we found that the CDKN2A gene may predict GBM sensitivity and resistance to drugs. Discussion Our findings suggest that CRGs play a crucial role in GBM outcomes and provide new insights into CRG-related target drugs/molecules for cancer prevention and treatment.
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Affiliation(s)
- Erliang Li
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Huanhuan Qiao
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Jin Sun
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Qiong Ma
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Li Lin
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Yixiang He
- Department of Orthopaedics, The First Affiliated Hospital of Lanzhou University, Gansu, China
| | - Shuang Li
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Xinggang Mao
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Xiaoping Zhang
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
| | - Bo Liao
- Department of Orthopaedics, The Second Affiliated Hospital of Air Force Military Medical University, Xi’an, Shaanxi, China
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11
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Picca A, Bruno F, Nichelli L, Sanson M, Rudà R. Advances in molecular and imaging biomarkers in lower-grade gliomas. Expert Rev Neurother 2023; 23:1217-1231. [PMID: 37982735 DOI: 10.1080/14737175.2023.2285472] [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: 08/07/2023] [Accepted: 11/15/2023] [Indexed: 11/21/2023]
Abstract
INTRODUCTION Lower-grade (grade 2-3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up. AREAS COVERED This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up. EXPERT OPINION The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs.
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Affiliation(s)
- Alberto Picca
- Service de Neurologie 2 Mazarin, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau-Paris Brain Institute-ICM, AP-HP, Paris, France
| | - Francesco Bruno
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University and City of Health and Science University Hospital, Turin, Italy
| | - Lucia Nichelli
- Service de Neuroradiologie, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
| | - Marc Sanson
- Service de Neurologie 2 Mazarin, Hôpital Universitaire Pitié-Salpêtrière, AP-HP, Paris, France
- Sorbonne Université, Inserm, CNRS, UMRS1127, Institut du Cerveau-Paris Brain Institute-ICM, AP-HP, Paris, France
| | - Roberta Rudà
- Division of Neuro-Oncology, Department of Neuroscience "Rita Levi Montalcini", University and City of Health and Science University Hospital, Turin, Italy
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12
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Gudmundson AT, Koo A, Virovka A, Amirault AL, Soo M, Cho JH, Oeltzschner G, Edden RA, Stark C. Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.528046. [PMID: 37205343 PMCID: PMC10187197 DOI: 10.1101/2023.02.10.528046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.
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Affiliation(s)
- Aaron T. Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Annie Koo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Anna Virovka
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Alyssa L. Amirault
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Madelene Soo
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Jocelyn H. Cho
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Richard A.E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD
| | - Craig Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
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13
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Pearl H, Fleischer CC. Association between altered metabolism and genetic mutations in human glioma. Cancer Rep (Hoboken) 2023; 6:e1799. [PMID: 36916606 PMCID: PMC10172161 DOI: 10.1002/cnr2.1799] [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: 10/06/2022] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 03/15/2023] Open
Abstract
BACKGROUND Molecular markers for classification of gliomas include isocitrate dehydrogenase (IDH) mutations and codeletion of chromosomal arms 1p and 19q (1p/19q). While mutations in IDH enzymes result in the well-characterized production of oncometabolite 2-hydroxyglutarate, dysregulation of other metabolites in IDH tumors is less characterized. Similarly, the effects of 1p/19q codeletion on cellular metabolism are also unclear. AIM This study aimed to quantify changes in tumor metabolites in human glioma tissue as a function of both IDH mutation and 1p/19q codeletion. METHODS AND RESULTS Deidentified human glioma tissue and associated clinical data were obtained from the Emory University Winship Cancer Institute tissue biobank from 14 patients (WHO grades II, III, and IV; seven female and seven male). Proton (1 H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy data were acquired using a 600 MHz Bruker AVANCE III NMR spectrometer. Metabolite concentrations were calculated using LCModel. Differences in metabolite concentrations as a function of IDH mutation, 1p/19q codeletion, and survival status were determined using Mann-Whitney U tests. Concentrations of alanine, glutamine, and glutamate were significantly lower in glioma tissue with IDH mutations compared to tissue with IDH wildtype. Additionally, glutamate concentration was significantly lower in glioma tissue with 1p/19q codeletion compared to intact 1p/19q. Exploratory analysis revealed alanine concentration varied significantly as a function of survival status. CONCLUSIONS Given the emerging landscape of glioma treatments that target metabolic dysregulation, an improved understanding of altered metabolism in molecular sub-types of gliomas, including those with IDH mutation and 1p/19q codeletion, is an important consideration for treatment stratification and personalized medicine.
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Affiliation(s)
- Hannah Pearl
- College of Arts and Sciences, Tufts University, Medford, Massachusetts, USA
| | - Candace C Fleischer
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA.,Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
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14
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Li L, Leng W, Chen J, Li S, Lei B, Zhang H, Zhao H. Identification of a copper metabolism-related gene signature for predicting prognosis and immune response in glioma. Cancer Med 2023; 12:10123-10137. [PMID: 36856182 PMCID: PMC10166918 DOI: 10.1002/cam4.5688] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/06/2023] [Accepted: 02/01/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper-induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism-related genes associated with cancer development remain unclear. METHODS We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism-related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis. RESULTS The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high-risk group or low-risk group, and the low-risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high-risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower-grade glioma, and non-tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients. CONCLUSIONS Our study could contribute to the prognosis prediction and decision-making in patients with gliomas.
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Affiliation(s)
- Ling Li
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenyuan Leng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Junying Chen
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Shaoying Li
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bingxi Lei
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huasong Zhang
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Otolaryngology, Longgang E.N.T hospital & Shenzhen Key Laboratory of E.N.T, Institute of E.N.T, Shenzhen, China
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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15
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Branzoli F, Salgues B, Marjańska M, Laloi-Michelin M, Herman P, Le Collen L, Delemer B, Riancho J, Kuhn E, Jublanc C, Burnichon N, Amar L, Favier J, Gimenez-Roqueplo AP, Buffet A, Lussey-Lepoutre C. SDHx mutation and pituitary adenoma: can in vivo 1H-MR spectroscopy unravel the link? Endocr Relat Cancer 2023; 30:ERC-22-0198. [PMID: 36449569 PMCID: PMC9885742 DOI: 10.1530/erc-22-0198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022]
Abstract
Germline mutations in genes encoding succinate dehydrogenase (SDH) are frequently involved in pheochromocytoma/paraganglioma (PPGL) development and were implicated in patients with the '3PAs' syndrome (associating pituitary adenoma (PA) and PPGL) or isolated PA. However, the causality link between SDHx mutation and PA remains difficult to establish, and in vivo tools for detecting hallmarks of SDH deficiency are scarce. Proton magnetic resonance spectroscopy (1H-MRS) can detect succinate in vivo as a biomarker of SDHx mutations in PGL. The objective of this study was to demonstrate the causality link between PA and SDH deficiency in vivo using 1H-MRS as a novel noninvasive tool for succinate detection in PA. Three SDHx-mutated patients suffering from a PPGL and a macroprolactinoma and one patient with an apparently sporadic non-functioning pituitary macroadenoma underwent MRI examination at 3 T. An optimized 1H-MRS semi-LASER sequence (TR = 2500 ms, TE = 144 ms) was employed for the detection of succinate in vivo. Succinate and choline-containing compounds were identified in the MR spectra as single resonances at 2.44 and 3.2 ppm, respectively. Choline compounds were detected in all the tumors (three PGL and four PAs), while a succinate peak was only observed in the three macroprolactinomas and the three PGL of SDHx-mutated patients, demonstrating SDH deficiency in these tumors. In conclusion, the detection of succinate by 1H-MRS as a hallmark of SDH deficiency in vivo is feasible in PA, laying the groundwork for a better understanding of the biological link between SDHx mutations and the development of these tumors.
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Affiliation(s)
- Francesca Branzoli
- Paris Brain Institute - Institut du Cerveau (ICM), Center for Neuroimaging Research (CENIR), Paris, France
- Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France
| | - Betty Salgues
- Sorbonne University, nuclear medicine department, Pitié-Salpêtrière Hospital, Assistance -Publique Hôpitaux de Paris, Paris, France
- Paris Cardiovascular Research Center (PARCC), Inserm, Paris, France
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Marie Laloi-Michelin
- Endocrinology department, Lariboisière Hospital, Assistance -Publique Hôpitaux de Paris, Paris, France
| | - Philippe Herman
- ENT unit, Lariboisière Hospital, Assistance -Publique Hôpitaux de Paris, Paris-Cité University, INSERM U1141, Paris, France
| | - Lauriane Le Collen
- Inserm/CNRS UMR 1283/8199, Pasteur Institute of Lille, EGID, University of Lille, Lille, France
- Department of Endocrinology Diabetology, University Hospital Center of Reims, Reims, France
- Department of Genetic, University Hospital Center of Reims, Reims, France
| | - Brigitte Delemer
- Department of Endocrinology Diabetology, University Hospital Center of Reims, Reims, France
- CRESTIC EA 3804, University of Reims Champagne Ardenne, UFR Sciences Exactes et Naturelles, Moulin de La Housse, BP 1039, Reims, France
| | - Julien Riancho
- AP-HP, Hôpital Européen Georges Pompidou, Hypertension Unit, and Reference centre for rare adrenal diseases, Paris, France
| | - Emmanuelle Kuhn
- Pituitary Unit, Pitié-Salpêtrière Hospital APHP, Sorbonne University, Paris, France
| | - Christel Jublanc
- Pituitary Unit, Pitié-Salpêtrière Hospital APHP, Sorbonne University, Paris, France
| | - Nelly Burnichon
- Département de médecine génomique des tumeurs et des cancers, AP-HP, Hôpital Européen Georges Pompidou, Paris, France
- Université Paris Cité, Inserm, PARCC, Paris, France
| | - Laurence Amar
- AP-HP, Hôpital Européen Georges Pompidou, Hypertension Unit, and Reference centre for rare adrenal diseases, Paris, France
- Université Paris Cité, Inserm, PARCC, Paris, France
| | | | - Anne-Paule Gimenez-Roqueplo
- Département de médecine génomique des tumeurs et des cancers, AP-HP, Hôpital Européen Georges Pompidou, Paris, France
- Université Paris Cité, Inserm, PARCC, Paris, France
| | - Alexandre Buffet
- Département de médecine génomique des tumeurs et des cancers, AP-HP, Hôpital Européen Georges Pompidou, Paris, France
- Université Paris Cité, Inserm, PARCC, Paris, France
| | - Charlotte Lussey-Lepoutre
- Sorbonne University, nuclear medicine department, Pitié-Salpêtrière Hospital, Assistance -Publique Hôpitaux de Paris, Paris, France
- Paris Cardiovascular Research Center (PARCC), Inserm, Paris, France
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16
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Weng G, Ermiş E, Maragkou T, Krcek R, Reinhardt P, Zubak I, Schucht P, Wiest R, Slotboom J, Radojewski P. Accurate prediction of isocitrate dehydrogenase -mutation status of gliomas using SLOW-editing magnetic resonance spectroscopic imaging at 7 T MR. Neurooncol Adv 2023; 5:vdad001. [PMID: 36875625 PMCID: PMC9977233 DOI: 10.1093/noajnl/vdad001] [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] [Indexed: 01/05/2023] Open
Abstract
Background 2-hydroxy-glutarate (2HG) is a metabolite that accumulates in isocitrate dehydrogenase (IDH)-mutated gliomas and can be detected noninvasively using MR spectroscopy. However, due to the low concentration of 2HG, established magnetic resonance spectroscopic imaging (MRSI) techniques at the low field have limitations with respect to signal-to-noise and to the spatial resolution that can be obtained within clinically acceptable measurement times. Recently a tailored editing method for 2HG detection at 7 Tesla (7 T) named SLOW-EPSI was developed. The underlying prospective study aimed to compare SLOW-EPSI to established techniques at 7 T and 3 T for IDH-mutation status determination. Methods The applied sequences were MEGA-SVS and MEGA-CSI at both field strengths and SLOW-EPSI at 7 T only. Measurements were performed on a MAGNETOM-Terra 7 T MR-scanner in clinical mode using a Nova 1Tx32Rx head coil and on a 3 T MAGNETOM-Prisma scanner with a standard 32-channel head coil. Results Fourteen patients with suspected glioma were enrolled. Histopathological confirmation was available in 12 patients. IDH mutation was confirmed in 9 out of 12 cases and 3 cases were characterized as IDH wildtype. SLOW-EPSI at 7 T showed the highest accuracy for IDH-status prediction (91.7% accuracy, 11 of the 12 predictions correct with 1 false negative case). At 7 T, MEGA-CSI had an accuracy of 58.3% and MEGA-SVS had an accuracy of 75%. At 3 T, MEGA-CSI showed an accuracy of 63.6% and MEGA-SVS of 33.3%. The co-edited cystathionine was detected in 2 out of 3 oligodendroglioma cases with 1p/19q codeletion. Conclusions Depending on the pulse sequence, spectral editing can be a powerful tool for the noninvasive determination of the IDH status. SLOW-editing EPSI sequence is the preferable pulse sequence when used at 7 T for IDH-status characterization.
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Affiliation(s)
- Guodong Weng
- Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland.,Translational Imaging Center, sitem-insel, Bern, Switzerland
| | - Ekin Ermiş
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Theoni Maragkou
- Institute of Pathology, University of Bern, Bern, Switzerland
| | - Reinhardt Krcek
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Philipp Reinhardt
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Irena Zubak
- Department of Neurosurgery, Inselspital Bern and University Hospital, Bern, Switzerland
| | - Philippe Schucht
- Department of Neurosurgery, Inselspital Bern and University Hospital, Bern, Switzerland
| | - Roland Wiest
- Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland.,Translational Imaging Center, sitem-insel, Bern, Switzerland
| | - Johannes Slotboom
- Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland.,Translational Imaging Center, sitem-insel, Bern, Switzerland
| | - Piotr Radojewski
- Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland.,Translational Imaging Center, sitem-insel, Bern, Switzerland
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17
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Liu Z, Zhu Y, Zhang L, Jiang W, Liu Y, Tang Q, Cai X, Li J, Wang L, Tao C, Yin X, Li X, Hou S, Jiang D, Liu K, Zhou X, Zhang H, Liu M, Fan C, Tian Y. Structural and functional imaging of brains. Sci China Chem 2022; 66:324-366. [PMID: 36536633 PMCID: PMC9753096 DOI: 10.1007/s11426-022-1408-5] [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: 07/27/2022] [Accepted: 09/28/2022] [Indexed: 12/23/2022]
Abstract
Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.
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Affiliation(s)
- Zhichao Liu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 China
| | - Ying Zhu
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Liming Zhang
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 China
| | - Weiping Jiang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan, 430071 China
| | - Yawei Liu
- State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022 China
| | - Qiaowei Tang
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Xiaoqing Cai
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Jiang Li
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Lihua Wang
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Changlu Tao
- Interdisciplinary Center for Brain Information, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | | | - Xiaowei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Shangguo Hou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055 China
| | - Dawei Jiang
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Kai Liu
- Department of Chemistry, Tsinghua University, Beijing, 100084 China
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan, 430071 China
| | - Hongjie Zhang
- State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022 China
- Department of Chemistry, Tsinghua University, Beijing, 100084 China
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan, 430071 China
| | - Chunhai Fan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Yang Tian
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 China
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18
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Hui SCN, Saleh MG, Zöllner HJ, Oeltzschner G, Fan H, Li Y, Song Y, Jiang H, Near J, Lu H, Mori S, Edden RAE. MRSCloud: A cloud-based MRS tool for basis set simulation. Magn Reson Med 2022; 88:1994-2004. [PMID: 35775808 PMCID: PMC9420769 DOI: 10.1002/mrm.29370] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/16/2022] [Accepted: 06/05/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T. METHODS The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio. RESULTS Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96. CONCLUSIONS Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.
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Affiliation(s)
- Steve C N Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Muhammad G Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Hongli Fan
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yue Li
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- AnatomyWorks, LLC, Ellicott City, Maryland, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Hangyi Jiang
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Jamie Near
- Sunnybrook Research Institute and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Susumu Mori
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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19
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Natsumeda M, Matsuzawa H, Watanabe M, Motohashi K, Gabdulkhaev R, Tsukamoto Y, Kanemaru Y, Watanabe J, Ogura R, Okada M, Kurabe S, Okamoto K, Kakita A, Igarashi H, Fujii Y. SWI by 7T MR Imaging for the Microscopic Imaging Diagnosis of Astrocytic and Oligodendroglial Tumors. AJNR Am J Neuroradiol 2022; 43:1575-1581. [PMID: 36229164 PMCID: PMC9731250 DOI: 10.3174/ajnr.a7666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 08/21/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND AND PURPOSE Despite advances in molecular imaging, preoperative diagnosis of astrocytomas and oligodendrogliomas can be challenging. In the present study, we assessed whether 7T SWI can be used to distinguish astrocytomas and oligodendrogliomas and whether malignant grading of gliomas is possible. MATERIALS AND METHODS 7T SWI was performed on 21 patients with gliomas before surgery with optimization for sharp visualization of the corticomedullary junction. Scoring for cortical thickening and displacement of medullary vessels, characteristic of oligodendroglial tumors, and cortical tapering, characteristic of astrocytic tumors, was performed. Additionally, characteristics of malignancy, including thickening of the medullary veins, the presence of microbleeds, and/or necrosis were scored. RESULTS Scoring for oligodendroglial (highest possible score, +3) and astrocytic (lowest score possible, -3) characteristics yielded a significant difference between astrocytomas and oligodendrogliomas (mean, -1.93 versus +1.71, P < .01). Scoring for malignancy was significantly different among the World Health Organization grade II (n = 10), grade III (n = 4), and grade IV (n = 7) tumors (mean, 0.20 versus 1.38 versus 2.79). Cortical thickening was observed significantly more frequently in oligodendrogliomas (P < .02), with a sensitivity of 71.4% and specificity of 85.7%; observation of tapering of the cortex was higher in astrocytomas (P < .01) with a sensitivity of 85.7% and specificity of 100%. CONCLUSIONS Visualization of the corticomedullary junction by 7T SWI was useful in distinguishing astrocytomas and oligodendrogliomas. Observation of tapering of the cortex was most sensitive and specific for diagnosing astrocytomas. Reliably predicting malignant grade was also possible by 7T SWI.
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Affiliation(s)
- M Natsumeda
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - H Matsuzawa
- Center for Integrated Human Brain Science (H.M., M.W., H.I.)
| | - M Watanabe
- Center for Integrated Human Brain Science (H.M., M.W., H.I.)
| | - K Motohashi
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | | | - Y Tsukamoto
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - Y Kanemaru
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - J Watanabe
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - R Ogura
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - M Okada
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - S Kurabe
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
| | - K Okamoto
- Department of Translational Research (K.O.), Brain Research Institute, Niigata University, Niigata, Japan
| | - A Kakita
- Department of Pathology (R.G., A.K.)
| | - H Igarashi
- Center for Integrated Human Brain Science (H.M., M.W., H.I.)
| | - Y Fujii
- From the Department of Neurosurgery (M.N., K.M., Y.T., Y.K., J.W., R.O., M.O., S.K., Y.F.)
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20
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Sansone G, Vivori N, Vivori C, Di Stefano AL, Picca A. Basic premises: searching for new targets and strategies in diffuse gliomas. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00507-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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Bumes E, Fellner C, Fellner FA, Fleischanderl K, Häckl M, Lenz S, Linker R, Mirus T, Oefner PJ, Paar C, Proescholdt MA, Riemenschneider MJ, Rosengarth K, Weis S, Wendl C, Wimmer S, Hau P, Gronwald W, Hutterer M. Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning. Cancers (Basel) 2022; 14:cancers14112762. [PMID: 35681741 PMCID: PMC9179368 DOI: 10.3390/cancers14112762] [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: 04/30/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The enzyme isocitrate dehydrogenase (IDH) affects glioma cell metabolism in multiple ways. Mutation of IDH is not only indicative of the presence of astrocytoma or oligodendroglioma but it also comes with a better prognosis and constitutes a promising therapeutic target. Therefore, determination of IDH mutation status is essential in clinical practice. In most patients, tissue can be obtained by resection or biopsy to determine IDH status histologically. However, in some cases, this is not possible for technical reasons. We recently showed in a small cohort of patients that non-invasive determination of IDH mutation status using proton magnetic resonance spectroscopy (1H-MRS) at 3.0 Tesla (T) together with machine learning techniques is feasible in a standard clinical setting and with acceptable effort. Here, we demonstrate that our approach showed comparably good results in sensitivity (82.6%) and specificity (72.7%) in a larger validation cohort employing 1H-MRS at 1.5 T in a retrospective, distinct setting. We concluded that our method works well regardless of the magnetic field strength and scanner used, and thus, may improve patient care. Abstract The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (1H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2–95.1%) and a specificity of 72.7% (95% CI, 57.2–85.0%) could be achieved. We concluded that our 1H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting.
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Affiliation(s)
- Elisabeth Bumes
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
- Correspondence: ; Tel.: +49-941-944-18751
| | - Claudia Fellner
- Department of Radiology and Division of Neuroradiology, Regensburg University Hospital, 93055 Regensburg, Germany; (C.F.); (C.W.)
| | - Franz A. Fellner
- Central Institute of Radiology, Kepler University Hospital, 4021 Linz, Austria;
| | - Karin Fleischanderl
- Division of Molecular Pathology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria; (K.F.); (S.L.)
| | - Martina Häckl
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Stefan Lenz
- Division of Molecular Pathology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria; (K.F.); (S.L.)
| | - Ralf Linker
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
| | - Tim Mirus
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Peter J. Oefner
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Christian Paar
- Institute of Laboratory Medicine, Kepler University Hospital, 4021 Linz, Austria;
| | | | | | - Katharina Rosengarth
- Department of Neurosurgery, Regensburg University Hospital, 93053 Regensburg, Germany; (M.A.P.); (K.R.)
| | - Serge Weis
- Division of Neuropathology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria;
| | - Christina Wendl
- Department of Radiology and Division of Neuroradiology, Regensburg University Hospital, 93055 Regensburg, Germany; (C.F.); (C.W.)
| | - Sibylle Wimmer
- Institute of Neuroradiology, Neuromed Campus, Kepler University Hospital, 4020 Linz, Austria;
| | - Peter Hau
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
| | - Wolfram Gronwald
- Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany; (M.H.); (T.M.); (P.J.O.); (W.G.)
| | - Markus Hutterer
- Department of Neurology and Wilhelm Sander-NeuroOncology Unit, Regensburg University Hospital, 93055 Regensburg, Germany; (R.L.); (P.H.); (M.H.)
- Department of Neurology with Acute Geriatrics, Saint John of God Hospital Linz, 4021 Linz, Austria
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22
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Abstract
Abstract
Purpose
Gliomas, the most common primary brain tumours, have recently been re-classified incorporating molecular aspects with important clinical, prognostic, and predictive implications. Concurrently, the reprogramming of metabolism, altering intracellular and extracellular metabolites affecting gene expression, differentiation, and the tumour microenvironment, is increasingly being studied, and alterations in metabolic pathways are becoming hallmarks of cancer. Magnetic resonance spectroscopy (MRS) is a complementary, non-invasive technique capable of quantifying multiple metabolites. The aim of this review focuses on the methodology and analysis techniques in proton MRS (1H MRS), including a brief look at X-nuclei MRS, and on its perspectives for diagnostic and prognostic biomarkers in gliomas in both clinical practice and preclinical research.
Methods
PubMed literature research was performed cross-linking the following key words: glioma, MRS, brain, in-vivo, human, animal model, clinical, pre-clinical, techniques, sequences, 1H, X-nuclei, Artificial Intelligence (AI), hyperpolarization.
Results
We selected clinical works (n = 51), preclinical studies (n = 35) and AI MRS application papers (n = 15) published within the last two decades. The methodological papers (n = 62) were taken into account since the technique first description.
Conclusions
Given the development of treatments targeting specific cancer metabolic pathways, MRS could play a key role in allowing non-invasive assessment for patient diagnosis and stratification, predicting and monitoring treatment responses and prognosis. The characterization of gliomas through MRS will benefit of a wide synergy among scientists and clinicians of different specialties within the context of new translational competences. Head coils, MRI hardware and post-processing analysis progress, advances in research, experts’ consensus recommendations and specific professionalizing programs will make the technique increasingly trustworthy, responsive, accessible.
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23
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Branzoli F, Deelchand DK, Liserre R, Poliani PL, Nichelli L, Sanson M, Lehéricy S, Marjańska M. The influence of cystathionine on neurochemical quantification in brain tumor in vivo MR spectroscopy. Magn Reson Med 2022; 88:537-545. [PMID: 35381117 PMCID: PMC9232981 DOI: 10.1002/mrm.29252] [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: 11/09/2021] [Revised: 02/15/2022] [Accepted: 03/10/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To evaluate the ability of the PRESS sequence (TE = 97 ms, optimized for 2-hydroxyglutarate detection) to detect cystathionine in gliomas and the effect of the omission of cystathionine on the quantification of the full neurochemical profile. METHODS Twenty-three subjects with a glioma were retrospectively included based on the availability of both MEGA-PRESS and PRESS acquisitions at 3T, and the presence of the cystathionine signal in the edited MR spectrum. In eight subjects, the PRESS acquisition was performed also in normal tissue. Metabolite quantification was performed using LCModel and simulated basis sets. The LCModel analysis for the PRESS data was performed with and without cystathionine. RESULTS All subjects with glioma had detectable cystathionine levels >1 mM with Cramér-Rao lower bounds (CRLB) <15%. The mean cystathionine concentrations were 3.49 ± 1.17 mM for MEGA-PRESS and 2.20 ± 0.80 mM for PRESS data. Cystathionine concentrations showed a significant correlation between the two MRS methods (r = 0.58, p = .004), and it was not detectable in normal tissue. Using PRESS, 19 metabolites were quantified with CRLB <50% for more than half of the subjects. The metabolites that were significantly (p < .0028) and mostly affected by the omission of cystathionine were aspartate, betaine, citrate, γ-aminobutyric acid (GABA), and serine. CONCLUSIONS Cystathionine was detectable by PRESS in all the selected gliomas, while it was not detectable in normal tissue. The omission from the spectral analysis of cystathionine led to severe biases in the quantification of other neurochemicals that may play key roles in cancer metabolism.
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Affiliation(s)
- Francesca Branzoli
- Paris Brain Institute-Institut du Cerveau (ICM), Center for Neuroimaging Research (CENIR), Paris, France.,Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France
| | - Dinesh K Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Roberto Liserre
- Department of Radiology, Neuroradiology Unit, ASST Spedali Civili University Hospital, Brescia, Italy
| | - Pietro Luigi Poliani
- Pathology Unit, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Lucia Nichelli
- Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France.,Department of Neuroradiology, Pitié Salpêtrière Hospital, Paris, France
| | - Marc Sanson
- Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France.,Department of Neurology 2, Pitié-Salpêtrière Hospital, Paris, France.,Onconeurotek Tumor Bank, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute-Institut du Cerveau (ICM), Center for Neuroimaging Research (CENIR), Paris, France.,Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France.,Department of Neuroradiology, Pitié Salpêtrière Hospital, Paris, France
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
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Pienkowski T, Kowalczyk T, Garcia-Romero N, Ayuso-Sacido A, Ciborowski M. Proteomics and metabolomics approach in adult and pediatric glioma diagnostics. Biochim Biophys Acta Rev Cancer 2022; 1877:188721. [PMID: 35304294 DOI: 10.1016/j.bbcan.2022.188721] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 12/26/2022]
Abstract
The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.
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Affiliation(s)
- Tomasz Pienkowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland.
| | - Tomasz Kowalczyk
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland; Department of Medical Microbiology and Nanobiomedical Engineering, Medical University of Bialystok, Mickiewicza 2C, 15-222 Bialystok, Poland
| | - Noemi Garcia-Romero
- Faculty of Experimental Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain; Brain Tumor Laboratory, Fundación Vithas, Grupo Hospitales Vithas, 28043 Madrid, Spain
| | - Angel Ayuso-Sacido
- Faculty of Experimental Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain; Brain Tumor Laboratory, Fundación Vithas, Grupo Hospitales Vithas, 28043 Madrid, Spain; Faculty of Medicine, Universidad Francisco de Vitoria, 28223 Madrid, Spain
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276 Bialystok, Poland
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25
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Yu S. Overexpression of SKA Complex Is Associated With Poor Prognosis in Gliomas. Front Neurol 2022; 12:755681. [PMID: 35095717 PMCID: PMC8791909 DOI: 10.3389/fneur.2021.755681] [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: 08/09/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022] Open
Abstract
The spindle and kinetochore-associated complex is composed of three members: SKA1, SKA2, and SKA3. It is necessary for stabilizing spindle microtubules attaching to kinetochore (KT) in the middle stage of mitosis. The SKA complex is associated with poor prognosis in several human cancers. However, the role of SKA complex in rare malignant diseases, such as gliomas, has not been fully investigated. We investigated several databases, including Oncomine, UALCAN, and cBioPortal to explore the expression profile and prognostic significance of SKA complex in patients with gliomas. Gene ontology and Kyoto Encyclopedia of Genes and Genome pathways were used to analyze the potential enriched pathways. The genes co-expressed with SKA complex were identified and used for developing a protein-protein interaction (PPI) network using the STRING database. We found a significant overexpression of the mRNA levels of SKA1, SKA2, and SKA3 in patients with glioma patients. Higher expression of SKA1 and SKA3, but not SKA2, was significantly correlated with shorter overall survival of patients with glioma. In glioma, SKA complex was found to be involved in nuclear division, chromosome segregation, and DNA replication. The results of PPI network identified 10 hub genes (CCNB2, UBE2C, BUB1B, TPX2, CCNA2, CCNB1, MELK, TOP2A, PBK, and KIF11), all of which were overexpressed and negatively associated with prognosis of patients with glioma. In conclusion, our study sheds new insights into the biological role and prognostic significance of SKA complex in glioma.
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Affiliation(s)
- Shoukai Yu
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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26
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Metabolomic Phenotyping of Gliomas: What Can We Get with Simplified Protocol for Intact Tissue Analysis? Cancers (Basel) 2022; 14:cancers14020312. [PMID: 35053475 PMCID: PMC8773998 DOI: 10.3390/cancers14020312] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/02/2022] [Accepted: 01/05/2022] [Indexed: 12/17/2022] Open
Abstract
Glioblastoma multiforme is one of the most malignant neoplasms among humans in their third and fourth decades of life, which is evidenced by short patient survival times and rapid tumor-cell proliferation after radiation and chemotherapy. At present, the diagnosis of gliomas and decisions related to therapeutic strategies are based on genetic testing and histological analysis of the tumor, with molecular biomarkers still being sought to complement the diagnostic panel. This work aims to enable the metabolomic characterization of cancer tissue and the discovery of potential biomarkers via high-resolution mass spectrometry coupled to liquid chromatography and a solvent-free sampling protocol that uses a microprobe to extract metabolites directly from intact tumors. The metabolomic analyses were performed independently from genetic and histological testing and at a later time. Despite the small cohort analyzed in this study, the results indicated that the proposed method is able to identify metabolites associated with different malignancy grades of glioma, as well as IDH and 1p19q codeletion mutations. A comparison of the constellation of identified metabolites and the results of standard tests indicated the validity of using the characterization of one comprehensive tumor phenotype as a reflection of all diagnostically meaningful information. Due to its simplicity, the proposed analytical approach was verified as being compatible with a surgical environment and applicable for large-scale studies.
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27
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Trautwein C, Zizmare L, Mäurer I, Bender B, Bayer B, Ernemann U, Tatagiba M, Grau SJ, Pichler BJ, Skardelly M, Tabatabai G. Tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology and treatment. JCI Insight 2021; 7:153526. [PMID: 34941573 PMCID: PMC8855807 DOI: 10.1172/jci.insight.153526] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
The discovery of the oncometabolite 2-hydroxyglutarate in isocitrate dehydrogenase 1–mutated (IDH1-mutated) tumor entities affirmed the role of metabolism in cancer. However, large databases with tissue metabolites that are modulated by IDH1 mutation remain an area of development. Here, we present an unprecedented and valuable resource for tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology, and tumor treatments in 101 tissue samples from 73 diffuse glioma patients (24 astrocytoma, 17 oligodendroglioma, 32 glioblastoma), investigated by NMR-based metabolomics and supported by RNA-Seq. We discovered comparison-specific metabolites and pathways modulated by IDH1 (IDH1 mutation status cohort) and tumor entity. The Longitudinal investigation cohort provides metabolic profiles of untreated and corresponding treated glioma samples at first progression. Most interestingly, univariate and multivariate cox regressions and Kaplan-Meier analyses revealed that tissue metabolites correlate with progression-free and overall survival. Thus, this study introduces potentially novel candidate prognostic and surrogate metabolite biomarkers for future prospective clinical studies, aiming at further refining patient stratification in diffuse glioma. Furthermore, our data will facilitate the generation of so-far–unanticipated hypotheses for experimental studies to advance our molecular understanding of glioma biology.
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Affiliation(s)
- Christoph Trautwein
- Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Laimdota Zizmare
- Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Irina Mäurer
- Department of Neurology & Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Benjamin Bender
- Department of Neurology & Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Björn Bayer
- Department of Neurology & Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Ulrike Ernemann
- Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Marcos Tatagiba
- Department of Neurosurgery, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Stefan J Grau
- Department of Neurosurgery, University of Cologne, Cologne, Germany
| | - Bernd J Pichler
- Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Marco Skardelly
- Departments of Neurosurgery, Neuroradiology, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Ghazaleh Tabatabai
- Department of Neurology & Interdisciplinary Neuro-Oncology, University Hospital Tübingen, Eberhard Karls University of Tübingen, Tübingen, Germany
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28
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Abstract
This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.
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Affiliation(s)
- Raymond Y Huang
- Department of Neuroradiology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Whitney B Pope
- Radiology, Section of Neuroradiology, Brain Tumor Imaging, UCLA Medical Center, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Boulevard, Suite 615, Los Angeles, CA 90024, USA
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29
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Shams Z, van der Kemp WJM, Emir U, Dankbaar JW, Snijders TJ, de Vos FYF, Klomp DWJ, Wijnen JP, Wiegers EC. Comparison of 2-Hydroxyglutarate Detection With sLASER and MEGA-sLASER at 7T. Front Neurol 2021; 12:718423. [PMID: 34557149 PMCID: PMC8452903 DOI: 10.3389/fneur.2021.718423] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/05/2021] [Indexed: 12/15/2022] Open
Abstract
The onco-metabolite 2-hydroxyglutarate (2HG), a biomarker of IDH-mutant gliomas, can be detected with 1H MR spectroscopy (1H-MRS). Recent studies showed measurements of 2HG at 7T with substantial gain in signal to noise ratio (SNR) and spectral resolution, offering higher specificity and sensitivity for 2HG detection. In this study, we assessed the sensitivity of semi-localized by adiabatic selective refocusing (sLASER) and J-difference MEsher-GArwood-semi-LASER (MEGA-sLASER) for 2HG detection at 7T. We performed spectral editing at long TE using a TE-optimized sLASER sequence (110 ms) and J-difference spectroscopy using MEGA-sLASER (TE = 74ms) in phantoms with different 2HG concentrations to assess the sensitivity of 2HG detection. The robustness of the methods against B0 inhomogeneity was investigated. Moreover, the performance of these two techniques was evaluated in four patients with IDH1-mutated glioma. In contrary to MEGA-sLASER, sLASER was able to detect 2HG concentration as low as 0.5 mM. In case of a composite phantom containing 2HG with overlapping metabolites, MEGA-sLASER provided a clean 2HG signal with higher fitting reliability (lower %CRLB). The results demonstrate that sLASER is more robust against field inhomogeneities and experimental or motion-related artifacts which promotes to adopt sLASER in clinical implementations.
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Affiliation(s)
- Zahra Shams
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Uzay Emir
- School of Health Sciences, Purdue University, West Lafayette, IN, United States
| | - Jan Willem Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tom J Snijders
- Department of Neurology & Neurosurgery, University Medical Center Utrecht/UMC Utrecht Brain Center, Utrecht, Netherlands
| | - Filip Y F de Vos
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Dennis W J Klomp
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jannie P Wijnen
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Evita C Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
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30
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van Zijl PCM, Brindle K, Lu H, Barker PB, Edden R, Yadav N, Knutsson L. Hyperpolarized MRI, functional MRI, MR spectroscopy and CEST to provide metabolic information in vivo. Curr Opin Chem Biol 2021; 63:209-218. [PMID: 34298353 PMCID: PMC8384704 DOI: 10.1016/j.cbpa.2021.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 12/13/2022]
Abstract
Access to metabolic information in vivo using magnetic resonance (MR) technologies has generally been the niche of MR spectroscopy (MRS) and spectroscopic imaging (MRSI). Metabolic fluxes can be studied using the infusion of substrates labeled with magnetic isotopes, with the use of hyperpolarization especially powerful. Unfortunately, these promising methods are not yet accepted clinically, where fast, simple, and reliable measurement and diagnosis are key. Recent advances in functional MRI and chemical exchange saturation transfer (CEST) MRI allow the use of water imaging to study oxygen metabolism and tissue metabolite levels. These, together with the use of novel data analysis approaches such as machine learning for all of these metabolic MR approaches, are increasing the likelihood of their clinical translation.
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Affiliation(s)
- Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA.
| | - Kevin Brindle
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter B Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Richard Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Nirbhay Yadav
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Linda Knutsson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medical Radiation Physics, Lund University, Lund, Sweden
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Abstract
The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.
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32
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Choi IY, Andronesi OC, Barker P, Bogner W, Edden RAE, Kaiser LG, Lee P, Marjańska M, Terpstra M, de Graaf RA. Spectral editing in 1 H magnetic resonance spectroscopy: Experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4411. [PMID: 32946145 PMCID: PMC8557623 DOI: 10.1002/nbm.4411] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 05/08/2023]
Abstract
Spectral editing in in vivo 1 H-MRS provides an effective means to measure low-concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, for example, γ-aminobutyric acid, glutathione and D-2-hydroxyglutarate. Spectral editing strategies utilize known J-coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homonuclear editing techniques and considerations for data acquisition, processing and quantification. Also, we have listed the experts' recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted coedited resonances, spectral fitting of edited spectra, setting up multicenter clinical trials and recommending sequence parameters to be reported in publications.
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Affiliation(s)
- In-Young Choi
- Department of Neurology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas
| | - Ovidiu C Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter Barker
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, F. M. Kirby Center for Functional MRI, Kennedy Krieger Institute, Baltimore, Maryland
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, F. M. Kirby Center for Functional MRI, Kennedy Krieger Institute, Baltimore, Maryland
| | - Lana G Kaiser
- Henry H. Wheeler, Jr. Brain Imaging Center, University of California, Berkeley, California
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Robin A de Graaf
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut
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The role of 2-hydroxyglutarate magnetic resonance spectroscopy for the determination of isocitrate dehydrogenase status in lower grade gliomas versus glioblastoma: a systematic review and meta-analysis of diagnostic test accuracy. Neuroradiology 2021; 63:1823-1830. [PMID: 33811494 DOI: 10.1007/s00234-021-02702-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 03/28/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Magnetic resonance spectroscopy (MRS) provides a non-invasive means of determining isocitrate dehydrogenase (IDH) status. Determination of 2-hydroxyglutarate (2-HG) presence through MRS is a means of determining IDH status; however, differences may be seen by grade. The goal of this paper is to perform a diagnostic test accuracy (DTA) meta-analysis on 2-HG MRS for IDH status in both lower-grade glioma (LGG) and glioblastoma (GBM) in preoperative patients. METHODS A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy guidelines. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2. The search was up to date as of 09/02/2021. Nine English-language journal articles were included. RESULTS The meta-analysis found a pooled sensitivity of 93% (95% CI 58-99%) and specificity of 84% (95% CI 51-96%) for LGG (n= 181). For GBM (n= 77), the pooled sensitivity was 84% (95% CI 25.0-99%) and the specificity was 97% (95% CI 43-100%). CONCLUSION 2-HG MRS shows promise as a non-invasive means of determining IDH status, with specificity higher for GBM and sensitivity higher for LGG. The wide confidence intervals are notable, however, in particular related to the small number of IDH-mutant GBM studied. Diagnostic heterogeneity was particularly present for LGG, and the hierarchical summary receiver operator curves showed poor predictive accuracy in both groups. For more conclusive results, diagnostic test accuracy statistics need to be quantified with larger studies and more deliberate study design.
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34
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A retrospective overview of PHGDH and its inhibitors for regulating cancer metabolism. Eur J Med Chem 2021; 217:113379. [PMID: 33756126 DOI: 10.1016/j.ejmech.2021.113379] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/20/2022]
Abstract
Emerging evidence suggests that cancer metabolism is closely associated to the serine biosynthesis pathway (SSP), in which glycolytic intermediate 3-phosphoglycerate is converted to serine through a three-step enzymatic transformation. As the rate-limiting enzyme in the first step of SSP, phosphoglycerate dehydrogenase (PHGDH) is overexpressed in various diseases, especially in cancer. Genetic knockdown or silencing of PHGDH exhibits obvious anti-tumor response both in vitro and in vivo, demonstrating that PHGDH is a promising drug target for cancer therapy. So far, several types of PHGDH inhibitors have been identified as a significant and newly emerging option for anticancer treatment. Herein, this comprehensive review summarizes the recent achievements of PHGDH, especially its critical role in cancer and the development of PHGDH inhibitors in drug discovery.
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35
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Ruiz-Rodado V, Brender JR, Cherukuri MK, Gilbert MR, Larion M. Magnetic resonance spectroscopy for the study of cns malignancies. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2021; 122:23-41. [PMID: 33632416 PMCID: PMC7910526 DOI: 10.1016/j.pnmrs.2020.11.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 05/04/2023]
Abstract
Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.
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Affiliation(s)
- Victor Ruiz-Rodado
- Neuro-Oncology Branch, National Cancer Institute, Center for Cancer Research, National Institute of Health, Bethesda, United States.
| | - Jeffery R Brender
- Radiation Biology Branch, Center for Cancer Research, National Institute of Health, Bethesda, United States
| | - Murali K Cherukuri
- Radiation Biology Branch, Center for Cancer Research, National Institute of Health, Bethesda, United States
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, Center for Cancer Research, National Institute of Health, Bethesda, United States
| | - Mioara Larion
- Neuro-Oncology Branch, National Cancer Institute, Center for Cancer Research, National Institute of Health, Bethesda, United States.
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Sanvito F, Castellano A, Falini A. Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors. Cancers (Basel) 2021; 13:cancers13030424. [PMID: 33498680 PMCID: PMC7865835 DOI: 10.3390/cancers13030424] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/15/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored. Abstract In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.
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Affiliation(s)
- Francesco Sanvito
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Correspondence: ; Tel.: +39-02-2643-3015
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, 20132 Milan, Italy; (F.S.); (A.F.)
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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Viswanath P, Batsios G, Mukherjee J, Gillespie AM, Larson PEZ, Luchman HA, Phillips JJ, Costello JF, Pieper RO, Ronen SM. Non-invasive assessment of telomere maintenance mechanisms in brain tumors. Nat Commun 2021; 12:92. [PMID: 33397920 PMCID: PMC7782549 DOI: 10.1038/s41467-020-20312-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 11/27/2020] [Indexed: 01/29/2023] Open
Abstract
Telomere maintenance is a universal hallmark of cancer. Most tumors including low-grade oligodendrogliomas use telomerase reverse transcriptase (TERT) expression for telomere maintenance while astrocytomas use the alternative lengthening of telomeres (ALT) pathway. Although TERT and ALT are hallmarks of tumor proliferation and attractive therapeutic targets, translational methods of imaging TERT and ALT are lacking. Here we show that TERT and ALT are associated with unique 1H-magnetic resonance spectroscopy (MRS)-detectable metabolic signatures in genetically-engineered and patient-derived glioma models and patient biopsies. Importantly, we have leveraged this information to mechanistically validate hyperpolarized [1-13C]-alanine flux to pyruvate as an imaging biomarker of ALT status and hyperpolarized [1-13C]-alanine flux to lactate as an imaging biomarker of TERT status in low-grade gliomas. Collectively, we have identified metabolic biomarkers of TERT and ALT status that provide a way of integrating critical oncogenic information into non-invasive imaging modalities that can improve tumor diagnosis and treatment response monitoring.
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Affiliation(s)
- Pavithra Viswanath
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Georgios Batsios
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Joydeep Mukherjee
- Department of Neurological Surgery, Helen Diller Research Center, University of California San Francisco, San Francisco, CA, USA
| | - Anne Marie Gillespie
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - H Artee Luchman
- Department of Cell Biology and Anatomy, Arnie Charbonneau Cancer Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Joanna J Phillips
- Department of Neurological Surgery, Helen Diller Research Center, University of California San Francisco, San Francisco, CA, USA
| | - Joseph F Costello
- Department of Neurological Surgery, Helen Diller Research Center, University of California San Francisco, San Francisco, CA, USA
| | - Russell O Pieper
- Department of Neurological Surgery, Helen Diller Research Center, University of California San Francisco, San Francisco, CA, USA
| | - Sabrina M Ronen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
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Cano-Galiano A, Oudin A, Fack F, Allega MF, Sumpton D, Martinez-Garcia E, Dittmar G, Hau AC, De Falco A, Herold-Mende C, Bjerkvig R, Meiser J, Tardito S, Niclou SP. Cystathionine-γ-lyase drives antioxidant defense in cysteine-restricted IDH1-mutant astrocytomas. Neurooncol Adv 2021; 3:vdab057. [PMID: 34250481 PMCID: PMC8262642 DOI: 10.1093/noajnl/vdab057] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Mutations in isocitrate dehydrogenase 1 or 2 (IDH1/2) define glioma subtypes and are considered primary events in gliomagenesis, impacting tumor epigenetics and metabolism. IDH enzyme activity is crucial for the generation of reducing potential in normal cells, yet the impact of the mutation on the cellular antioxidant system in glioma is not understood. The aim of this study was to determine how glutathione (GSH), the main antioxidant in the brain, is maintained in IDH1-mutant gliomas, despite an altered NADPH/NADP balance. METHODS Proteomics, metabolomics, metabolic tracer studies, genetic silencing, and drug targeting approaches in vitro and in vivo were applied. Analyses were done in clinical specimen of different glioma subtypes, in glioma patient-derived cell lines carrying the endogenous IDH1 mutation and corresponding orthotopic xenografts in mice. RESULTS We find that cystathionine-γ-lyase (CSE), the enzyme responsible for cysteine production upstream of GSH biosynthesis, is specifically upregulated in IDH1-mutant astrocytomas. CSE inhibition sensitized these cells to cysteine depletion, an effect not observed in IDH1 wild-type gliomas. This correlated with an increase in reactive oxygen species and reduced GSH synthesis. Propargylglycine (PAG), a brain-penetrant drug specifically targeting CSE, led to delayed tumor growth in mice. CONCLUSIONS We show that IDH1-mutant astrocytic gliomas critically rely on NADPH-independent de novo GSH synthesis via CSE to maintain the antioxidant defense, which highlights a novel metabolic vulnerability that may be therapeutically exploited.
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Affiliation(s)
- Andrés Cano-Galiano
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Anais Oudin
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Fred Fack
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Maria-Francesca Allega
- Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | | | | | - Gunnar Dittmar
- Quantitative Biology Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Alfonso De Falco
- National Center of Genetics, Laboratoire national de santé, Dudelange, Luxembourg
| | | | - Rolf Bjerkvig
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Johannes Meiser
- Cancer Metabolism Group, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Saverio Tardito
- Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Department of Biomedicine, University of Bergen, Bergen, Norway
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39
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Bolan PJ, Branzoli F, Di Stefano AL, Nichelli L, Valabregue R, Saunders SL, Akçakaya M, Sanson M, Lehéricy S, Marjańska M. Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12267:730-739. [PMID: 35005744 PMCID: PMC8735854 DOI: 10.1007/978-3-030-59728-3_71] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.
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Affiliation(s)
- Patrick J Bolan
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
| | - Francesca Branzoli
- Institut du Cerveau - ICM, Centre de NeuroImagerie de Recherche - CENIR, Paris, France
- Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Anna Luisa Di Stefano
- Hôpital de la Pitié-Salpêtrière, Service de Neurologie 2, Paris, France
- Department of Neurology, Foch Hospital, Suresnes, Paris, France
| | - Lucia Nichelli
- Institut du Cerveau - ICM, Centre de NeuroImagerie de Recherche - CENIR, Paris, France
- Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Romain Valabregue
- Institut du Cerveau - ICM, Centre de NeuroImagerie de Recherche - CENIR, Paris, France
- Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Sara L Saunders
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
| | - Mehmet Akçakaya
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis MN, USA
| | - Marc Sanson
- Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
- Hôpital de la Pitié-Salpêtrière, Service de Neurologie 2, Paris, France
- Onconeurotek tumor bank, Institut du Cerveau et de la Moelle épinère - ICM, Paris, France
| | - Stéphane Lehéricy
- Institut du Cerveau - ICM, Centre de NeuroImagerie de Recherche - CENIR, Paris, France
- Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
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Naganawa S, Capizzano AA, Ota Y, Kim J, Srinivasan A, Moritani T. Succinate detection in glomus jugulare paraganglioma on MRS as a marker for SDHB mutation. OTOLARYNGOLOGY CASE REPORTS 2020. [DOI: 10.1016/j.xocr.2020.100207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
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41
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Takayasu T, Shah M, Dono A, Yan Y, Borkar R, Putluri N, Zhu JJ, Hama S, Yamasaki F, Tahara H, Sugiyama K, Kurisu K, Esquenazi Y, Ballester LY. Cerebrospinal fluid ctDNA and metabolites are informative biomarkers for the evaluation of CNS germ cell tumors. Sci Rep 2020; 10:14326. [PMID: 32868820 PMCID: PMC7459305 DOI: 10.1038/s41598-020-71161-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 08/04/2020] [Indexed: 12/11/2022] Open
Abstract
Serum and cerebrospinal fluid (CSF) levels of α-fetoprotein and β-subunit of human chorionic gonadotropin are used as biomarkers for the management of central nervous system (CNS) germ cell tumors (GCTs). However, additional discriminating biomarkers are required. Especially, biomarkers to differentiate non-germinomatous germ cell tumors (NGGCTs) from germinomas are critical, as these have a distinct prognosis. We investigated CSF samples from 12 patients with CNS-GCT patients (8 germinomas and 4 NGGCTs). We analyzed circulating tumor DNA (ctDNA) in CSF to detect mutated genes. We also used liquid chromatography-mass spectrometry to characterize metabolites in CSF. We detected KIT and/or NRAS mutation, known as frequently mutated genes in GCTs, in 3/12 (25%) patients. We also found significant differences in the abundance of 15 metabolites between control and GCT, with unsupervised hierarchical clustering analysis. Metabolites related to the TCA cycle were increased in GCTs. Urea, ornithine, and short-chain acylcarnitines were decreased in GCTs. Moreover, we also detected several metabolites (e.g., betaine, guanidine acetic acid, and 2-aminoheptanoic acid) that displayed significant differences in abundance in patients with germinomas and NGGCTs. Our results suggest that ctDNA and metabolites in CSF can serve as novel biomarkers for CNS-GCTs and can be useful to differentiate germinomas from NGGCTs.
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Affiliation(s)
- Takeshi Takayasu
- Department of Pathology and Laboratory Medicine, Molecular Genetic Pathology and Neuropathology, The University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA.,Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ward, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Mauli Shah
- Department of Pathology and Laboratory Medicine, Molecular Genetic Pathology and Neuropathology, The University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA
| | - Antonio Dono
- Vivian L. Smith Department of Neurosurgery, UTHealth McGovern Medical School, the University of Texas Health Science Center, Houston, TX, USA
| | - Yuanqing Yan
- Vivian L. Smith Department of Neurosurgery, UTHealth McGovern Medical School, the University of Texas Health Science Center, Houston, TX, USA
| | - Roshan Borkar
- Metabolomics Core, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX, USA
| | - Nagireddy Putluri
- Metabolomics Core, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX, USA
| | - Jay-Jiguang Zhu
- Vivian L. Smith Department of Neurosurgery, UTHealth McGovern Medical School, the University of Texas Health Science Center, Houston, TX, USA.,Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Seiji Hama
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ward, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Fumiyuki Yamasaki
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ward, Hiroshima City, Hiroshima, 734-8551, Japan.
| | - Hidetoshi Tahara
- Department of Cellular and Molecular Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazuhiko Sugiyama
- Department of Clinical Oncology and Neuro-Oncology Program, Hiroshima University Hospital, Hiroshima City, Hiroshima, Japan
| | - Kaoru Kurisu
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3, Kasumi, Minami-ward, Hiroshima City, Hiroshima, 734-8551, Japan
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, UTHealth McGovern Medical School, the University of Texas Health Science Center, Houston, TX, USA.,Memorial Hermann Hospital-TMC, Houston, TX, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center, Houston, USA
| | - Leomar Y Ballester
- Department of Pathology and Laboratory Medicine, Molecular Genetic Pathology and Neuropathology, The University of Texas Health Science Center, 6431 Fannin St., MSB 2.136, Houston, TX, 77030, USA. .,Vivian L. Smith Department of Neurosurgery, UTHealth McGovern Medical School, the University of Texas Health Science Center, Houston, TX, USA. .,Memorial Hermann Hospital-TMC, Houston, TX, USA.
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Branzoli F, Marjańska M. Magnetic resonance spectroscopy of isocitrate dehydrogenase mutated gliomas: current knowledge on the neurochemical profile. Curr Opin Neurol 2020; 33:413-421. [PMID: 32657882 PMCID: PMC7526653 DOI: 10.1097/wco.0000000000000833] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Magnetic resonance spectroscopy (MRS) may play a key role for the management of patients with glioma. We highlighted the utility of MRS in the noninvasive diagnosis of gliomas with mutations in isocitrate dehydrogenase (IDH) genes, by providing an overview of the neurochemical alterations observed in different glioma subtypes, as well as during treatment and progression, both in vivo and ex vivo. RECENT FINDINGS D-2-hydroxyglutarate (2HG) decrease during anticancer treatments was recently shown to be associated with altered levels of other metabolites, including lactate, glutamate and glutathione, suggesting that tumour treatment leads to a metabolic reprogramming beyond 2HG depletion. In combination with 2HG quantification, cystathionine and glycine seem to be the most promising candidates for higher specific identification of glioma subtypes and follow-up of disease progression and response to treatment. SUMMARY The implementation of advanced MRS methods in the routine clinical practice will allow the quantification of metabolites that are not detectable with conventional methods and may enable immediate, accurate diagnosis of gliomas, which is crucial for planning optimal therapeutic strategies and follow-up examinations. The role of different metabolites as predictors of patient outcome still needs to be elucidated.
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Affiliation(s)
- Francesca Branzoli
- Institut du Cerveau - ICM, Centre de Neuroimagerie de Recherche - CENIR
- ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
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Human IDH mutant 1p/19q co-deleted gliomas have low tumor acidity as evidenced by molecular MRI and PET: a retrospective study. Sci Rep 2020; 10:11922. [PMID: 32681084 PMCID: PMC7367867 DOI: 10.1038/s41598-020-68733-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/01/2020] [Indexed: 01/19/2023] Open
Abstract
Co-deletion of 1p/19q is a hallmark of oligodendroglioma and predicts better survival. However, little is understood about its metabolic characteristics. In this study, we aimed to explore the extracellular acidity of WHO grade II and III gliomas associated with 1p/19q co-deletion. We included 76 glioma patients who received amine chemical exchange saturation transfer (CEST) imaging at 3 T. Magnetic transfer ratio asymmetry (MTRasym) at 3.0 ppm was used as the pH-sensitive CEST biomarker, with higher MTRasym indicating lower pH. To control for the confounder factors, T2 relaxometry and l-6-18F-fluoro-3,4-dihydroxyphenylalnine (18F-FDOPA) PET data were collected in a subset of patients. We found a significantly lower MTRasym in 1p/19q co-deleted gliomas (co-deleted, 1.17% ± 0.32%; non-co-deleted, 1.72% ± 0.41%, P = 1.13 × 10−7), while FDOPA (P = 0.92) and T2 (P = 0.61) were not significantly affected. Receiver operating characteristic analysis confirmed that MTRasym could discriminate co-deletion status with an area under the curve of 0.85. In analysis of covariance, 1p/19q co-deletion status was the only significant contributor to the variability in MTRasym when controlling for age and FDOPA (P = 2.91 × 10−3) or T2 (P = 8.03 × 10−6). In conclusion, 1p/19q co-deleted gliomas were less acidic, which may be related to better prognosis. Amine CEST-MRI may serve as a non-invasive biomarker for identifying 1p/19q co-deletion status.
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Keunen O, Niclou SP. Is there a prominent role for MR spectroscopy in the clinical management of brain tumors? Neuro Oncol 2020; 22:903-904. [PMID: 32291457 DOI: 10.1093/neuonc/noaa098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Olivier Keunen
- Quantitative Biology Unit, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Strassen, Luxembourg
- Department of Biomedicine, University of Bergen, Bergen, Norway (S.P.N.)
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45
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Righi V, Cavallini N, Valentini A, Pinna G, Pavesi G, Rossi MC, Puzzolante A, Mucci A, Cocchi M. A metabolomic data fusion approach to support gliomas grading. NMR IN BIOMEDICINE 2020; 33:e4234. [PMID: 31825557 DOI: 10.1002/nbm.4234] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 11/12/2019] [Accepted: 11/12/2019] [Indexed: 06/10/2023]
Abstract
Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T2 -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.
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Affiliation(s)
- Valeria Righi
- Dipartimento di Scienze per la Qualità della Vita, Università di Bologna, Campus Rimini, Corso D'Augusto 237, Rimini, Italy
| | - Nicola Cavallini
- Dipartimento di Scienze Chimiche Geologiche, Università di Modena e Reggio Emilia, via G. Campi 103, Modena, Italy
| | - Antonella Valentini
- Dipartimento Integrato di Neuroscienze, Azienda Ospedaliero-Universitaria di Modena, Via Giardini 1355, Baggiovara, Modena, Italy
| | - Giampietro Pinna
- Dipartimento Integrato di Neuroscienze, Azienda Ospedaliero-Universitaria di Modena, Via Giardini 1355, Baggiovara, Modena, Italy
- Current. Istituto di Neurochirurgia, Azienda Ospedaliera Universitaria Integrata Verona, Piazzale Aristide Stefani 1, Verona, Italy
| | - Giacomo Pavesi
- Dipartimento Integrato di Neuroscienze, Azienda Ospedaliero-Universitaria di Modena, Via Giardini 1355, Baggiovara, Modena, Italy
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena Reggio Emilia, via G. Campi 287, Modena, Italy
| | - Maria Cecilia Rossi
- Centro Interdipartimentale Grandi Strumenti, Università di Modena e Reggio Emilia, via G. Campi 213/A, Modena, Italy
| | - Annette Puzzolante
- Dipartimento Integrato di Neuroscienze, Azienda Ospedaliero-Universitaria di Modena, Via Giardini 1355, Baggiovara, Modena, Italy
| | - Adele Mucci
- Dipartimento di Scienze Chimiche Geologiche, Università di Modena e Reggio Emilia, via G. Campi 103, Modena, Italy
| | - Marina Cocchi
- Dipartimento di Scienze Chimiche Geologiche, Università di Modena e Reggio Emilia, via G. Campi 103, Modena, Italy
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Sullivan MR, Vander Heiden MG. Determinants of nutrient limitation in cancer. Crit Rev Biochem Mol Biol 2019; 54:193-207. [PMID: 31162937 PMCID: PMC6715536 DOI: 10.1080/10409238.2019.1611733] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 04/22/2019] [Accepted: 04/23/2019] [Indexed: 12/12/2022]
Abstract
Proliferation requires that cells accumulate sufficient biomass to grow and divide. Cancer cells within tumors must acquire a variety of nutrients, and tumor growth slows or stops if necessary metabolites are not obtained in sufficient quantities. Importantly, the metabolic demands of cancer cells can be different from those of untransformed cells, and nutrient accessibility in tumors is different than in many normal tissues. Thus, cancer cell survival and proliferation may be limited by different metabolic factors than those that are necessary to maintain noncancerous cells. Understanding the variables that dictate which nutrients are critical to sustain tumor growth may identify vulnerabilities that could be used to treat cancer. This review examines the various cell-autonomous, local, and systemic factors that determine which nutrients are limiting for tumor growth.
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Affiliation(s)
- Mark R Sullivan
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology , Cambridge , MA , USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology , Cambridge , MA , USA
- Dana-Farber Cancer Institute , Boston , MA , USA
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Branzoli F, Deelchand DK, Sanson M, Lehéricy S, Marjańska M. In vivo 1 H MRS detection of cystathionine in human brain tumors. Magn Reson Med 2019; 82:1259-1265. [PMID: 31131476 DOI: 10.1002/mrm.27810] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 03/29/2019] [Accepted: 04/22/2019] [Indexed: 11/09/2022]
Abstract
PURPOSE To report the technical aspects of noninvasive detection of cystathionine in human brain glioma with edited MRS, and to investigate possible further acquisition improvements for robust quantification of this metabolite. METHODS In vivo 1 H MR spectra were acquired at 3 T in 15 participants with an isocitrate dehydrogenase-mutated glioma using a MEGA-PRESS (MEscher GArwood point resolved spectroscopy) sequence previously employed for 2-hydroxyglutarate detection (TR = 2 s, TE = 68 ms). The editing pulse was applied at 1.9 ppm for the edit-on condition and at 7.5 ppm for the edit-off condition. To evaluate the editing efficiency, spectra were acquired in 1 participant by placing the editing pulse for the edit-on condition at 1.9, 2.03, and 2.16 ppm. Cystathionine concentration was quantified using LCModel and a simulated basis set. To confirm chemical shifts and J-coupling values of cystathionine, the 1 H NMR cystathionine spectrum was measured using a high-resolution 500 MHz spectrometer. RESULTS In 12 gliomas, cystathionine was observed in the in vivo edited MR spectra at 2.72 and 3.85 ppm and quantified. The signal intensity of the cystathionine resonance at 2.72 ppm increased 1.7 and 2.13 times when the editing pulse was moved to 2.03 and 2.16 ppm, respectively. Cystathionine was not detectable in normal brain tissue. CONCLUSION Cystathionine can be detected in vivo by edited MRS using the same protocol as for 2-hydroxyglutarate detection. This finding may enable a more accurate, noninvasive investigation of cellular metabolism in glioma.
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Affiliation(s)
- Francesca Branzoli
- Institut du Cerveau et de la Moelle épinère-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Sorbonne Université, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France
| | - Dinesh K Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Marc Sanson
- Sorbonne Université, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neurologie 2, Paris, France.,Onconeurotek Tumor Bank, Institut du Cerveau et de la Moelle épinère-ICM, Paris, France
| | - Stéphane Lehéricy
- Institut du Cerveau et de la Moelle épinère-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.,Sorbonne Université, UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neuroradiologie, Paris, France
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota
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Livermore LJ, Isabelle M, Bell IM, Scott C, Walsby-Tickle J, Gannon J, Plaha P, Vallance C, Ansorge O. Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy. Neurooncol Adv 2019; 1:vdz008. [PMID: 31608327 PMCID: PMC6777649 DOI: 10.1093/noajnl/vdz008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines. METHODS Fresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing. RESULTS Fresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%-94% sensitivity and 90%-100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min. CONCLUSION These data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.
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Affiliation(s)
- Laurent James Livermore
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, UK
| | | | - Ian Mac Bell
- Renishaw plc., Spectroscopy Products Division, UK
| | - Connor Scott
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, UK
| | | | - Joan Gannon
- Department of Chemistry, University of Oxford, UK
| | - Puneet Plaha
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, UK
| | | | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, UK
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