Correction to: Neuro-radiological characteristics of adult diffuse grade II and III insular gliomas classified according to WHO 2016.

In the initial, online publication, the authors' given names were captured as family names and vice versa. The names are correctly shown here. The original article has been corrected.

Neuro-radiological characteristics of adult diffuse grade II and III insular gliomas classified according to WHO 2016.

INTRODUCTION: The phenotypic heterogeneity of diffuse gliomas is still inconsistently explained by known molecular abnormalities. Here, we report the molecular and radiological features of diffuse grade WHO II and III gliomas involving the insula and its potential impact on prognosis. METHODS: Clinical, pathological, molecular and neuro-radiological features of 43 consecutive patients who underwent a surgical resection between 2006 and 2013 for a grade II and III gliomas involving the insula was retrospectively analyzed. RESULTS: Median age was 44.4 years. Eight patients had oligodendrogliomas, IDH mutant (IDHmut) and 1p/19q-codeleted (6 grade II, 2 grade III). Twenty-eight patients had diffuse astrocytomas, IDHmut (22 grade II and 6 grade III) and seven patients had grade II diffuse astrocytomas, IDHwt (A-IDHwt). Vimentin staining was exclusively recorded in tumor cells from A-IDHwt (p = 0.001). Mean cerebral blood volume (CBV) (p = 0.018), maximal value of CBV (p = 0.017) and ratio of the corrected CBV (p = 0.022) were lower for A-IDHwt. Volumetric segmentation of ADC allowed the identification of the tumor cores, which were smaller in A-IDHwt (p < 0.001). The tumor occurrences of A-IDHwt were exclusively located into the temporo-insular region. Median progression-free survival (PFS) and overall survival (OS) were 50.9 months (95% CI: 26.7-75.0) and 80.9 months (60.1-101.6). By multivariate analysis, A-IDHwt (p = 0.009; p = 0.019), 7p gain and 10q loss (p = 0.009; p = 0.016) and vimentin positive staining (p = 0.011; p = 0.029) were associated with poor PFS and OS respectively. CONCLUSIONS: Insular low-grade A-IDHwt presented with poor prognosis despite a smaller tumor core and no evidence of increased perfusion on MR imaging.

Extended Modality Propagation: Image Synthesis of Pathological Cases.

This paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms.

A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling.

In this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tumors by using multi-atlas patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model automatically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

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.