In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

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Microdialysis enables measurement of the chemistry of the cerebral extracellular fluid. This study's objective was to utilise microdialysis to monitor levels of glucose, lactate, pyruvate, glutamate and glycerol in patients following surgery for intrinsic brain tumours, and to assess the concentration of growth factors, cytokines and other proteins involved in the pathogenesis of high-grade gliomas in vivo. Eight patients with suspected high-grade gliomas were studied. Seven of these underwent resection with one microdialysis catheter placed at the tumour resection margin and, in six of these seven cases, a second microdialysis catheter in macroscopically normal peritumour tissue. The remaining glioma patient had an image-guided biopsy with a single catheter inserted stereotactically at the tumour margin. Histology demonstrated WHO IV glioblastoma in five cases, WHO III anaplastic astrocytoma in two cases, and one cerebral lymphoma. In the high-grade gliomas (WHO IV and III), tumour margin microdialysates consistently showed significantly lower glucose, higher lactate/pyruvate (L/P) ratio, higher glutamate and higher glycerol, relative to peritumour microdialysates (P < 0.05). These results indicate that malignant glioma margin tissue is metabolically extremely active. There was great variability in the microdialysate concentrations of growth factors (TGFalpha, EGF, VEGF), cytokines (IL-1alpha, IL-1beta, IL-1ra, IL-6, IL-8), matrix metalloproteinases (MMP-2, MMP-9) and their endogenous inhibitors (TIMP-1, TIMP-2). Notably, microdialysates from the glioma resection margin demonstrated significantly higher IL-8 concentration and higher MMP-2/TIMP-1 ratio when compared to peritumour microdialysates (P < 0.05), suggesting an environment favouring invasion and angiogenesis at the tumour margin. Microdialysis is a promising technique to study in vivo glioma metabolism, and may assist in the development of new therapies.

BackgroundGenetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.MethodsWe recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.ResultsWe collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).ConclusionMulti-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Background:Acquiring clinically annotated, spatially stratified tissue samples from human glioblastoma (GBM) is compromised by haemorrhage, brain shift and subjective identification of ‘normal' brain. We tested the use of 5-aminolevulinic acid (5-ALA) fluorescence to objective tissue sampling and to derive tumour-initiating cells (TICs) from mass and margin.Methods:The 5-ALA was administered to 30 GBM patients. Samples were taken from the non-fluorescent necrotic core, fluorescent tumour mass and non-fluorescent margin. We compared the efficiency of isolating TICs from these areas in 5-ALA versus control patients. HRMAS 1H NMR was used to reveal metabolic alterations due to 5-ALA. We then characterised TICs for self-renewal in vitro and tumorigenicity in vivo.Results:The derivation of TICs was not compromised by 5-ALA and the metabolic profile was similar between tumours from 5-ALA patients and controls. The TICs from the fluorescent mass were self-renewing in vitro and tumour-forming in vivo, whereas TICs from non-fluorescent margin did not self-renew in vitro but did form tumours in vivo.Conclusion:Our data show that 5-ALA does not compromise the derivation of TICs. It also reveals that the margin contains TICs, which are phenotypically different from those isolated from the corresponding mass.

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Glioblastoma, the most common and aggressive adult brain tumor, is characterized by extreme phenotypic diversity and treatment failure. Through fluorescence-guided resection, we identified fluorescent tissue in the sub-ependymal zone (SEZ) of patients with glioblastoma. Histologic analysis and genomic characterization revealed that the SEZ harbors malignant cells with tumor-initiating capacity, analogous to cells isolated from the fluorescent tumor mass (T). We observed resistance to supramaximal chemotherapy doses along with differential patterns of drug response between T and SEZ in the same tumor. Our results reveal novel insights into glioblastoma growth dynamics, with implications for understanding and limiting treatment resistance. Cancer Res; 75(1); 194-202. Ó2014 AACR.