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Gill SK, Rose HEL, Wilson M, Rodriguez Gutierrez D, Worthington L, Davies NP, MacPherson L, Hargrave DR, Saunders DE, Clark CA, Payne GS, Leach MO, Howe FA, Auer DP, Jaspan T, Morgan PS, Grundy RG, Avula S, Pizer B, Arvanitis TN, Peet AC. Characterisation of paediatric brain tumours by their MRS metabolite profiles. NMR Biomed 2024; 37:e5101. [PMID: 38303627 DOI: 10.1002/nbm.5101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 11/20/2023] [Accepted: 12/04/2023] [Indexed: 02/03/2024]
Abstract
1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.
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Affiliation(s)
- Simrandip K Gill
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Martin Wilson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | | | - Lara Worthington
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Darren R Hargrave
- Paediatric Oncology Unit, Great Ormond Street Hospital For Sick Children, London, UK
| | - Dawn E Saunders
- Paediatric Oncology Unit, Great Ormond Street Hospital For Sick Children, London, UK
| | - Christopher A Clark
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Geoffrey S Payne
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Martin O Leach
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Franklyn A Howe
- Neurosciences Research Section, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, UK
| | - Dorothee P Auer
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Radiological Sciences, Department of Clinical Neuroscience, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
| | - Tim Jaspan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
| | - Paul S Morgan
- Medical Physics, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Richard G Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Shivaram Avula
- Department of Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Barry Pizer
- Department of Paediatric Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Theodoros N Arvanitis
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, UK
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
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Abstract
The shortly upcoming 5th edition of the World Health Organization Classification of Tumors of the Central Nervous System is bringing extensive changes in the terminology of diffuse high-grade gliomas (DHGGs). Previously "glioblastoma," as a descriptive entity, could have been applied to classify some tumors from the family of pediatric or adult DHGGs. However, now the term "glioblastoma" has been divested and is no longer applied to tumors in the family of pediatric types of DHGGs. As an entity, glioblastoma remains, however, in the family of adult types of diffuse gliomas under the insignia of "glioblastoma, IDH-wildtype." Of note, glioblastomas still can be detected in children when glioblastoma, IDH-wildtype is found in this population, despite being much more common in adults. Despite the separation from the family of pediatric types of DHGGs, what was previously labeled as "pediatric glioblastomas" still remains with novel labels and as new entities. As a result of advances in molecular biology, most of the previously called "pediatric glioblastomas" are now classified in one of the four family members of pediatric types of DHGGs. In this review, the term glioblastoma is still apocryphally employed mainly due to its historical relevance and the paucity of recent literature dealing with the recently described new entities. Therefore, "glioblastoma" is used here as an umbrella term in the attempt to encompass multiple entities such as astrocytoma, IDH-mutant (grade 4); glioblastoma, IDH-wildtype; diffuse hemispheric glioma, H3 G34-mutant; diffuse pediatric-type high-grade glioma, H3-wildtype and IDH-wildtype; and high grade infant-type hemispheric glioma. Glioblastomas are highly aggressive neoplasms. They may arise anywhere in the developing central nervous system, including the spinal cord. Signs and symptoms are non-specific, typically of short duration, and usually derived from increased intracranial pressure or seizure. Localized symptoms may also occur. The standard of care of "pediatric glioblastomas" is not well-established, typically composed of surgery with maximal safe tumor resection. Subsequent chemoradiation is recommended if the patient is older than 3 years. If younger than 3 years, surgery is followed by chemotherapy. In general, "pediatric glioblastomas" also have a poor prognosis despite surgery and adjuvant therapy. Magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of glioblastomas. In addition to the typical conventional MRI features, i.e., highly heterogeneous invasive masses with indistinct borders, mass effect on surrounding structures, and a variable degree of enhancement, the lesions may show restricted diffusion in the solid components, hemorrhage, and increased perfusion, reflecting increased vascularity and angiogenesis. In addition, magnetic resonance spectroscopy has proven helpful in pre- and postsurgical evaluation. Lastly, we will refer to new MRI techniques, which have already been applied in evaluating adult glioblastomas, with promising results, yet not widely utilized in children.
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Affiliation(s)
- Fabrício Guimarães Gonçalves
- Division of Neuroradiology, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Arastoo Vossough
- Division of Neuroradiology, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Dastmalchi F, Deleyrolle LP, Karachi A, Mitchell DA, Rahman M. Metabolomics Monitoring of Treatment Response to Brain Tumor Immunotherapy. Front Oncol 2021; 11:691246. [PMID: 34150663 PMCID: PMC8209463 DOI: 10.3389/fonc.2021.691246] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 05/17/2021] [Indexed: 12/15/2022] Open
Abstract
Immunotherapy has revolutionized care for many solid tissue malignancies, and is being investigated for efficacy in the treatment of malignant brain tumors. Identifying a non-invasive monitoring technique such as metabolomics monitoring to predict patient response to immunotherapy has the potential to simplify treatment decision-making and to ensure therapy is tailored based on early patient response. Metabolomic analysis of peripheral immune response is feasible due to large metabolic shifts that immune cells undergo when activated. The utility of this approach is under investigation. In this review, we discuss the metabolic changes induced during activation of an immune response, and the role of metabolic profiling to monitor immune responses in the context of immunotherapy for malignant brain tumors. This review provides original insights into how metabolomics monitoring could have an important impact in the field of tumor immunotherapy if achievable.
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Affiliation(s)
- Farhad Dastmalchi
- Department of Neurosurgery, Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, United States
| | - Loic P Deleyrolle
- Department of Neurosurgery, Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, United States
| | - Aida Karachi
- Department of Neurosurgery, Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, United States
| | - Duane A Mitchell
- Department of Neurosurgery, Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, United States
| | - Maryam Rahman
- Department of Neurosurgery, Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, United States
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Morana G, Piccardo A, Puntoni M, Nozza P, Cama A, Raso A, Mascelli S, Massollo M, Milanaccio C, Garrè ML, Rossi A. Diagnostic and prognostic value of 18F-DOPA PET and 1H-MR spectroscopy in pediatric supratentorial infiltrative gliomas: a comparative study. Neuro Oncol 2015; 17:1637-47. [PMID: 26405202 DOI: 10.1093/neuonc/nov099] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 05/05/2015] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND (1)H-MR spectroscopy (MRS) and (18)F-dihydroxyphenylalanine (DOPA) PET are noninvasive imaging techniques able to assess metabolic features of brain tumors. The aim of this study was to compare diagnostic and prognostic information gathered by (18)F-DOPA PET and (1)H-MRS in children with supratentorial infiltrative gliomas or nonneoplastic brain lesions suspected to be gliomas. METHODS We retrospectively analyzed 27 pediatric patients with supratentorial infiltrative brain lesions on conventional MRI (21 gliomas and 6 nonneoplastic lesions) who underwent (18)F-DOPA PET and (1)H-MRS within 2 weeks of each other. (1)H-MRS data (choline/N-acetylaspartate, choline-to-creatine ratios, and presence of lactate) and (18)F-DOPA uptake parameters (lesion-to-normal tissue and lesion-to-striatum ratios) were compared and correlated with histology, WHO tumor grade, and patient outcome. RESULTS (1)H-MRS and (18)F-DOPA PET data were positively correlated. Sensitivity, specificity, and accuracy in distinguishing gliomas from nonneoplastic lesions were 95%, 83%, and 93% for (1)H-MRS and 76%, 83%, and 78% for (18)F-DOPA PET, respectively. No statistically significant differences were found between the 2 techniques (P > .05). Significant differences regarding (18)F-DOPA uptake and (1)H-MRS ratios were found between low-grade and high-grade gliomas (P≤.001 and P≤.04, respectively). On multivariate analysis, (18)F-DOPA uptake independently correlated with progression-free survival (P≤.05) and overall survival (P = .04), whereas (1)H-MRS did not show significant association with outcome. CONCLUSIONS (1)H-MRS and (18)F-DOPA PET provide useful complementary information for evaluating the metabolism of pediatric brain lesions. (1)H-MRS represents the method of first choice for differentiating brain gliomas from nonneoplastic lesions.(18)F-DOPA uptake better discriminates low-grade from high-grade gliomas and is an independent predictor of outcome.
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Affiliation(s)
- Giovanni Morana
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Arnoldo Piccardo
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Matteo Puntoni
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Paolo Nozza
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Armando Cama
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Alessandro Raso
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Samantha Mascelli
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Michela Massollo
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Claudia Milanaccio
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Maria Luisa Garrè
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
| | - Andrea Rossi
- Istituto Giannina Gaslini, Genova, Italy (G.M., P.N., A.C., A.R., S.M., C.M., M.L.G., A.R.); Nuclear Medicine Unit, Ospedali Galliera, Genova, Italy (A.P., M.M.); Clinical Trial Unit, Scientific Directorate, Ospedali Galliera, Genova, Italy (M.P.)
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