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.

A B S T R A C T PurposeGlioblastoma is an incurable solid tumor characterized by increased expression of vascular endothelial growth factor (VEGF). We performed a phase II study of cediranib in patients with recurrent glioblastoma. MethodsCediranib, an oral pan-VEGF receptor tyrosine kinase inhibitor, was administered (45 mg/d) until progression or unacceptable toxicity to patients with recurrent glioblastoma. The primary end point was the proportion of patients alive and progression free at 6 months (APF6). We performed magnetic resonance imaging (MRI) and plasma and urinary biomarker evaluations at multiple time points. ResultsThirty-one patients with recurrent glioblastoma were accrued. APF6 after cediranib was 25.8%. Radiographic partial responses were observed by MRI in 17 (56.7%) of 30 evaluable patients using three-dimensional measurements and in eight (27%) of 30 evaluable patients using twodimensional measurements. For the 15 patients who entered the study taking corticosteroids, the dose was reduced (n ϭ 10) or discontinued (n ϭ 5). Toxicities were manageable. Grade 3/4 toxicities included hypertension (four of 31; 12.9%); diarrhea (two of 31; 6.4%); and fatigue (six of 31; 19.4%). Fifteen (48.4%) of 31 patients required at least one dose reduction and 15 patients required temporary drug interruptions due to toxicity. Drug interruptions were not associated with outcome. Changes in plasma placental growth factor, basic fibroblast growth factor, matrix metalloproteinase (MMP) -2, soluble VEGF receptor 1, stromal cell-derived factor-1␣, and soluble Tek/Tie2 receptor and in urinary MMP-9/neutrophil gelatinase-associated lipocalin activity after cediranib were associated with radiographic response or survival. ConclusionCediranib monotherapy for recurrent glioblastoma is associated with encouraging proportions of radiographic response, 6-month progression-free survival, and a steroid-sparing effect with manageable toxicity. We identified early changes in circulating molecules as potential biomarkers of response to cediranib. The efficacy of cediranib and the predictive value of these candidate biomarkers will be explored in prospective trials.

Mutations of arginine 132 (R132) in the enzyme isocitrate dehydrogenase-1 (IDH1) are present in up to 86% of grade II and III gliomas and secondary glioblastoma. R132 mutations in IDH1 result in excess production of the metabolite 2-hydroxyglutarate (2HG), which could be used as a biomarker for this subset of gliomas. Here, we use optimized spectral-editing and two-dimensional (2D) correlation magnetic resonance spectroscopy (MRS) methods to unambiguously detect 2HG non-invasively in glioma patients with IDH1 mutations. By comparison, fitting of conventional 1D MR spectra can provide false-positive readouts owing to spectral overlap of 2HG and chemically similar brain metabolites, such as glutamate and glutamine. 2HG has been found also by 2D high-resolution magic angle spinning MRS performed ex vivo on a separate set of glioma biopsy samples. 2HG detection by in vivo or ex vivo MRS enabled detailed molecular characterization of a clinically important subset of human gliomas. This has implications for diagnosis as well as monitoring of treatments targeting IDH mutations.

Significance This study demonstrates that antiangiogenic therapy increases tumor blood perfusion in a subset of newly diagnosed glioblastoma patients, and that it is these patients who survive longer when this expensive and potentially toxic therapy is combined with standard radiation and chemotherapy. This study provides fresh insights into the selection of glioblastoma patients most likely to benefit from antiangiogenic treatments.

Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data. Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming. With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively. We developed a deep learning technique to noninvasively predict genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. .

A recent joint meeting was held on January 30, 2014, with the US Food and Drug Administration (FDA), National Cancer Institute (NCI), clinical scientists, imaging experts, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocate groups to discuss imaging endpoints for clinical trials in glioblastoma. This workshop developed a set of priorities and action items including the creation of a standardized MRI protocol for multicenter studies. The current document outlines consensus recommendations for a standardized Brain Tumor Imaging Protocol (BTIP), along with the scientific and practical justifications for these recommendations, resulting from a series of discussions between various experts involved in aspects of neuro-oncology neuroimaging for clinical trials. The minimum recommended sequences include: (i) parameter-matched precontrast and postcontrast inversion recovery-prepared, isotropic 3D T1-weighted gradient-recalled echo; (ii) axial 2D T2-weighted turbo spin-echo acquired after contrast injection and before postcontrast 3D T1-weighted images to control timing of images after contrast administration; (iii) precontrast, axial 2D T2-weighted fluid-attenuated inversion recovery; and (iv) precontrast, axial 2D, 3-directional diffusion-weighted images. Recommended ranges of sequence parameters are provided for both 1.5 T and 3 T MR systems.

The serum of patients with subacute limbic and brain-stem dysfunction and testicular cancer contains antibodies against a protein found in normal brain and in testicular tumors. Detection of these antibodies supports the paraneoplastic origin of the neurologic disorder and could be of diagnostic importance.

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