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

Non-invasive grading of astrocytic tumours from the relative contents of myo-inositol and glycine measured by in vivo MRS

Altres ajuts: INTERPRET (EU-IST1999-10310). This work was also partially funded by the Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, which is an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU FEDER funds.MRI and MRS are established methodologies for evaluating intracranial lesions. One MR spectral feature suggested for in vivo grading of astrocytic tumours is the apparent myo-Inositol (mI) intensity (ca 3.55ppm) at short echo times, although glycine (gly) may also contribute in vivo to this resonance. The purpose of this study was to quantitatively evaluate the mI + gly contribution to the recorded spectral pattern in vivo and correlate it with in vitro data obtained from perchloric acid extraction of tumour biopsies. Patient spectra (n = 95) at 1.5T at short (20-31 ms) and long (135-136 ms) echo times were obtained from the INTERPRET MRS database (http://gabrmn.uab.es/interpretvalidateddb/). Phantom spectra were acquired with a comparable protocol. Spectra were automatically processed and the ratios of the (mI + gly) to Cr peak heights ((mI + gly)/Cr) calculated. Perchloric acid extracts of brain tumour biopsies were analysed by high-resolution NMR at 9.4T. The ratio (mI + gly)/Cr decreased significantly with astrocytic grade in vivo between low-grade astrocytoma (A2) and glioblastoma multiforme (GBM). In vitro results displayed a somewhat different tendency, with anaplastic astrocytomas having significantly higher (mI + gly)/Cr than A2 and GBM. The discrepancy between in vivo and in vitro data suggests that the NMR visibility of glycine in glial brain tumours is restricted in vivo

Imaging of skull vault tumors in adults

The skull vault, formed by the flat bones of the skull, has a limited spectrum of disease that lies between the fields of neuro- and musculoskeletal radiology. Its unique abnormalities, as well as other ubiquitous ones, present particular features in this location. Moreover, some benign entities in this region may mimic malignancy if analyzed using classical bone-tumor criteria, and proper patient management requires being familiar with these presentations. This article is structured as a practical review offering a systematic diagnostic approach to focal calvarial lesions, broadly organized into four categories: (1) pseudolesions: arachnoid granulations, meningo-/encephaloceles, vascular canals, frontal hyperostosis, parietal thinning, parietal foramina, and sinus pericrani; (2) lytic: fibrous dysplasia, epidermal inclusion and dermoid cysts, eosinophilic granuloma, hemangioma, aneurysmal bone cyst, giant cell tumor, metastasis, and myeloma; (3) sclerotic: osteomas, osteosarcoma, and metastasis; (4) transdiploic: meningioma, hemangiopericytoma, lymphoma, and metastasis, along with other less common entities. Tips on the potential usefulness of functional imaging techniques such as MR dynamic susceptibility (T2*) perfusion, MR spectroscopy, diffusion-weighted imaging, and PET imaging are provided

Development of robust discriminant equations for assessing subtypes of glioblastoma biopsies

In the preceding decade, various studies on glioblastoma (Gb) demonstrated that signatures obtained from gene expression microarrays correlate better with survival than with histopathological classification. However, there is not a universal consensus formula to predict patient survival. We developed a gene signature using the expression profile of 47 Gbs through an unsupervised procedure and two groups were obtained. Subsequent to a training procedure through leave-one-out cross-validation, we fitted a discriminant (linear discriminant analysis (LDA)) equation using the four most discriminant probesets. This was repeated for two other published signatures and the performance of LDA equations was evaluated on an independent test set, which contained status of IDH1 mutation, EGFR amplification, MGMT methylation and gene VEGF expression, among other clinical and molecular information. The unsupervised local signature was composed of 69 probesets and clearly defined two Gb groups, which would agree with primary and secondary Gbs. This hypothesis was confirmed by predicting cases from the independent data set using the equations developed by us. The high survival group predicted by equations based on our local and one of the published signatures contained a significantly higher percentage of cases displaying IDH1 mutation and non-amplification of EGFR. In contrast, only the equation based on the published signature showed in the poor survival group a significant high percentage of cases displaying a hypothesised methylation of MGMT gene promoter and overexpression of gene VEGF. We have produced a robust equation to confidently discriminate Gb subtypes based in the normalised expression level of only four genes

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

Abstract Background Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with any new case from any MR scanner manufacturer and 2nd, improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested. Conclusions The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses.</p

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

Background: Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results: This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1st, can be easily used with acted to help in the categorisation of MR Spectra from abnormal brain masses

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses

Background: Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored. Results: This work presents the INTERPRET DSS version 3.0, analysing the improvements made from its first release in 2002. Version 3.0 is aimed to be a program that 1(st), can be easily used with any new case from any MR scanner manufacturer and 2(nd), improves the initial analysis capabilities of the first version. The main improvements are an embedded database, user accounts, more diagnostic discrimination capabilities and the possibility to analyse data acquired under additional data acquisition conditions. Other improvements include a customisable graphical user interface (GUI). Most diagnostic problems included have been addressed through a pattern-recognition based approach, in which classifiers based on linear discriminant analysis (LDA) were trained and tested. Conclusions: The INTERPRET DSS 3.0 allows radiologists, medical physicists, biochemists or, generally speaking, any person with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1.5 T, and to analyse them. The system is expected to help in the categorisation of MR Spectra from abnormal brain masses