Inhibition of histone deacetylase 6 (HDAC6) protects against vincristine-induced peripheral neuropathies and inhibits tumor growth

As cancer is becoming more and more a chronic disease, a large proportion of patients is confronted with devastating side effects of certain anti-cancer drugs. The most common neurological complications are painful peripheral neuropathies. Chemotherapeutics that interfere with microtubules, including plant-derived vinca-alkaloids such as vincristine, can cause these chemotherapy-induced peripheral neuropathies (CIPN). Available treatments focus on symptom alleviation and pain reduction rather than prevention of the neuropathy. The aim of this study was to investigate the potential of specific histone deacetylase 6 (HDAC6) inhibitors as a preventive therapy for CIPN using multiple rodent models for vincristine-induced peripheral neuropathies (VIPN). HDAC6 inhibition increased the level of acetylated α-tubulin in tissues of rodents undergoing vincristine-based chemotherapy, which correlates to a reduced severity of the neurological symptoms, both at the electrophysiological and the behavioral level. Mechanistically, disturbances in axonal transport of mitochondria is considered as an important contributing factor in the pathophysiology of VIPN. As vincristine interferes with the polymerization of microtubules, we investigated whether disturbances in axonal transport could contribute to VIPN. We observed that increasing α-tubulin acetylation through HDAC6 inhibition restores vincristine-induced defects of axonal transport in cultured dorsal root ganglion neurons. Finally, we assured that HDAC6-inhibition offers neuroprotection without interfering with the anti-cancer efficacy of vincristine using a mouse model for acute lymphoblastic leukemia. Taken together, our results emphasize the therapeutic potential of HDAC6 inhibitors with beneficial effects both on vincristine-induced neurotoxicity, as well as on tumor proliferation. ispartof: Neurobiology of Disease vol:111 pages:59-69 ispartof: location:United States status: publishe

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

Untrained Segmentation and Longitudinal Assessment of Brain Lesions in Multi-Spectral MRI

Brain tumor lesions, MS white matter lesions and ischemic stroke lesions are all similar in the sense that they appear in MR images as a collection of spatially coherent voxels that are hyper-intense, hypo-intense or a combination of these with respect to the healthy tissue. Moreover, these lesions evolve relatively smoothly over time. This work presents a collection of methods that tackle the problems of single-temporal segmentation, multi-temporal segmentation and multi-temporal regression of different types of lesions in brain MR images. This collection of methods will be referred to as TIMinG, a toolkit for Tumor Image-based Morphology and Growth. As the name implies, TIMinG is originally designed for brain tumors. With only some minor modifications, we show that TIMinG is also able to perform single-temporal segmentation and multi-temporal segmentation of MS lesions as well as single-temporal segmentation of stroke lesions. Basically, TIMinG is a novel fully-automated lesion segmentation method that is directly applicable to any individual patient MR image. The method is independent of the scanner or acquisition protocol and does not require any manually annotated training data. TIMinG is the best performing untrained whole tumor segmentation method and the third best overall method (trained and untrained), compared with other methods from the MICCAI Brain Tumor Segmentation 2012-2013 Challenge. Moreover, TIMinG is able to segment MS lesions and stroke lesions with a performance that is similar to state-of-the-art dedicated MS lesion and stroke lesion segmentation methods. The fact that the method is untrained and unsupervised and generalizes well to different MR modalities and various types of lesions makes the method well suited for lesion quantification in small clinical settings and pre-clinical research settings. Secondly, TIMinG is able to segment all images in a patient time series simultaneously. The TIMinG multi-temporal segmentation method is a mathematical generalization of the single-temporal method. The method is validated for both tumor and MS lesion time series. The overall segmentation accuracy and consistency is compared with the single-temporal segmentation results. It is shown for tumor patient time series that the segmentation accuracy is increased. Moreover, an increase in the temporal consistency of the tumor segmentations is shown, i.e. the TIMinG multi-temporal segmentation method performs better than the single-temporal method in measuring the whole tumor volume changes between consecutive time points. Finally, TIMinG models the lesion growth over time and simulates its shape at every moment. Simultaneously, it estimates the velocity of the local lesion growth. The real clinical acquisition dates are hereby taken into account. TIMinG's multi-temporal regression is illustrated on 16 brain tumor patient time series and the model prediction power is validated using a leave-one-out cross-validation approach. Overall, TIMinG is intended to be used by clinicians as an exploratory tool to assess the lesion morphology and growth. It can also serve as a basis for subsequent quantification, like maximal growth velocity, principal axis of growth, local acceleration of the lesion, etc. It suffices for the clinician to have a time series -two or more time points- of MR images available to obtain quantitative measures about the lesion morphology and growth.Haeck T., ''Untrained segmentation and longitudinal assessment of brain lesions in multi-spectral MRI'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, February 2017, Leuven, Belgium.status: publishe

An untrained and unsupervised method for MRI brain tumor segmentation

© 2016 IEEE. We present a fully-automated MRI brain tumor segmentation method that does not require any manually annotated training data. The method is independent of the scanner or acquisition protocol and is directly applicable to any individual patient image. An Expectation Maximization-approach is used to estimate intensity models for both normal and tumorous tissue. The segmentation is represented by a level-set that is iteratively updated to label voxels as normal or tumorous, based on which intensity model explains the voxels' intensity the best. The method is compared with the method by Menze et al. [1], which is considered to be a benchmark for unsupervised tumor segmentation. The performance of our method for segmenting the tumor volume is summarized by an average Dice score of 0.87 ± 0.06 on the training data set of the MICCAI BraTS Challenge 2012-2013.Haeck T., Maes F., Suetens P., ''An untrained and unsupervised method for MRI brain tumor segmentation'', Proceedings 13th IEEE international symposium on biomedical imaging - ISBI 2016, pp. 265-268, April 13-16, 2016, Prague, Czech Republic.status: publishe

Automated model-based segmentation of brain tumors in MR images

Haeck T., Maes F., Suetens P., ''Automated model-based segmentation of brain tumors in MR images'', Proceedings Multimodal brain tumor image segmentation challenge - BraTS 2015, held in conjunction with MICCAI 2015, pp. 25-28, October 5, 2015, Munich, Germany.status: publishe

Feasibility of atlas-based segmentation of the brain in the presence of tumor by a weighted least-squares demons algorithm

Haeck T., Dhollander T., Maes F., Sunaert S., Suetens P., ''Feasibility of atlas-based segmentation of the brain in the presence of tumor by a weighted least-squares demons algorithm'', ISMRM 21st annual meeting & exhibition, April 20-26, 2013, Salt Lake City, Utah, USA.status: publishe

Advanced head models to improve TMS-based motor cortex localization

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