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Ungan G, Pons-Escoda A, Ulinic D, Arús C, Vellido A, Julià-Sapé M. Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study. Cancers (Basel) 2023; 15:3709. [PMID: 37509372 PMCID: PMC10377805 DOI: 10.3390/cancers15143709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/26/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. PURPOSE To test whether MV grids can be classified with models trained with SV. METHODS Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. RESULTS The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. DISCUSSION The reasons for failure in the classification of the MV test set were related to the presence of artifacts.
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Affiliation(s)
- Gülnur Ungan
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Albert Pons-Escoda
- Group de Neuro-Oncologia, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, 08908 Barcelona, Spain
| | - Daniel Ulinic
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Alfredo Vellido
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- IDEAI-UPC Research Center, UPC BarcelonaTech, 08034 Barcelona, Spain
| | - Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red (CIBER), 28029 Madrid, Spain
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
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Dikaios N. Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data. NMR IN BIOMEDICINE 2021; 34:e4479. [PMID: 33448078 DOI: 10.1002/nbm.4479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/24/2020] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Abstract
Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the proton signal of different molecules present in the tissue. However, its performance is severely hindered by low SNR, field inhomogeneities and overlapping spectra of metabolites, which affect the quantification of metabolites. Consequently, MRS is rarely included in routine clinical protocols and has not been proven in multi-institutional trials. This work proposes an alternative approach, where instead of quantifying metabolites' concentration, deep learning (DL) is used to model the complex nonlinear relationship between diseases and their spectroscopic metabolic fingerprint (pattern). DL requires large training datasets, acquired (ideally) with the same protocol/scanner, which are very rarely available. To overcome this limitation, a novel method is proposed that can quantum mechanically synthesise MRS data for any scanner/acquisition protocol. The proposed methodology is applied to the challenging clinical problem of differentiating metastasis from glioblastoma brain tumours on data acquired across multiple institutions. DL algorithms were trained on the augmented synthetic spectra and tested on two independent datasets acquired by different scanners, achieving a receiver operating characteristic area under the curve of up to 0.96 and 0.97, respectively.
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Affiliation(s)
- Nikolaos Dikaios
- Mathematics Research Center, Academy of Athens, Athens, Greece
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
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Differentiation of multiple sclerosis lesions and low-grade brain tumors on MRS data: machine learning approaches. Neurol Sci 2021; 42:3389-3395. [PMID: 33411201 DOI: 10.1007/s10072-020-04950-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 11/28/2020] [Indexed: 10/22/2022]
Abstract
Some multiple sclerosis (MS) lesions may have great similarities with neoplastic brain lesions in magnetic resonance (MR) imaging and thus wrong diagnoses may occur. In this study, differentiation of MS and low-grade brain tumors was performed with computer-aided diagnosis (CAD) methods by magnetic resonance spectroscopy (MRS) data. MRS data belonging to 51 MS and 39 low-grade brain tumor patients were obtained. The feature extraction from MRS data was performed by the help of peak integration (PI) and full spectra (FS) methods and the most significant features were identified. For the classification step, artificial neural network (ANN), support vector machine (SVM), and linear discriminant analysis (LDA) methods were used and the differentiation between MS and brain tumor was performed automatically. Examining the results, one can conclude that data which belong to MS and low-grade brain tumor cases were automatically differentiated from each other with the help of ANN with 100% accuracy, 100% sensitivity, and 100% specificity. Using of MR spectroscopy and artificial intelligence methods may be useful as a complementary imaging technique to MR imaging in the differentiation of MS lesions and low-grade brain tumors.
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EkŞİ Z, ÇakiroĞlu M, Öz C, AralaŞmak A, Karadelİ HH, Özcan ME. Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning. ARQUIVOS DE NEURO-PSIQUIATRIA 2020; 78:789-796. [PMID: 33331515 DOI: 10.1590/0004-282x20200094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process. OBJECTIVE This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods. METHODS MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NAA) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. RESULTS RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity. CONCLUSIONS A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.
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Affiliation(s)
- Ziya EkŞİ
- Sakarya University, Department of Computer Engineering, Sakarya, Turkey
| | - Murat ÇakiroĞlu
- Sakarya University, Department of Mechatronic Engineering, Sakarya, Turkey
| | - Cemil Öz
- Sakarya University, Department of Computer Engineering, Sakarya, Turkey
| | - Ayse AralaŞmak
- Memorial Bahçelievler Hospital, Department of Radiology, Istanbul, Turkey
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Hellström J, Romanos Zapata R, Libard S, Wikström J, Ortiz-Nieto F, Alafuzoff I, Raininko R. Evaluation of the INTERPRET decision-support system: can it improve the diagnostic value of magnetic resonance spectroscopy of the brain? Neuroradiology 2018; 61:43-53. [PMID: 30443796 PMCID: PMC6336758 DOI: 10.1007/s00234-018-2129-7] [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: 07/24/2018] [Accepted: 11/01/2018] [Indexed: 12/05/2022]
Abstract
Purpose We evaluated in a clinical setting the INTERPRET decision-support system (DSS), a software generated to aid in MRS analysis to achieve a specific diagnosis for brain lesions. Methods The material consisted of 100 examinations of focal intracranial lesions with confirmed diagnoses. MRS was obtained at 1.5 T using TE 20–30 ms. Data were processed with the LCModel for conventional analysis. The INTERPRET DSS 3.1. was used to obtain specific diagnoses. MRI and MRS were reviewed by one interpreter. DSS analysis was made by another interpreter, in 80 cases by two interpreters. The diagnoses were compared with the definitive diagnoses. For comparisons between DSS, conventional MRS analysis, and MRI, the diagnoses were categorised: high-grade tumour, low-grade tumour, non-neoplastic lesion. Results Interobserver agreement in choosing the diagnosis from the INTERPRET database was 75%. The diagnosis was correct in 38/100 cases, incorrect in 57 cases. No good match was found in 5/100 cases. The diagnostic category was correct with DSS/conventional MRS/MRI in 67/58/52 cases, indeterminate in 5/8/20 cases, incorrect in 28/34/28 cases. Results with DSS were not significantly better than with conventional MRS analysis. All definitive diagnoses did not exist in the INTERPRET database. In the 61 adult patients with the diagnosis included in the database, DSS/conventional MRS/MRI yielded a correct diagnosis category in 48/32/29 cases (DSS vs conventional MRS: p = 0.002, DSS vs MRI: p = 0.0004). Conclusion Use of the INTERPRET DSS did not improve MRS categorisation of the lesions in the unselected clinical cases. In adult patients with lesions existing in the INTERPRET database, DSS improved the results, which indicates the potential of this software with an extended database. Electronic supplementary material The online version of this article (10.1007/s00234-018-2129-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- J Hellström
- Department of Radiology, Uppsala University, Uppsala, Sweden.
| | | | - S Libard
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.,Department of Pathology, Uppsala University Hospital, Uppsala, Sweden
| | - J Wikström
- Department of Radiology, Uppsala University, Uppsala, Sweden
| | - F Ortiz-Nieto
- Department of Radiology, Uppsala University, Uppsala, Sweden
| | - I Alafuzoff
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.,Department of Pathology, Uppsala University Hospital, Uppsala, Sweden
| | - R Raininko
- Department of Radiology, Uppsala University, Uppsala, Sweden
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Beigi M, Ghasemi K, Mirzaghavami P, Khanmohammadi M, SalighehRad H. Malignancy probability map as a novel imaging biomarker to predict malignancy distribution: employing MRS in GBM patients. J Neurooncol 2018. [PMID: 29542059 DOI: 10.1007/s11060-018-2829-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The main aim of this study was to propose a new statistical method for evaluation of spatial malignancy distribution within Magnetic Resonance Spectroscopy (MRS) grid in Glioblastoma Multiforme patients. Voxels with different malignancy probabilities were presented as a novel MRS-based Malignancy Probability Map (MPM). For this purpose, a predictive probability-based clustering approach was developed, including the two following steps: (1) Gaussian Mixture Model, (2) Quadratic Discriminate Analysis coupled with Genetic Algorithm. Clustered probability values from two methods were then integrated to exploit the MPM. Results show that the suggested method is able to estimate the malignancy distribution with over 90% sensitivity and specificity. The proposed MRS-based MPM has an acceptable accuracy for providing useful complementary information about regional diffuse glioma malignancy, with the potential to lead to better detection of tumoral regions with high probability of malignancy. So, it also may encourage the use of additional information of this map as a tool for dose painting.
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Affiliation(s)
- Manijeh Beigi
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Kevan Ghasemi
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Parvin Mirzaghavami
- Medical Physics Department, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | | | - Hamidreza SalighehRad
- Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Imaging, Tehran University of Medical Sciences, Tehran, Iran.
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
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Julià-Sapé M, Griffiths JR, Tate AR, Howe FA, Acosta D, Postma G, Underwood J, Majós C, Arús C. Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes. NMR IN BIOMEDICINE 2015; 28:1772-1787. [PMID: 26768492 DOI: 10.1002/nbm.3439] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 07/15/2015] [Accepted: 10/01/2015] [Indexed: 06/05/2023]
Abstract
The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
| | | | - A Rosemary Tate
- School of Informatics, University of Sussex, Falmer, Brighton, UK
| | - Franklyn A Howe
- Cardiovascular and Cell Sciences Research Institute, St George's, University of London, London, UK
| | - Dionisio Acosta
- CHIME, University College London, The Farr Institute of Health Informatics Research, London, UK
| | - Geert Postma
- Radboud University Nijmegen, Institute for Molecules and Materials, Analytical Chemistry, Nijmegen, The Netherlands
| | | | - Carles Majós
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Institut de Diagnòstic per la Imatge (IDI), CSU de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
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Sáez C, Martí-Bonmatí L, Alberich-Bayarri A, Robles M, García-Gómez JM. Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV ¹H MRS: evaluation as an additional information procedure for novice radiologists. Comput Biol Med 2013; 45:26-33. [PMID: 24480160 DOI: 10.1016/j.compbiomed.2013.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 11/13/2013] [Accepted: 11/18/2013] [Indexed: 11/28/2022]
Abstract
The results of a randomized pilot study and qualitative evaluation of the clinical decision support system Curiam BT are reported. We evaluated the system's feasibility and potential value as a radiological information procedure complementary to magnetic resonance (MR) imaging to assist novice radiologists in diagnosing brain tumours using MR spectroscopy (1.5 and 3.0T). Fifty-five cases were analysed at three hospitals according to four non-exclusive diagnostic questions. Our results show that Curiam BT improved the diagnostic accuracy in all the four questions. Additionally, we discuss the findings of the users' feedback about the system, and the further work to optimize it for real environments and to conduct a large clinical trial.
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Affiliation(s)
- Carlos Sáez
- Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Camino de Vera s/n, 46022 Valéncia, Spain.
| | - Luis Martí-Bonmatí
- Department of Radiology, Hospital Quirón Valencia, Valencia, Spain; Radiology, Department of Medicine, Universidad de Valencia, Spain
| | | | - Montserrat Robles
- Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Camino de Vera s/n, 46022 Valéncia, Spain
| | - Juan M García-Gómez
- Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Camino de Vera s/n, 46022 Valéncia, Spain
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Fuster-Garcia E, Tortajada S, Vicente J, Robles M, García-Gómez JM. Extracting MRS discriminant functional features of brain tumors. NMR IN BIOMEDICINE 2013; 26:578-592. [PMID: 23239454 DOI: 10.1002/nbm.2895] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Revised: 10/26/2012] [Accepted: 10/26/2012] [Indexed: 06/01/2023]
Abstract
The current challenge in automatic brain tumor classification based on MRS is the improvement of the robustness of the classification models that explicitly account for the probable breach of the independent and identically distributed conditions in the MRS data points. To contribute to this purpose, a new algorithm for the extraction of discriminant MRS features of brain tumors based on a functional approach is presented. Functional data analysis based on region segmentation (RSFDA) is based on the functional data analysis formalism using nonuniformly distributed B splines according to spectral regions that are highly correlated. An exhaustive characterization of the method is presented in this work using controlled and real scenarios. The performance of RSFDA was compared with other widely used feature extraction methods. In all simulated conditions, RSFDA was proven to be stable with respect to the number of variables selected and with respect to the classification performance against noise and baseline artifacts. Furthermore, with real multicenter datasets classification, RSFDA and peak integration (PI) obtained better performance than the other feature extraction methods used for comparison. Other advantages of the method proposed are its usefulness in selecting the optimal number of features for classification and its simplified functional representation of the spectra, which contributes to highlight the discriminative regions of the MR spectrum for each classification task.
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Affiliation(s)
- Elies Fuster-Garcia
- Biomedical Informatics Group (IBIME-ITACA), Universitat Politècnica de València, Valencia, Spain.
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Julià-Sapé M, Lurgi M, Mier M, Estanyol F, Rafael X, Candiota AP, Barceló A, García A, Martínez-Bisbal MC, Ferrer-Luna R, Moreno-Torres Á, Celda B, Arús C. Strategies for annotation and curation of translational databases: the eTUMOUR project. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2012. [PMID: 23180768 PMCID: PMC3504476 DOI: 10.1093/database/bas035] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The eTUMOUR (eT) multi-centre project gathered in vivo and ex vivo magnetic resonance (MR) data, as well as transcriptomic and clinical information from brain tumour patients, with the purpose of improving the diagnostic and prognostic evaluation of future patients. In order to carry this out, among other work, a database—the eTDB—was developed. In addition to complex permission rules and software and management quality control (QC), it was necessary to develop anonymization, processing and data visualization tools for the data uploaded. It was also necessary to develop sophisticated curation strategies that involved on one hand, dedicated fields for QC-generated meta-data and specialized queries and global permissions for senior curators and on the other, to establish a set of metrics to quantify its contents. The indispensable dataset (ID), completeness and pairedness indices were set. The database contains 1317 cases created as a result of the eT project and 304 from a previous project, INTERPRET. The number of cases fulfilling the ID was 656. Completeness and pairedness were heterogeneous, depending on the data type involved.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Facultat de Biociències Universitat Autònoma de Barcelona, Cerdanyola del Vallès 08193 Spain
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Julià-Sapé M, Coronel I, Majós C, Candiota AP, Serrallonga M, Cos M, Aguilera C, Acebes JJ, Griffiths JR, Arús C. Prospective diagnostic performance evaluation of single-voxel 1H MRS for typing and grading of brain tumours. NMR IN BIOMEDICINE 2012; 25:661-73. [PMID: 21954036 DOI: 10.1002/nbm.1782] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2011] [Revised: 07/12/2011] [Accepted: 07/14/2011] [Indexed: 05/31/2023]
Abstract
The purpose of this study was to evaluate whether single-voxel (1)H MRS could add useful information to conventional MRI in the preoperative characterisation of the type and grade of brain tumours. MRI and MRS examinations from a prospective cohort of 40 consecutive patients were analysed double blind by radiologists and spectroscopists before the histological diagnosis was known. The spectroscopists had only the MR spectra, whereas the radiologists had both the MR images and basic clinical details (age, sex and presenting symptoms). Then, the radiologists and spectroscopists exchanged their predictions and re-evaluated their initial opinions, taking into account the new evidence. Spectroscopists used four different systems of analysis for (1)H MRS data, and the efficacy of each of these methods was also evaluated. Information extracted from (1)H MRS significantly improved the radiologists' MRI-based characterisation of grade IV tumours (glioblastomas, metastases, medulloblastomas and lymphomas) in the cohort [area under the curve (AUC) in the MRI re-evaluation 0.93 versus AUC in the MRI evaluation 0.85], and also of the less malignant glial tumours (AUC in the MRI re-evaluation 0.93 versus AUC in the MRI evaluation 0.81). One of the MRS analysis systems used, the INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance) decision support system, outperformed the others, as well as being better than the MRI evaluation for the characterisation of grade III astrocytomas. Thus, preoperative MRS data improve the radiologists' performance in diagnosing grade IV tumours and, for those of grade II-III, MRS data help them to recognise the glial lineage. Even in cases in which their diagnoses were not improved, the provision of MRS data to the radiologists had no negative influence on their predictions.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
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Raschke F, Fuster-Garcia E, Opstad KS, Howe FA. Classification of single-voxel 1H spectra of brain tumours using LCModel. NMR IN BIOMEDICINE 2012; 25:322-331. [PMID: 21796709 DOI: 10.1002/nbm.1753] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2010] [Revised: 05/03/2011] [Accepted: 05/05/2011] [Indexed: 05/31/2023]
Abstract
This study presents a novel method for the direct classification of (1)H single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS.
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Affiliation(s)
- F Raschke
- Division of Clinical Sciences, St. George's University of London, London, UK.
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