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INTRODUCTION: Perfusion is associated with grade and survival in children’s brain tumours. Dynamic susceptibility-contrast (DSC-) MRI measures perfusion non-invasively, estimating relative cerebral blood volume (rCBV). We previously showed significant differences between pre-treatment rCBV in low- and high-grade tumours in a multicentre study. Contrast agent leakage from tumour vessels during acquisition affects rCBV accuracy. A contrast agent pre-bolus can be given but this can be challenging in a clinical environment, introducing variability. Alternatively, a single bolus can be administered with leakage correction applied when processing the data. We investigated pre-treatment rCBV values in a multicentre study without pre-bolus administration. METHODS: Thirty-six patients underwent pre-treatment DSC-MRI scans at 2 centres on 4 different scanners. Protocols were variable. Pixel-by-pixel contrast agent concentration time courses were analysed. Maps of uncorrected (rCBVuncorr) and leakage-corrected rCBV (rCBVcorr) were produced. Whole-tumour regions-of-interest were defined and median whole-tumour DSC-MRI parameters calculated. Patients subsequently underwent surgery / biopsy. Tumours were classified and graded. RESULTS: Twelve tumours were classified as low-grade; 24 as high-grade. Median whole-tumour rCBVuncorr was significantly higher in high-grade tumours than in low-grade tumours (1.628 vs -0.167, p

Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

INTRODUCTION Magnetic Resonance Imaging (MRI) is routinely used in the assessment of children’s brain tumours. Reduced diffusion and increased perfusion on MRI are commonly associated with higher grade but there is a lack of quantitative data linking these parameters to survival. Machine learning is increasingly being used to develop diagnostic tools but its use in survival analysis is rare. In this study we combine quantitative parameters from diffusion and perfusion MRI with machine learning to develop a model of survival for paediatric brain tumours. METHOD: 69 children from 4 centres (Birmingham, Liverpool, Nottingham, Newcastle) underwent MRI with diffusion and perfusion (dynamic susceptibility contrast) at diagnosis. Images were processed to form ADC, cerebral blood volume (CBV) and vessel leakage correction (K2) parameter maps. Parameter mean, standard deviation and heterogeneity measures (skewness and kurtosis) were calculated from tumour and whole brain and used in iterative Bayesian survival analysis. The features selected were used for k-means clustering and differences in survival between clusters assessed by Kaplan-Meier and Cox-regression. RESULTS Bayesian analysis revealed the 5 top features determining survival to be tumour volume, ADC kurtosis, CBV mean, K2 mean and whole brain CBV mean. K-means clustering using these features showed two distinct clusters (high- and low-risk) which bore significantly different survival characteristics (Hazard Ratio = 5.6). DISCUSSION AND CONCLUSION Diffusion and perfusion MRI can be used to aid the prediction of survival in children’s brain tumours. Tumour perfusion played a particularly important role in predicting survival despite being less routinely measured than diffusion.

Studies in adults have shown that brain tumour perfusion correlates with grade. These studies are dominated by gliomas grade II to IV which are rare in children. The standard method, Dynamic Susceptibility Contrast MRI, provides estimates of relative cerebral blood volume (rCBV) but contrast agent leakage affects rCBV accuracy. The majority of perfusion studies have been conducted at single centres and variation in acquisition protocols makes the generalizability of results questionable. The aim of this study was to compare leakage-corrected rCBV with grade in paediatric brain tumours at multiple centres. Scans were analysed from 85 patients at 4 centres on 6 scanners prior to treatment. MRI protocols varied between centres. Histological diagnoses including grade were obtained. Whole-tumour median rCBV was significantly higher in the 45 high grade than the 40 low grade tumours (2.54 ± 1.63 ml/100ml vs 1.68 ± 1.36 ml/100ml, p=0.010). Low grade tumours, particularly pilocytic astrocytomas (grade I), displayed more contrast agent leakage consistent with their appearance on contrast enhanced images and required more leakage correction than high grade tumours. This finding differs from that in adults where contrast agent uptake is usually associated with higher grade. A cut-off of 1.70 ml/100ml for rCBV gave sensitivity and specificity of 76% and 65% respectively for discriminating grade. In summary, perfusion MRI can be used to help distinguish between low and high grade paediatric brain tumours. This finding is robust across multiple centres and acquisition protocols but correction should be made for leakage of contrast agent from the vessels.

Background: Malignant brain tumors in children generally have a very poor prognosis when they relapse and improvements are required in their management. It can be difficult to accurately diagnose abnormalities detected during tumor surveillance, and new techniques are required to aid this process. This study investigates how metabolite profiles measured noninvasively by 1H magnetic resonance spectroscopy (MRS) at relapse reflect those at diagnosis and may be used in this monitoring process. Methods: Single-voxel MRS (1.5 T, point-resolved spectroscopy, echo time 30 ms, repetition time 1500 ms was performed on 19 children with grades II–IV brain tumors during routine MRI scans prior to treatment for a suspected brain tumor and at suspected first relapse. MRS was analyzed using TARQUIN software to provide metabolite concentrations. Paired Student's t-tests were performed between metabolite profiles at diagnosis and at first relapse. Results: There was no significant difference (P > .05) in the level of any metabolite, lipid, or macromolecule from tumors prior to treatment and at first relapse. This was true for the whole group (n = 19), those with a local relapse (n = 12), and those with a distant relapse (n = 7). Lipids at 1.3 ppm were close to significance when comparing the level at diagnosis with that at distant first relapse (P = .07, 6.5 vs 12.9). In 5 cases the MRS indicative of tumor preceded a formal diagnosis of relapse. Conclusions: Tumor metabolite profiles, measured by MRS, do not change greatly from diagnosis to first relapse, and this can aid the confirmation of the presence of tumor.