Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization

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 1H-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.Peer ReviewedPostprint (author's final draft

Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization

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 1H-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

Conceptual Graphs Based Information Retrieval in HealthAgents

This paper focuses on the problem of representing, in a meaningful way, the knowledge involved in the HealthAgents project. Our work is motivated by the complexity of representing Electronic Health-care Records in a consistent manner. We present HADOM (HealthAgents Domain Ontology) which conceptualises the required HealthAgents information and propose describing the sources knowledge by the means of Conceptual Graphs (CGs). This allows to build upon the existing ontology permitting for modularity and °exibility. The novelty of our approach lies in the ease with which CGs can be placed above other formalisms and their potential for optimised querying and retrieval

Predicting the outcome of grade II glioma treated with temozolomide using proton magnetic resonance spectroscopy

International audienceBACKGROUND: This study was designed to evaluate proton magnetic resonance spectroscopy ((1)H-MRS) for monitoring the WHO grade II glioma (low-grade glioma (LGG)) treated with temozolomide (TMZ).METHODS: This prospective study included adult patients with progressive LGG that was confirmed by magnetic resonance imaging (MRI). Temozolomide was administered at every 28 days. Response to TMZ was evaluated by monthly MRI examinations that included MRI with volumetric calculations and (1)H-MRS for assessing Cho/Cr and Cho/NAA ratios. Univariate, multivariate and receiver-operating characteristic statistical analyses were performed on the results.RESULTS: A total of 21 LGGs from 31 patients were included in the study, and followed for at least n=14 months during treatment. A total of 18 (86%) patients experienced a decrease in tumour volume with a greater decrease of metabolic ratios. Subsequently, five (28%) of these tumours resumed growth despite the continuation of TMZ administration with an earlier increase of metabolic ratios of 2 months. Three (14%) patients did not show any volume or metabolic change. The evolutions of the metabolic ratios, mean(Cho/Cr)(n) and mean(Cho/NAA)(n), were significantly correlated over time (Spearman ρ=+0.95) and followed a logarithmic regression (P>0.001). The evolutions over time of metabolic ratios, mean(Cho/Cr)(n) and mean(Cho/NAA)(n), were significantly correlated with the evolution of the mean relative decrease of tumour volume, mean(ΔV(n)/V(o)), according to a linear regression (P<0.001) in the 'response/no relapse' patient group, and with the evolution of the mean tumour volume (meanV(n)), according to an exponential regression (P<0.001) in the 'response/relapse' patient group. The mean relative decrease of metabolic ratio, mean(Δ(Cho/Cr)(n)/(Cho/Cr)(o)), at n=3 months was predictive of tumour response over the 14 months of follow-up. The mean relative change between metabolic ratios, mean((Cho/NAA)(n)-(Cho/Cr)(n))/(Cho/NAA)(n), at n=4 months was predictive of tumour relapse with a significant cutoff of 0.046, a sensitivity of 60% and a specificity of 100% (P=0.004).CONCLUSIONS: The (1)H-MRS profile changes more widely and rapidly than tumour volume during the response and relapse phases, and represents an early predictive factor of outcome over 14 months of follow-up. Thus, (1)H-MRS may be a promising, non-invasive tool for predicting and monitoring the clinical response to TMZ

Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

Contains fulltext : 75361.pdf (publisher's version ) (Open Access)14 p

Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database.

Item does not contain fulltextOBJECT: The aim of this study was to estimate the accuracy of routine magnetic resonance (MR) imaging studies in the classification of brain tumors in terms of both cell type and grade of malignancy. METHODS: The authors retrospectively assessed the correlation between neuroimaging classifications and histopathological diagnoses by using multicenter database records from 393 patients with brain tumors. An ontology was devised to establish diagnostic agreement. Each tumor category was compared with the corresponding histopathological diagnoses by dichotomization. Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs, respectively), and the Wilson 95% confidence intervals (CI) for each were calculated. In routine reporting of MR imaging examinations, tumor types and grades were classified with a high specificity (85.2-100%); sensitivity varied, depending on the tumor type and grade, alone or in combination. The recognition of broad diagnostic categories (neuroepithelial or meningeal lesions) was highly sensitive, whereas when both detailed type and grade were considered, sensitivity diverged, being highest in low-grade meningioma (sensitivity 100%, 95% CI 96.2-100.0%) and lowest in high-grade meningioma (sensitivity 0.0%, 95% CI 0.0-65.8%) and low-grade oligodendroglioma (sensitivity 15%, 95% CI 5.2-36.0%). In neuroepithelial tumors, sensitivity was inversely related to the precision in reporting of grade and cellular origin; "glioma" was a frequent neuroimaging classification associated with higher sensitivity in the corresponding category. The PPVs varied among categories, in general being greater than their prevalence in this dataset. The NPV was high in all categories (69.8-100%). CONCLUSIONS: The PPVs and NPVs provided in this study may be used as estimates of posttest probabilities of diagnostic accuracy using MR imaging. This study targets the need for noninvasively increasing sensitivity in categorizing most brain tumor types while retaining high specificity, especially in the differentiation of high- and low-grade glial tumor classes

MRS quality assessment in a multicentre study on MRS-based classification of brain tumours.

Contains fulltext : 69096.pdf (publisher's version ) (Closed access)This paper reports on quality assessment of MRS in the European Union-funded multicentre project INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance; http://azizu.uab.es/INTERPRET), which has developed brain tumour classification software using in vivo proton MR spectra. The quality assessment consisted of both MR system quality assurance (SQA) and quality control (QC) of spectral data acquired from patients and healthy volunteers. The system performance of the MR spectrometers at all participating centres was checked bimonthly by a short measurement protocol using a specially designed INTERPRET phantom. In addition, a more extended SQA protocol was performed yearly and after each hardware or software upgrade. To compare the system performance for in vivo measurements, each centre acquired MR spectra from the brain of five healthy volunteers. All MR systems fulfilled generally accepted minimal system performance for brain MRS during the entire data acquisition period. The QC procedure of the MR spectra in the database comprised automatic determination of the signal-to-noise ratio (SNR) in a water-suppressed spectrum and of the line width of the water resonance (water band width, WBW) in the corresponding non-suppressed spectrum. Values of SNR > 10 and WBW < 8 Hz at 1.5 T were determined empirically as conservative threshold levels required for spectra to be of acceptable quality. These thresholds only hold for SNR and WBW values using the definitions and data processing described in this article. A final QC check consisted of visual inspection of each clinically validated water-suppressed metabolite spectrum by two, or, in the case of disagreement, three, experienced MR spectroscopists, to detect artefacts such as large baseline distortions, exceptionally broadened metabolite peaks, insufficient removal of the water line, large phase errors, and signals originating from outside the voxel. In the end, 10% of 889 spectra with completed spectroscopic judgement were discarded