Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

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.Funding from project MARESCAN (SAF2011-23870) from Ministerio de Economia y Competitividad in Spain. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. JRG acknowledges support from Cancer Research UK, the University of Cambridge and Hutchison Whampoa Ltd.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/nbm.343

Wykorzystanie wielowymiarowych technik analizy widm 1H MRS in vivo w różnicowaniu wrodzonych wad metabolizmu

The thesis is based on a series of publications A.1 - A.3. The primary aim was to assess the utility of 1H MRS in vivo technique and multivariate data analysis methods in diagnosis of inborn errors of metabolism in children. 1H MRS in vivo technique provides valuable insight into brain biochemistry. However, relatively low spectral resolution and sensitivity of the technique hamper differentiation of various disorders. Application of multivariate data analysis techniques making use of covariances or correlations between metabolites is expected to facilitate extraction of clinically useful spectral features. In publications A.1 and A.2 visualization of metabolic differences between rare inborn errors of metabolism and other neurological disorders commonly encountered in clinical practice was achieved using dimensionality reduction of 1H MRS data acquired from the regions of interest located in brain white matter. Both water scaled metabolite levels determined with LCModel software (publication A.1) and unresolved 1H MRS in vivo spectra normalized to the sum of low-molecular metabolites (publication A.2) were subjected to principal component analysis. The obtained results proved the usefulness of the method in differentiation of various white matter neurometabolic disiorders (van der Knaap disease, metachromatic leukodystrophy, globoidal leukodystrophy and Canavan disease) from other neurological disorders (cerebral palsy, global developmental delay and epileptic encephalopathy). As neurometabolic disorders individually are extremely rare, it was necessary to pool 1H MRS in vivo data acquired during a relatively long period of time. The publication A.3 was devoted to assessment of long-term MRI scanner reproducibility and application of unsupervised change point detection technique in the analysis of phantom metabolite levels time series. Although multivariate techniques of data analysis are increasingly used for detection of complex 1H MRS derived metabolic signatures in pathological conditions, multivariate analysis of regional heterogeneity of the normal human brain has not been paid attention so far. Thus, the secondary aim of this work was to determine metabolic coordinates of various brain regions in 1H MRS in vivo derived multivariate space. A.1 Polnik A (Skorupa A), Sokół M, Jamroz E, Paprocka J, Wicher M, Banasik T, Marszał E, Kiełtyka A, Konopka M. Contribution of 1H MRS to differential diagnosis of neurologic disorders in children. W: Some aspects of medical physics - in vivo and in vitro studies. Eds.: Z. Drzazga, K. Ślosarek, Polish Journal of Environmental Studies. Series of Monographs. Vol.1, 2010, str. 27-33. A.2. Skorupa A., Jamroz E, Paprocka J, Sokół M, Wicher M, Kiełtyka A. Bridging the gap between metabolic profile determination and visualization in neurometabolic disorders: a multivariate analysis of proton magnetic resonance in vivo spectra. J Chemometrics. 2013;27:76–90. A.3 Skorupa A, Wicher M, Banasik T, Jamroz E, Paprocka J, Kiełtyka A, Sokół M, Konopka M. Fourand- one-half years' experience in monitoring of reproducibility of an MR spectroscopy systemapplication of in vitro results to interpretation of in vivo data. J Appl Clin Med Phys. 2014;15(3):4754

A Prototype Decision Support System for MR Spectroscopy-Assisted Diagnosis of Brain Tumours

Our objective is to develop a decision support system that improves the accuracy of non-invasive brain tumour diagnosis and grading by enabling radiologists to use data from Magnetic Resonance Spectroscopy (MRS). The system, which uses pattern recognition techniques, is trained on a validated database of spectra and associated clinical information to provide automated classification of spectra from brain tumours. An innovative user-interface presents classification results as a two-dimensional overview plot in which points representing cases of different diseases form distinct clusters. Users can inspect any cases in these plots and compare them with the new, unknown spectrum. Hence, the overview plot can both communicate the classification of a case and help provide explanation for that classification in part by supporting human case-based reasoning. This paper describes the development of a prototype system implemented in JAVA