Prospective multicentre evaluation and refinement of an analysis tool for magnetic resonance spectroscopy of childhood cerebellar tumours

AbstractBackgroundA tool for diagnosing childhood cerebellar tumours using magnetic resonance (MR) spectroscopy peak height measurement has been developed based on retrospective analysis of single-centre data.ObjectiveTo determine the diagnostic accuracy of the peak height measurement tool in a multicentre prospective study, and optimise it by adding new prospective data to the original dataset.Materials and methodsMagnetic resonance imaging (MRI) and single-voxel MR spectroscopy were performed on children with cerebellar tumours at three centres. Spectra were processed using standard scanner software and peak heights for N-acetyl aspartate, creatine, total choline and myo-inositol were measured. The original diagnostic tool was used to classify 26 new tumours as pilocytic astrocytoma, medulloblastoma or ependymoma. These spectra were subsequently combined with the original dataset to develop an optimised scheme from 53 tumours in total.ResultsOf the pilocytic astrocytomas, medulloblastomas and ependymomas, 65.4% were correctly assigned using the original tool. An optimized scheme was produced from the combined dataset correctly assigning 90.6%. Rare tumour types showed distinctive MR spectroscopy features.ConclusionThe original diagnostic tool gave modest accuracy when tested prospectively on multicentre data. Increasing the dataset provided a diagnostic tool based on MR spectroscopy peak height measurement with high levels of accuracy for multicentre data

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

1H‐magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single‐voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation

Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours

Background: Brain tumours cause the highest mortality and morbidity rate of all childhood tumour groups and new methods are required to improve clinical management. 1H magnetic resonance spectroscopy (MRS) allows non-invasive concentration measurements of small molecules present in tumour tissue, providing clinically useful imaging biomarkers. The primary aim of this study was to investigate whether MRS detectable molecules can predict the survival of paediatric brain tumour patients. Patients and methods: Short echo time (30 ms) single voxel 1H MRS was performed on children attending Birmingham Children’s Hospital with a suspected brain tumour and 115 patients were included in the survival analysis. Patients were followed-up for a median period of 35 months and Cox-Regression was used to establish the prognostic value of individual MRS detectable molecules. A multivariate model of survival was also investigated to improve prognostic power. Results: Lipids and scyllo-inositol predicted poor survival whilst glutamine and N-acetyl aspartate predicted improved survival (p < 0.05). A multivariate model of survival based on three MRS biomarkers predicted survival with a similar accuracy to histologic grading (p < 5e–5). A negative correlation between lipids and glutamine was found, suggesting a functional link between these molecules. Conclusions: MRS detectable biomolecules have been identified that predict survival of paediatric brain tumour patients across a range of tumour types. The evaluation of these biomarkers in large prospective studies of specific tumour types should be undertaken. The correlation between lipids and glutamine provides new insight into paediatric brain tumour metabolism that may present novel targets for therapy

The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy

Magnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.

INTRODUCTION Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma. METHODS A retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction. RESULTS The optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2 ± 1.7 and C-index of 90.0 ± 0.3. CONCLUSIONS VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI

A comparison between simulated and experimental basis sets for assessing short-TE in vivo1H MRS data at 1.5 T

A number of algorithms designed to determine metabolite concentrations from in vivo1H MRS require a collection of single metabolite spectra, known as a basis set, which can be obtained experimentally or by simulation. It has been assumed that basis sets can be used interchangeably, but no systematic study has investigated the effects of small variations in basis functions on the metabolite values obtained. The aim of this study was to compare the results of simulated with experimental basis sets when used to fit short-TE 1H MRS data of variable quality at 1.5 T. Two hundred and twelve paediatric brain tumour spectra were included in the analysis, and each was analysed twice with LCModel™ using a simulated and experimental basis set. To determine the influence of data quality on quantification, each spectrum was assessed and 152 were classified as being of ‘good’ quality. Bland–Altman statistics were used to measure the agreement between the two basis sets for all available spectra and only ‘good’-quality spectra. Monte-Carlo simulations were performed to investigate the influence of minor shifts in metabolite frequencies on metabolite concentration estimates. All metabolites showed good agreement between the two basis sets, and the average metabolite limits of agreement were approximately ±3.84 mm for all available data and ±0.99 mm for good-quality data. Errors obtained from the Monte-Carlo analysis were found to be more accurate than the Cramer–Rao lower bounds (CRLB) for 12 of 15 metabolites when metabolite frequency shifting was considered. For the majority of purposes, a level of agreement of ±0.99 mm between simulated and experimental basis sets is sufficiently small for them to be used interchangeably. Multiple analyses using slightly modified basis sets may be useful in estimating fitting errors, which are not predicted by CRLBs

Short echo time single voxel 1H magnetic resonance spectroscopy in the diagnosis and characterisation of pineal tumours in children

Background: Magnetic resonance spectroscopy (MRS) has been successful in characterising a range of brain tumours and is a useful aid to non-invasive diagnosis. The pineal region poses considerable surgical challenges and a major surgical resection is not required in the management of all tumours. Improved non-invasive assessment of pineal region tumours would be of considerable benefit. Methods: Single voxel MRS (TE 30 ms, TR 1500, 1.5 T) was performed on 15 pineal tumours: 5 germinomas, 1 non-germinomatous secreting germ cell tumour (GCT), 2 teratomas, 5 pineoblastomas, 1 pineal parenchymal tumour (PPT) of intermediate differentiation and 1 pineocytoma. Two germinomas outside the pineal gland were also studied. Metabolite, lipid and macromolecule concentrations were determined with LCModel™. Results: Germ cell tumours had significantly higher lipid and macromolecule concentrations than other tumours (t-test; P < 0.05). The teratomas had significantly lower total choline and creatine levels than germinomas (z test; P < 0.05). Taurine was convincingly detected in germinomas as well as PPTs. Conclusions: Magnetic resonance spectroscopy is useful for characterising pineal region tumours, aiding the non-invasive diagnosis and giving additional biological insight

Magnetic resonance spectroscopy in the assessment of pilocytic astrocytomas

Background: Pilocytic astrocytomas (PA) are common childhood brain tumours whose management and prognosis vary widely depending on location. 1H magnetic resonance spectroscopy (MRS) measures biochemistry in vivo and shows promise for characterising brain tumours and aiding management. Methods: Single voxel MRS (1.5 Tesla, TE 30 ms, TR 1500 ms) was performed on 27 children with PAs. Cases were designated ‘progressors’ if tumour progression led to their clinical management plan being altered. Results: Prior to treatment, supratentorial tumours had significantly higher myo-inositol (p < 0.01, t-test) and glutamate plus glutamine (p = 0.02, t-test) than cerebellar tumours. Optic pathway or thalamic tumours that progressed had a significantly (p = 0.04, t-test) lower myo-inositol at initial MRS than those with stable disease. Myo-inositol levels decreased significantly in progressors between the initial and subsequent MRS (p = 0.03, paired t-test). Changes in myo-inositol occurred before clinical and radiological progression. Conclusions: MRS identifies differences with anatomical location in PAs and yields potential non-invasive biomarkers of prognosis