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Hernández-Villegas Y, Ortega-Martorell S, Arús C, Vellido A, Julià-Sapé M. Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization. NMR IN BIOMEDICINE 2022; 35:e4193. [PMID: 31793715 DOI: 10.1002/nbm.4193] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 08/04/2019] [Accepted: 08/27/2019] [Indexed: 06/10/2023]
Abstract
Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert-based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition-based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non-Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1 H-MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach.
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Affiliation(s)
- Yanisleydis Hernández-Villegas
- Departamento de Bioquímica y Biología Molecular, Universidad Autónoma de Barcelona (UAB), Spain
- Centro de Investigación Biomédica en Red (CIBER), Spain
- Instituto de Biotecnología y de Biomedicina (IBB), Universidad Autónoma de Barcelona (UAB), Spain
| | | | - Carles Arús
- Departamento de Bioquímica y Biología Molecular, Universidad Autónoma de Barcelona (UAB), Spain
- Centro de Investigación Biomédica en Red (CIBER), Spain
- Instituto de Biotecnología y de Biomedicina (IBB), Universidad Autónoma de Barcelona (UAB), Spain
| | - Alfredo Vellido
- Centro de Investigación Biomédica en Red (CIBER), Spain
- SOCO research group at Intelligent Data Science and Artificial Intelligence Research Center (IDEAI-UPC), Universitat Politècnica de Catalunya-BarcelonaTech, Spain
| | - Margarida Julià-Sapé
- Departamento de Bioquímica y Biología Molecular, Universidad Autónoma de Barcelona (UAB), Spain
- Centro de Investigación Biomédica en Red (CIBER), Spain
- Instituto de Biotecnología y de Biomedicina (IBB), Universidad Autónoma de Barcelona (UAB), Spain
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Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04051-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Kyathanahally SP, Mocioiu V, Pedrosa de Barros N, Slotboom J, Wright AJ, Julià-Sapé M, Arús C, Kreis R. Quality of clinical brain tumor MR spectra judged by humans and machine learning tools. Magn Reson Med 2017; 79:2500-2510. [PMID: 28994492 DOI: 10.1002/mrm.26948] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 08/14/2017] [Accepted: 09/06/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. METHODS A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. RESULTS AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. CONCLUSION Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Sreenath P Kyathanahally
- Departments of Radiology and Clinical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Victor Mocioiu
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
| | - Nuno Pedrosa de Barros
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.,DRNN, Institute of Diagnostic and Interventional Neuroradiology/SCAN, University Hospital Bern, Bern, Switzerland
| | - Johannes Slotboom
- DRNN, Institute of Diagnostic and Interventional Neuroradiology/SCAN, University Hospital Bern, Bern, Switzerland
| | - Alan J Wright
- CRUK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - 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, Edifici Cs, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.,Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 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, Edifici Cs, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.,Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Roland Kreis
- Departments of Radiology and Clinical Research, University of Bern, Bern, Switzerland
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