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Todate,multipleadvancedmagneticresonanceimaging(MRI)methodsbeyondconventionalqualitativestructuralimagingforthediagnosis,prognosis,andtreatmentfollow-upofgliomahavedemonstratedtheirutilityforclinicalstudies.However,thesemethodsoftenrelyoncomplexoff-scannerprocessingtoyieldthemostinformationandtoextractquantitativebiomarkers,limitingtheirpracticaluseforstudies,aswellastheirclinicaltranslation.Whilecommunity-drivensoftwaresolutionsexistfortheseadvancedMRImethods,manyaspiringclinicalresearchersfacechallengesinacquiringthenecessaryknowledgetoeffectivelyapplythesetools.Thisguide,aninitiativeoftheGliomaMRimaging2.0network(GliMR),aimstoprovideanoverviewofexistingsolutions,communities,andrepositorieswiththeultimategoalofenablingstandardization,openscience,andreproduciblequantitativeimagingstudiesofgliomas.Yet,mostofthereviewedtoolsandapproachestoimagedataanalysesmayalsobeusedinthecontextofstudiesondiseasesotherthanglioma.

Todate,multipleadvancedmagneticresonanceimaging(MRI)methodsbeyondconventionalqualitativestructuralimagingforthediagnosis,prognosis,andtreatmentfollow-upofgliomahavedemonstratedtheirutilityforclinicalstudies.However,thesemethodsoftenrelyoncomplexoff-scannerprocessingtoyieldthemostinformationandtoextractquantitativebiomarkers,limitingtheirpracticaluseforstudies,aswellastheirclinicaltranslation.Whilecommunity-drivensoftwaresolutionsexistfortheseadvancedMRImethods,manyaspiringclinicalresearchersfacechallengesinacquiringthenecessaryknowledgetoeffectivelyapplythesetools.Thisguide,aninitiativeoftheGliomaMRimaging2.0network(GliMR),aimstoprovideanoverviewofexistingsolutions,communities,andrepositorieswiththeultimategoalofenablingstandardization,openscience,andreproduciblequantitativeimagingstudiesofgliomas.Yet,mostofthereviewedtoolsandapproachestoimagedataanalysesmayalsobeusedinthecontextofstudiesondiseasesotherthanglioma.

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF).

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). The second part of this review addresses magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications.

Item does not contain fulltext, AIMS: To evaluate the accuracy of single-voxel Magnetic Resonance Spectroscopy ((1)H MRS) as a non-invasive diagnostic aid for paediatric brain tumours in a multi-national study. Our hypotheses are (1) that automated classification based on (1)H MRS provides an accurate non-invasive diagnosis in multi-centre datasets and (2) using a protocol which increases the metabolite information improves the diagnostic accuracy. METHODS: Seventy-eight patients under 16 years old with histologically proven brain tumours from 10 international centres were investigated. Discrimination of 29 medulloblastomas, 11 ependymomas and 38 pilocytic astrocytomas (PILOAs) was evaluated. Single-voxel MRS was undertaken prior to diagnosis (1.5 T Point-Resolved Spectroscopy (PRESS), Proton Brain Exam (PROBE) or Stimulated Echo Acquisition Mode (STEAM), echo time (TE) 20-32 ms and 135-136 ms). MRS data were processed using two strategies, determination of metabolite concentrations using TARQUIN software and automatic feature extraction with Peak Integration (PI). Linear Discriminant Analysis (LDA) was applied to this data to produce diagnostic classifiers. An evaluation of the diagnostic accuracy was performed based on resampling to measure the Balanced Accuracy Rate (BAR). RESULTS: The accuracy of the diagnostic classifiers for discriminating the three tumour types was found to be high (BAR 0.98) when a combination of TE was used. The combination of both TEs significantly improved the classification performance (p<0.01, Tukey's test) compared with the use of one TE alone. Other tumour types were classified accurately as glial or primitive neuroectodermal (BAR 1.00). CONCLUSION: (1)H MRS has excellent accuracy for the non-invasive diagnosis of common childhood brain tumours particularly if the metabolite information is maximised and should become part of routine clinical assessment for these children.

Item does not contain fulltext, AIMS: To evaluate the accuracy of single-voxel Magnetic Resonance Spectroscopy ((1)H MRS) as a non-invasive diagnostic aid for paediatric brain tumours in a multi-national study. Our hypotheses are (1) that automated classification based on (1)H MRS provides an accurate non-invasive diagnosis in multi-centre datasets and (2) using a protocol which increases the metabolite information improves the diagnostic accuracy. METHODS: Seventy-eight patients under 16 years old with histologically proven brain tumours from 10 international centres were investigated. Discrimination of 29 medulloblastomas, 11 ependymomas and 38 pilocytic astrocytomas (PILOAs) was evaluated. Single-voxel MRS was undertaken prior to diagnosis (1.5 T Point-Resolved Spectroscopy (PRESS), Proton Brain Exam (PROBE) or Stimulated Echo Acquisition Mode (STEAM), echo time (TE) 20-32 ms and 135-136 ms). MRS data were processed using two strategies, determination of metabolite concentrations using TARQUIN software and automatic feature extraction with Peak Integration (PI). Linear Discriminant Analysis (LDA) was applied to this data to produce diagnostic classifiers. An evaluation of the diagnostic accuracy was performed based on resampling to measure the Balanced Accuracy Rate (BAR). RESULTS: The accuracy of the diagnostic classifiers for discriminating the three tumour types was found to be high (BAR 0.98) when a combination of TE was used. The combination of both TEs significantly improved the classification performance (p<0.01, Tukey's test) compared with the use of one TE alone. Other tumour types were classified accurately as glial or primitive neuroectodermal (BAR 1.00). CONCLUSION: (1)H MRS has excellent accuracy for the non-invasive diagnosis of common childhood brain tumours particularly if the metabolite information is maximised and should become part of routine clinical assessment for these children.

Item does not contain fulltext, OBJECT: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. MATERIALS AND METHODS: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. RESULTS: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 +/- 0.03 for 1.5T and 0.88 +/- 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. CONCLUSION: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T (1)H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.

Item does not contain fulltext, In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.

Item does not contain fulltext, OBJECT: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. MATERIALS AND METHODS: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. RESULTS: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 +/- 0.03 for 1.5T and 0.88 +/- 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. CONCLUSION: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T (1)H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.

Item does not contain fulltext, In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.

Item does not contain fulltext, In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce.

Item does not contain fulltext, OBJECT: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. MATERIALS AND METHODS: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. RESULTS: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 +/- 0.03 for 1.5T and 0.88 +/- 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. CONCLUSION: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T (1)H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.

[EN] Finite Elements Method is a well established mathematical technique for solving Partial Derivative Differential Equations. In this article we present an example of application of Finite Elements Method: the harmonic resonance modes of a guitar obtained from the resolution of the eigenvalues equation via the Finite Elements Method. Graphical output and direct comparison with experiments facilitates the understanding of the method and an extra motivation, establishing a link between Mathematics and the resolution of real engineering problems than is specially necessary in the teaching of Mathematics in the engineering schools., [CA] Els elements finits actualment constitueixen una eina fonamental per a resolució d’equacions diferencials en contextos reals. En aquest article presentem un bon exemple per al mètode: l’obtenció dels modes de ressonància sobre la tapa harmònica d’una guitarra, a partir de la resolució d’una equació d’autovalors amb el mètode dels elements finits. Per la facilitat de interpretació i contrastació experimental dels resultats obtinguts, pot oferir a mès una gratificació i motivació addicional, molt necessària en tot procès d’aprenentatge.

[EN] Finite Elements Method is a well established mathematical technique for solving Partial Derivative Differential Equations. In this article we present an example of application of Finite Elements Method: the harmonic resonance modes of a guitar obtained from the resolution of the eigenvalues equation via the Finite Elements Method. Graphical output and direct comparison with experiments facilitates the understanding of the method and an extra motivation, establishing a link between Mathematics and the resolution of real engineering problems than is specially necessary in the teaching of Mathematics in the engineering schools., [CA] Els elements finits actualment constitueixen una eina fonamental per a resolució d’equacions diferencials en contextos reals. En aquest article presentem un bon exemple per al mètode: l’obtenció dels modes de ressonància sobre la tapa harmònica d’una guitarra, a partir de la resolució d’una equació d’autovalors amb el mètode dels elements finits. Per la facilitat de interpretació i contrastació experimental dels resultats obtinguts, pot oferir a mès una gratificació i motivació addicional, molt necessària en tot procès d’aprenentatge.

[EN] Finite Elements Method is a well established mathematical technique for solving Partial Derivative Differential Equations. In this article we present an example of application of Finite Elements Method: the harmonic resonance modes of a guitar obtained from the resolution of the eigenvalues equation via the Finite Elements Method. Graphical output and direct comparison with experiments facilitates the understanding of the method and an extra motivation, establishing a link between Mathematics and the resolution of real engineering problems than is specially necessary in the teaching of Mathematics in the engineering schools., [CA] Els elements finits actualment constitueixen una eina fonamental per a resolució d’equacions diferencials en contextos reals. En aquest article presentem un bon exemple per al mètode: l’obtenció dels modes de ressonància sobre la tapa harmònica d’una guitarra, a partir de la resolució d’una equació d’autovalors amb el mètode dels elements finits. Per la facilitat de interpretació i contrastació experimental dels resultats obtinguts, pot oferir a mès una gratificació i motivació addicional, molt necessària en tot procès d’aprenentatge.

[EN] Finite Elements Method is a well established mathematical technique for solving Partial Derivative Differential Equations. In this article we present an example of application of Finite Elements Method: the harmonic resonance modes of a guitar obtained from the resolution of the eigenvalues equation via the Finite Elements Method. Graphical output and direct comparison with experiments facilitates the understanding of the method and an extra motivation, establishing a link between Mathematics and the resolution of real engineering problems than is specially necessary in the teaching of Mathematics in the engineering schools., [CA] Els elements finits actualment constitueixen una eina fonamental per a resolució d’equacions diferencials en contextos reals. En aquest article presentem un bon exemple per al mètode: l’obtenció dels modes de ressonància sobre la tapa harmònica d’una guitarra, a partir de la resolució d’una equació d’autovalors amb el mètode dels elements finits. Per la facilitat de interpretació i contrastació experimental dels resultats obtinguts, pot oferir a mès una gratificació i motivació addicional, molt necessària en tot procès d’aprenentatge.

[EN] Finite Elements Method is a well established mathematical technique for solving Partial Derivative Differential Equations. In this article we present an example of application of Finite Elements Method: the harmonic resonance modes of a guitar obtained from the resolution of the eigenvalues equation via the Finite Elements Method. Graphical output and direct comparison with experiments facilitates the understanding of the method and an extra motivation, establishing a link between Mathematics and the resolution of real engineering problems than is specially necessary in the teaching of Mathematics in the engineering schools., [CA] Els elements finits actualment constitueixen una eina fonamental per a resolució d’equacions diferencials en contextos reals. En aquest article presentem un bon exemple per al mètode: l’obtenció dels modes de ressonància sobre la tapa harmònica d’una guitarra, a partir de la resolució d’una equació d’autovalors amb el mètode dels elements finits. Per la facilitat de interpretació i contrastació experimental dels resultats obtinguts, pot oferir a mès una gratificació i motivació addicional, molt necessària en tot procès d’aprenentatge.