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1 H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours., (© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Background and Purpose: Pediatric posterior fossa tumors often present with hydrocephalus; postoperatively, up to 25% of patients develop cerebellar mutism syndrome. Arterial spin-labeling is a noninvasive means of quantifying CBF and bolus arrival time. The aim of this study was to investigate how changes in perfusion metrics in children with posterior fossa tumors are modulated by cerebellar mutism syndrome and hydrocephalus requiring pre-resection CSF diversion., Materials and Methods: Forty-four patients were prospectively scanned at 3 time points (preoperatively, postoperatively, and at 3-month follow-up) with single- and multi-inflow time arterial spin-labeling sequences. Regional analyses of CBF and bolus arrival time were conducted using coregistered anatomic parcellations. ANOVA and multivariable, linear mixed-effects modeling analysis approaches were used. The study was registered at clinicaltrials.gov (NCT03471026)., Results: CBF increased after tumor resection and at follow-up scanning ( P = .045). Bolus arrival time decreased after tumor resection and at follow-up scanning ( P = .018). Bolus arrival time was prolonged ( P = .058) following the midline approach, compared with cerebellar hemispheric surgical approaches to posterior fossa tumors. Multivariable linear mixed-effects modeling showed that regional perfusion changes were more pronounced in the 6 children who presented with symptomatic obstructive hydrocephalus requiring pre-resection CSF diversion, with hydrocephalus lowering the baseline mean CBF by 20.5 (standard error, 6.27) mL/100g/min. Children diagnosed with cerebellar mutism syndrome (8/44, 18.2%) had significantly higher CBF at follow-up imaging than those who were not ( P = .040), but no differences in pre- or postoperative perfusion parameters were seen., Conclusions: Multi-inflow time arterial spin-labeling shows promise as a noninvasive tool to evaluate cerebral perfusion in the setting of pediatric obstructive hydrocephalus and demonstrates increased CBF following resolution of cerebellar mutism syndrome., (© 2022 by American Journal of Neuroradiology.)

Brain tumours are the most frequently occuring solid tumours affecting childhood, representing 27% of all cancers. The most common posterior fossa tumours are medulloblastoma, pilocytic astrocytoma and ependymoma. Texture Analysis (TA) of Magnetic Resonance Imaging (MRI) aims to represent pixel distributions, intensities and dependencies using mathematically defined features. Such features could potentially provide quantifiable information that is beyond the human vision capabilities, and hence be used to supplement qualitative assessments conducted by radiologists. The primary aim of this study was to carry out a multicentre investigation on the efficacy of 3D TA for diagnostic classification of childhood brain tumours, using conventional MRI images. The data used had been acquired at three different hospitals and consisted of pre-contrast T1 and T2-weighted MRI series, obtained from 121 children diagnosed with medulloblastoma, pilocytic astrocytoma and ependymoma. Using 3D textural features, based on first, second and higher order statistical methods, a support vector machine (SVM) classifier was trained and tested using the leave-one-out cross-validation (LOOCV) approach. An essential outcome of this study is that 3D TA demonstrated a good overall performance, when used on data acquired from a number of centres and using scanners made by different manufacturers and at different magnetic field strengths.

Background: The functional diffusion map (fDM) has been suggested as a tool for early detection of tumor treatment efficacy. We aim to study 3 factors that could act as potential confounders in the fDM: areas of necrosis, tumor grade, and change in tumor size., Methods: Thirty-four pediatric patients with brain tumors were enrolled in a retrospective study, approved by the local ethics committee, to examine the fDM. Tumors were selected to encompass a range of types and grades. A qualitative analysis was carried out to compare how fDM findings may be affected by each of the 3 confounders by comparing fDM findings to clinical image reports., Results: Results show that the fDM in areas of necrosis do not discriminate between treatment response and tumor progression. Furthermore, tumor grade alters the behavior of the fDM: a decrease in apparent diffusion coefficient (ADC) is a sign of tumor progression in high-grade tumors and treatment response in low-grade tumors. Our results also suggest using only tumor area overlap between the 2 time points analyzed for the fDM in tumors of varying size., Conclusions: Interpretation of fDM results needs to take into account the underlying biology of both tumor and healthy tissue. Careful interpretation of the results is required with due consideration to areas of necrosis, tumor grade, and change in tumor size.

The ability to assess brain tumor perfusion and abnormalities in the vascular structure in vivo could provide significant benefits in terms of lesion diagnosis and assessment of treatment response. Arterial spin labeling (ASL) has emerged as an increasingly viable methodology for non-invasive assessment of perfusion. Although kinetic models have been developed to describe perfusion in healthy tissue, the dynamic behaviour of the ASL signal in the brain tumor environment has not been extensively studied. We show here that dynamic ASL data acquired in brain tumors displays an increased level of 'biphasic' behaviour, compared to that seen in healthy tissue. A new two-stage model is presented which more accurately describes this behaviour, and provides measurements of perfusion, pre-capillary blood volume fraction and transit time, and capillary bolus arrival time. These biomarkers offer a novel contrast in the tumor and surrounding tissue, and provide a means for measuring tumor perfusion and vascular structural abnormalities in a fully non-invasive manner.

Despite current advances in neuro-imaging, the characterisation of paediatric brain tumours and neurological disorders is very challenging. Whilst Magnetic Resonance Imaging (MRI) provides images of superb clarity, it gives little information on how aggressive a tumour is. It is also very difficult to visually inspect any underlying textural patterns between tumours on MR images. This gives rise to the need for a quantitative means of analysing MR images for characterising tumours. In this work, we present a preliminary investigation into the effectiveness of texture analysis as a quantitative approach for classifying paediatric brain tumours.

Embryonal brain tumors constitute a large and important subgroup of pediatric brain tumors. Apparent diffusion coefficient (ADC) measures have been previously used in the analysis of these tumors. We investigated a newly described ADC-derived parameter, the apparent transient coefficient in tumor (ATCT), a measure of the gradient change of ADC from the peri-tumoral edema into the tumor core, to study whether ATCT correlates with survival outcome. Sixty-one patients with histologically proven embryonal brain tumors and who had diffusion-weighted imaging (DWI) as part of their clinical imaging were enrolled in a retrospective study correlating ADC measures with survival. Kaplan-Meier survival curves were constructed for extent of surgical resection, age <3 years at diagnosis, tumor type, and metastasis at presentation. A multivariate survival analysis was performed that took into consideration ATCT and variables found to be significant in the Kaplan-Meier analysis as covariates. Results from the multivariate analysis showed that ATCT was the only significant covariate (P < .001). Survival analysis using Kaplan-Meier curves, dividing the patients into 4 groups of increasing values of ATCT, showed that more negative values of ATCT were significantly associated with a poorer prognosis (P < .001). A statistically significant difference was observed for survival data with respect to the change in ADC from edema into the tumor volume. Results show that more negative ATCT values are significantly associated with a poorer survival among children with embryonal brain tumors, irrespective of tumor type, extent of resection, age <3 years at diagnosis, and metastasis at presentation.

Objective: To determine if histograms of apparent diffusion coefficients (ADC) can be used to differentiate paediatric brain tumours., Methods: Imaging of histologically confirmed tumours with pre-operative ADC maps were reviewed (54 cases, 32 male, mean age 6.1 years; range 0.1-15.8 years) comprising 6 groups. Whole tumour ADC histograms were calculated; normalised for volume. Stepwise logistic regression analysis was used to differentiate tumour types using histogram metrics, initially for all groups and then for specific subsets., Results: All 6 groups (5 dysembryoplastic neuroectodermal tumours, 22 primitive neuroectodermal tumours (PNET), 5 ependymomas, 7 choroid plexus papillomas, 4 atypical teratoid rhabdoid tumours (ATRT) and 9 juvenile pilocytic astrocytomas (JPA)) were compared. 74% (40/54) were correctly classified using logistic regression of ADC histogram parameters. In the analysis of posterior fossa tumours, 80% of ependymomas, 100% of astrocytomas and 94% of PNET-medulloblastoma were classified correctly. All PNETs were discriminated from ATRTs (22 PNET and 4 supratentorial ATRTs) (100%)., Conclusions: ADC histograms are useful in differentiating paediatric brain tumours, in particular, the common posterior fossa tumours of childhood. PNETs were differentiated from supratentorial ATRTs, in all cases, which has important implications in terms of clinical management. Key Points • MR based apparent diffusion coefficient histograms can help differentiate paediatric brain tumours • ADC histogram parameters correctly classified the great majority of posterior fossa tumours.

In a prospective study, patients with a radiologically proven brain tumour underwent diffusion tensor imaging (DTI) prior to definitive diagnosis and treatment. Twenty-eight patients with a histologically proven glioblastoma or metastasis were included in the study. Following the definition of regions of interest, DTI metrics [mean diffusivity (MD) and fractional anisotropy (FA)] were calculated for the tumour volume and the surrounding region of peritumoral oedema. These metrics were then subjected to logistic regression to investigate their ability to discriminate between glioblastomas and cerebral metastases. A cross-validation was performed to investigate the ability of the model to predict tumour. The logistic regression analysis correctly distinguished glioblastoma in 15 of 16 cases (93.8%) and metastasis in 11 of 12 cases (91.7%). Cross-validation resulted in the model correctly predicting 14 of 16 (87.5%) glioblastomas and 10 of 12 (83.3%) metastases studied. MD was significantly higher (p = 0.02) and FA was significantly lower (p = 0.04) within the oedema surrounding metastases than within the oedema around glioblastomas. MD was significantly higher (p = 0.02) within the tumour volume of the glioblastomas. Our results demonstrate that, when DTI metrics from the tumour volume and surrounding peritumoral oedema are studied in combination, glioblastoma can be reliably discriminated from cerebral metastases., (Copyright © 2010 John Wiley & Sons, Ltd.)

Gliomas are the most common primary brain tumors and the majority are highly malignant, with one of the worst prognoses for patients. Gliomas are characterized by invasive growth into normal brain tissue that makes complete surgical resection and accurate radiotherapy planning extremely difficult. We have performed independent component analysis of magnetic resonance spectroscopy imaging data from human gliomas to segment brain tissue into tumor core, tumor infiltration, and normal brain, with confirmation by diffusion tensor imaging analysis. Our data are consistent with previous studies that compared anomalies in isotropic and anisotropic diffusion images to determine regions of potential glioma infiltration. We show that coefficients of independent components can be used to create colored images for easy visual identification of regions of infiltrative tumor growth., ((c) 2009 Wiley-Liss, Inc.)

Conventional structural imaging provides limited information on tumor characterization and prognosis. Advances in neurosurgical techniques, radiotherapy planning and novel drug treatments for brain tumors have generated increasing need for reproducible, noninvasive, quantitative imaging biomarkers. This Review considers the role of physiological MRI and PET molecular imaging in understanding metabolic processes associated with tumor growth, blood flow and ultrastructure. We address the utility of various techniques in distinguishing between tumors and non-neoplastic processes, in tumor grading, in defining anatomical relationships between tumor and eloquent brain regions and in determining the biological substrates of treatment response. Much of the evidence is derived from limited case series in individual centers. Despite their 'added value', the effect of these techniques as an adjunct to structural imaging in clinical research and practice remains limited.

The objective was to study fibre orientation in the cerebral white matter of a patient with a brain tumour using diffusion tensor imaging (DTI). A patient with a mild left hemiparesis and a tumour in the right frontal lobe and 20 healthy volunteers were scanned with a DTI sequence. The scans were spatially normalised and the fibre orientation in the patient compared with the fibre orientation in normal controls. DTI disclosed a change of the orientation of fibres in the patient compared with normal controls. In the normal appearing white matter adjacent to the tumour fibres deviated from the normal superior inferior orientation in the corona radiata by about 30 degrees. This finding was consistent with a displacement by distant mass effect rather than a destruction of fibres, in agreement with the neurological examination. In conclusion, DTI demonstrated a deviation of fibres in normal appearing white matter adjacent to a tumour. The technique will improve understanding of the effects of structural abnormalities on fibres. This will assist the interpretation of clinical findings and functional imaging studies and guide neurosurgical interventions.