1
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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 IN BIOMEDICINE 2024; 37:e5101. [PMID: 38303627 DOI: 10.1002/nbm.5101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 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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2
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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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Zhao D, Grist JT, Rose HEL, Davies NP, Wilson M, MacPherson L, Abernethy LJ, Avula S, Pizer B, Gutierrez DR, Jaspan T, Morgan PS, Mitra D, Bailey S, Sawlani V, Arvanitis TN, Sun Y, Peet AC. Metabolite selection for machine learning in childhood brain tumour classification. NMR IN BIOMEDICINE 2022; 35:e4673. [PMID: 35088473 DOI: 10.1002/nbm.4673] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N-acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.
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Affiliation(s)
- Dadi Zhao
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - James T Grist
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Imaging and Medical Physics, University Hospitals Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | | | | | | | - Barry Pizer
- Paediatric Oncology, Alder Hey Children's Hospital, Liverpool, UK
| | - Daniel R Gutierrez
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Tim Jaspan
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Paul S Morgan
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Dipayan Mitra
- Neuroradiology, The Newcastle upon Tyne Hospitals, Newcastle upon Tyne, UK
| | - Simon Bailey
- Paediatric Oncology, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Vijay Sawlani
- Radiology, Queen Elizabeth Hospital Birmingham, Birmingham, UK
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Yu Sun
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- University of Birmingham and Southeast University Joint Research Centre for Biomedical Engineering, Suzhou, China
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
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Withey SB, MacPherson L, Oates A, Powell S, Novak J, Abernethy L, Pizer B, Grundy R, Morgan PS, Bailey S, Mitra D, Arvanitis TN, Auer DP, Avula S, Peet AC. Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study. Pediatr Radiol 2022; 52:1134-1149. [PMID: 35290489 PMCID: PMC9107460 DOI: 10.1007/s00247-021-05266-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 08/31/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. OBJECTIVE We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. MATERIALS AND METHODS Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBVuncorr) and corrected (rCBVcorr) relative cerebral blood volume, and the leakage parameter, K2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. RESULTS Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBVuncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. -0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBVuncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. CONCLUSION Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children's brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors.
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Affiliation(s)
- Stephanie B Withey
- RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Lesley MacPherson
- Radiology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Adam Oates
- Radiology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Stephen Powell
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Jan Novak
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Department of Psychology, Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, UK
| | | | - Barry Pizer
- Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Richard Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Paul S Morgan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals, Nottingham, UK
- Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
| | - Simon Bailey
- Sir James Spence Institute of Child Health, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Dipayan Mitra
- Neuroradiology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
| | - Theodoros N Arvanitis
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Dorothee P Auer
- Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospitals Trust, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, Nottingham, UK
| | - Shivaram Avula
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Andrew C Peet
- Oncology, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK.
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
- Children's Brain Tumour Research Team, 4th Floor Institute of Child Health, Birmingham Women's and Children's Hospital NHS Foundation Trust, Steelhouse Lane, Birmingham, B4 6NH, UK.
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5
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Davies NP, Rose HEL, Manias KA, Natarajan K, Abernethy LJ, Oates A, Janjua U, Davies P, MacPherson L, Arvanitis TN, Peet AC. Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours. NMR IN BIOMEDICINE 2022; 35:e4630. [PMID: 34647377 DOI: 10.1002/nbm.4630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/20/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
1 H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.
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Affiliation(s)
- Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Karen A Manias
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Kal Natarajan
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Adam Oates
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Umair Janjua
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Paul Davies
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Lesley MacPherson
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
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Franco P, Huebschle I, Simon-Gabriel CP, Dacca K, Schnell O, Beck J, Mast H, Urbach H, Wuertemberger U, Prinz M, Hosp JA, Delev D, Mader I, Heiland DH. Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy. Cancers (Basel) 2021; 13:cancers13102417. [PMID: 34067701 PMCID: PMC8155922 DOI: 10.3390/cancers13102417] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/13/2021] [Accepted: 05/15/2021] [Indexed: 01/04/2023] Open
Abstract
Simple Summary Radiomics is a research field that integrates radiological and genetic information, but the application of the techniques that have been developed to this purpose have not been widely established in daily clinical practice. The purpose of our study is the development of a straightforward tool that can easily be used to preoperatively predict and correlate the metabolic signature of different CNS-lesions. Particularly in gliomas, we hope to integrate the molecular profile of these tumors into our prediction model. Our goal is to deliver an open-software tool with the intention of advancing the diagnostic work-up of gliomas to the latest standards. Abstract Proton magnetic resonance spectroscopy (1H-MRS) delivers information about the non-invasive metabolic landscape of brain pathologies. 1H-MRS is used in clinical setting in addition to MRI for diagnostic, prognostic and treatment response assessments, but the use of this radiological tool is not entirely widespread. The importance of developing automated analysis tools for 1H-MRS lies in the possibility of a straightforward application and simplified interpretation of metabolic and genetic data that allow for incorporation into the daily practice of a broad audience. Here, we report a prospective clinical imaging trial (DRKS00019855) which aimed to develop a novel MR-spectroscopy-based algorithm for in-depth characterization of brain lesions and prediction of molecular traits. Dimensional reduction of metabolic profiles demonstrated distinct patterns throughout pathologies. We combined a deep autoencoder and multi-layer linear discriminant models for voxel-wise prediction of the molecular profile based on MRS imaging. Molecular subtypes were predicted by an overall accuracy of 91.2% using a classifier score. Our study indicates a first step into combining the metabolic and molecular traits of lesions for advancing the pre-operative diagnostic workup of brain tumors and improve personalized tumor treatment.
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Affiliation(s)
- Pamela Franco
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Correspondence: ; Tel.: +49-(0)-761-270-50010; Fax: +49-(0)-761-270-51020
| | - Irene Huebschle
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Carl Philipp Simon-Gabriel
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Radiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany
| | - Karam Dacca
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Juergen Beck
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
| | - Hansjoerg Mast
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
| | - Horst Urbach
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
| | - Urs Wuertemberger
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
| | - Marco Prinz
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Signaling Research Centers BIOSS and CIBSS, University of Freiburg, 79106 Freiburg, Germany
- Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Jonas A. Hosp
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neurology and Neuroscience, Medical Center-University of Freiburg, 79106 Freiburg, Germany
| | - Daniel Delev
- Department of Neurosurgery, RWTH University of Aachen, 52074 Aachen, Germany;
| | - Irina Mader
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Department of Neuroradiology, Medical Center-University of Freiburg, 79106 Freiburg, Germany;
- Specialist Centre for Radiology, Schoen Clinic, 83569 Vogtareuth, Germany
| | - Dieter Henrik Heiland
- Department of Neurosurgery, Medical Center-University of Freiburg, 79106 Freiburg, Germany; (I.H.); (K.D.); (O.S.); (J.B.); (D.H.H.)
- Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; (C.P.S.-G.); (H.U.); (U.W.); (M.P.); (J.A.H.); (I.M.)
- Microenvironment and Immunology Research Laboratory, Medical Center-University of Freiburg, 79106 Freiburg, Germany
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7
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Attia NM, Sayed SAA, Riad KF, Korany GM. Magnetic resonance spectroscopy in pediatric brain tumors: how to make a more confident diagnosis. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-0135-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Non-invasive diagnosis of pediatric brain tumors can be challenging due to diverse tumor pathologies and similar imaging appearances. Magnetic resonance spectroscopy (MRS), when combined with high spatial resolution anatomic imaging obtained with conventional magnetic resonance imaging (MRI), provides metabolic information within the lesion as well as the surrounding tissue. The differentiation of neoplastic from non-neoplastic lesions and low-grade from high-grade neoplasms is essential for determining the choice of treatment and the best treatment plan. We aimed to measure specific metabolic ratios and evaluate metabolic profiles of various lesions by MRS to assist in making a more confident diagnosis.
Results
The choline/creatine (Cho/Cr), choline/N-acetylaspartate (Cho/NAA), and Cho/NAA+Cr ratios all had statistically significant values for the differentiation between neoplastic and non-neoplastic lesions at cutoffs 1.8, 2, and 0.8 respectively. The Cho/NAA, Cho/Cr, Cho/NAA+Cr, and myo-inositol/creatine (mI/Cr) ratios all had statistically significant values for the differentiation of high-grade from low-grade neoplasms at cutoffs 3.3, 3.5, 1.3, and 1.5 respectively. The presence of a lipid lactate peak was only significant for differentiating high-grade from low-grade neoplasms. Medulloblastomas, diffuse pontine gliomas, and choroid plexus carcinoma all showed characteristic metabolic profiles on MRS. Metastasis showed lower Cho/NAA and Cho/Cr ratios outside the tumor margin than high-grade neoplasms.
Conclusion
The use of certain metabolite ratios with high sensitivity and specificity to distinguish neoplastic from non-neoplastic lesions and low-grade from high-grade neoplasms while assessing the metabolic profile of the lesion aids in the non-invasive diagnosis of pediatric brain tumors. MRS facilitates earlier treatment planning by determining tumor spatial extent and predicting tumor behavior with potential to solve sampling problems of inaccessible and heterogenous lesions as well as unnecessary sampling of benign lesions.
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Zhou H, Hu R, Tang O, Hu C, Tang L, Chang K, Shen Q, Wu J, Zou B, Xiao B, Boxerman J, Chen W, Huang RY, Yang L, Bai HX, Zhu C. Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging. AJNR Am J Neuroradiol 2020; 41:1279-1285. [PMID: 32661052 PMCID: PMC7357647 DOI: 10.3174/ajnr.a6621] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 04/30/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND PURPOSE Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging. MATERIALS AND METHODS This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (n = 111), ependymoma (n = 70), and pilocytic astrocytoma (n = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. RESULTS For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P < .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma. CONCLUSIONS Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
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Affiliation(s)
- H Zhou
- Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - R Hu
- From the School of Computer Science and Engineering (R.H., B.Z., C.Z.)
| | - O Tang
- Warren Alpert Medical School, Brown University (O.T.), Providence, Rhode Island
| | - C Hu
- Department of Neurology (C.H.), Hunan Provincial People's Hospital, Changsha, Hunan, China
| | - L Tang
- Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - K Chang
- Department of Radiology (K.C.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Q Shen
- Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - J Wu
- Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - B Zou
- From the School of Computer Science and Engineering (R.H., B.Z., C.Z.)
| | - B Xiao
- Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - J Boxerman
- Department of Diagnostic Imaging (J.B., H.X.B.), Rhode Island Hospital
| | - W Chen
- Department of Pathology (W.C.), Hunan Children's Hospital, Changsha, Hunan, China
| | - R Y Huang
- Department of Radiology (R.Y.H.), Brigham and Women's Hospital, Boston, Massachusetts
| | - L Yang
- Departments of Neurology (L.Y.)
| | - H X Bai
- Department of Diagnostic Imaging (J.B., H.X.B.), Rhode Island Hospital
| | - C Zhu
- From the School of Computer Science and Engineering (R.H., B.Z., C.Z.)
- College of Literature and Journalism (C.Z.), Central South University, Changsha, Hunan, China
- Mobile Health Ministry of Education-China Mobile Joint Laboratory (C.Z.), China
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Ahmed HAK, Mokhtar H. The diagnostic value of MR spectroscopy versus DWI-MRI in therapeutic planning of suspicious multi-centric cerebral lesions. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00154-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Abstract
Background
A broad spectrum of non-neoplastic lesions can radiologically mimic cerebral neoplasms. Magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and diffusion-weighted imaging (DWI) are the most extensively used for enabling lesional characterization of different brain disorders. We aimed to assess the diagnostic value of MRS versus DWI in the diagnosis and therapeutic planning of multicentric cerebral focal lesions and in our retrospective study, we enrolled 64 patients with 100 brain lesions who underwent pre- and post-contrast MRI, MRS, and DWI. Diagnoses supplied by the histopathology and follow up clinical results as a gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated.
Results
Conventional MRI poorly differentiates multiple cerebral lesions with 89.33% sensitivity, 44.4% specificity, and 78% accuracy. MRS results revealed statistical significance for differentiating neoplastic from non-neoplastic lesions as regards Cho/Cr, Cho/NAA, and NAA/Cr ratios (M ± SD) with P < 0.001 (significant), and there is statistical significance for neoplastic lesion differentiation when Cho/NAA and Ch/Cr ratios measured in the pre-lesional areas outside the tumor margin. DWI showed mixed diffusion changes in most of the studied lesions and the measured ADC values ranges showed overlap in neoplastic and non-neoplastic lesions, P value = 0.236* (insignificant).
Conclusion
MRS was found to be a more accurate diagnostic tool than DWI with ADC measurements in the differentiation and therapeutic planning of multicentric cerebral focal lesions.
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Manias KA, Gill SK, MacPherson L, Oates A, Pinkey B, Davies P, Zarinabad N, Davies NP, Babourina-Brooks B, Wilson M, Peet AC. Diagnostic accuracy and added value of qualitative radiological review of 1H-magnetic resonance spectroscopy in evaluation of childhood brain tumors. Neurooncol Pract 2019; 6:428-437. [PMID: 31832213 DOI: 10.1093/nop/npz010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background 1H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI. Methods Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end. Results Radiologists' principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. Conclusions MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.
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Affiliation(s)
- Karen A Manias
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK.,Department of Pediatric Oncology, Birmingham Children's Hospital, UK
| | - Simrandip K Gill
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK.,Department of Pediatric Oncology, Birmingham Children's Hospital, UK
| | | | - Adam Oates
- Department of Radiology, Birmingham Children's Hospital, UK
| | | | - Paul Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK
| | | | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK.,Department of Pediatric Oncology, Birmingham Children's Hospital, UK.,Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, UK
| | | | | | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK.,Department of Pediatric Oncology, Birmingham Children's Hospital, UK
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11
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Manias KA, Peet A. What is MR spectroscopy? Arch Dis Child Educ Pract Ed 2018; 103:213-216. [PMID: 28844055 DOI: 10.1136/archdischild-2017-312839] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 07/03/2017] [Accepted: 07/17/2017] [Indexed: 11/03/2022]
Abstract
1H-Magnetic Resonance Spectroscopy (MRS) is a novel advanced imaging technique used as an adjunct to MRI to reveal complementary non-invasive information about the biochemical composition of imaged tissue. Clinical uses in paediatrics include aiding diagnosis of brain tumours, neonatal disorders such as hypoxic-ischaemic encephalopathy, inherited metabolic diseases, traumatic brain injury, demyelinating conditions and infectious brain lesions. MRS has potential to improve diagnosis and treatment monitoring of childhood brain tumours and other CNS diseases, facilitate biopsy and surgical planning, and provide prognostic biomarkers. MRS is employed as a research tool outside the brain in liver disease and disorders of muscle metabolism. The range of clinical uses is likely to increase with growing evidence for added value. Multicentre trials are needed to definitively establish the benefits of MRS in specific clinical scenarios and integrate this promising new technique into routine practice to improve patient care. This article gives a brief overview of MRS and its potential clinical applications, and addresses challenges surrounding translation into practice.
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Affiliation(s)
- Karen Angela Manias
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, West Midlands, UK.,Department of Paediatric Oncology, Birmingham Children's Hospital, Birmingham, West Midlands, UK
| | - Andrew Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, West Midlands, UK.,Department of Paediatric Oncology, Birmingham Children's Hospital, Birmingham, West Midlands, UK
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