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Query: UMLS:C0017638 (glioma)
 30,880 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We have studied the incorporation and cytotoxicity of doxorubicin in cultured rat C6 glioma cells grown as monolayers. Net incorporation was linear up to high extracellular concentrations of drug (10 micrograms/ml). Cytotoxicity was evaluated both by tritiated thymidine incorporation inhibition and cloning efficiency inhibition. For similar total drug exposures, cytotoxicity was very different according to the exposure time and the exposure dose; incubation with a low dose for a long time was much less cytotoxic than that performed with a high dose for a short period of time. We have obtained several clones of doxorubicin-resistant cells. As compared to the wild strain, these cells were characterized by a larger size, a slower growth, a reduced cloning efficiency and a differential sensitivity of 100-1,000 to doxorubicin. Net incorporation of doxorubicin was 5-fold reduced in these cells, due to an increased efflux of the drug. These cells provide an interesting model of doxorubicin-resistant solid tumor in culture.
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PMID:Cellular pharmacology of doxorubicin in sensitive and resistant rat glioblastoma cells in culture. 394 4

The uptake, intracellular distribution and cytotoxicity of high doses of extracellular zinc was investigated in C6 rat glioma cells. Net zinc uptake occurred only above certain thresholds in time and concentration, below them no alterations of the intracellular zinc level were observed. These results were obtained by measurements with the fluorescent dye Zinquin and by atomic absorption spectrometry, yielding similar results with both methods. Sequestration of zinc in intracellular vesicles was observed by fluorescence microscopy. A protective effect of vesicular sequestration is indicated, because increased levels of intracellular zinc located in vesicles did not necessarily lead to an increase in cytotoxicity. We were able to show that in C6 cells, in contrast to other cell lines, zinc that is released from proteins by the NO donor SNOC is also sequestered in vesicular structures. These zinc-carrying vesicles showed to be constitutive and are assumed to have a function in the maintainance of the cytosolic content of Zn2+ ions.
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PMID:Uptake and intracellular distribution of labile and total Zn(II) in C6 rat glioma cells investigated with fluorescent probes and atomic absorption. 1058 89

Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.
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PMID:Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. 2685 41

The detection and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is a very challenging task, despite the availability of modern medical image processing tools. Neuro-radiologists still diagnose deadly brain cancers such as even glioblastoma using manual segmentation. This approach is not only tedious, but also highly variable, featuring limited accuracy and precision, and hence raising the need for more robust, automated techniques. Deep learning methods such as the U-Net deep convolutional neural networks have been widely used in biomedical image segmentation. Although this model was demonstrated to yield desirable results on the BRATS 2015 dataset by using a pixel-wise segmentation map of the input image as an auto-encoder, which assures best segmentation accuracy, the output only showed limited accuracy and robustness for a number of cases. The goal of this work was to improve the U-net model by replacing the de-convolution component with an up-sampled by the Nearest-neighbor algorithm and also employing an elastic transformation to augment the training dataset to render the model more robust, especially for the segmentation of low-grade tumors. The proposed Nearest-Neighbor Re-sampling Based Elastic-Transformed (NNRET) U-net Deep CNN framework has been trained on 285 glioma patients BRATS 2017 MR dataset available through the MICCAI 2017 grand challenge. The framework has been tested on 146 patients using Dice similarity coefficient (DSC) & Intersection over Union (IoU) performance metrics and outweighed the classic U-net model.
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PMID:A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation. 3121 99

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance (p < 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor (p < 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC.
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PMID:Inception Modules Enhance Brain Tumor Segmentation. 3135 62

Purpose: Gliomas are the most common primary brain malignancies, with varying degrees of aggressiveness and prognosis. Understanding of tumor biology and intra-tumor heterogeneity is necessary for planning personalized therapy and predicting response to therapy. Accurate tumoral and intra-tumoral segmentation on MRI is the first step toward understanding the tumor biology through computational methods. The purpose of this study was to design a segmentation algorithm and evaluate its performance on pre-treatment brain MRIs obtained from patients with gliomas. Materials and Methods: In this study, we have designed a novel 3D U-Net architecture that segments various radiologically identifiable sub-regions like edema, enhancing tumor, and necrosis. Weighted patch extraction scheme from the tumor border regions is proposed to address the problem of class imbalance between tumor and non-tumorous patches. The architecture consists of a contracting path to capture context and the symmetric expanding path that enables precise localization. The Deep Convolutional Neural Network (DCNN) based architecture is trained on 285 patients, validated on 66 patients and tested on 191 patients with Glioma from Brain Tumor Segmentation (BraTS) 2018 challenge dataset. Three dimensional patches are extracted from multi-channel BraTS training dataset to train 3D U-Net architecture. The efficacy of the proposed approach is also tested on an independent dataset of 40 patients with High Grade Glioma from our tertiary cancer center. Segmentation results are assessed in terms of Dice Score, Sensitivity, Specificity, and Hausdorff 95 distance (ITCN intra-tumoral classification network). Result: Our proposed architecture achieved Dice scores of 0.88, 0.83, and 0.75 for the whole tumor, tumor core and enhancing tumor, respectively, on BraTS validation dataset and 0.85, 0.77, 0.67 on test dataset. The results were similar on the independent patients' dataset from our hospital, achieving Dice scores of 0.92, 0.90, and 0.81 for the whole tumor, tumor core and enhancing tumor, respectively. Conclusion: The results of this study show the potential of patch-based 3D U-Net for the accurate intra-tumor segmentation. From experiments, it is observed that the weighted patch-based segmentation approach gives comparable performance with the pixel-based approach when there is a thin boundary between tumor subparts.
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PMID:A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas. 3213 13

Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.
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PMID:Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features. 3232 96

Glioblastoma multiforme (GBM) is the most malignant form of glioma, and the overall survival time of patients with GBM is usually less than 14 months. Therefore, it is urgent to find new and effective medicine for GBM. Recently, marine natural products have been shown to exhibit strong inhibitory effects on cancer cells, providing a new avenue for exploring novel drugs for GBM treatment. In this study, we investigated the inhibitory effect of the Grincamycin (GCN) B-F, newly isolated from marine-derived Streptomyces Lusitanus SCSIO LR32, on GBM cells, and evaluated the mechanism of GCN B on GBM. The results, for the first time, showed that GCN B acted as a potent inhibitor to suppress growth and invasion of two human GBM cell lines U251 and 091214 in vitro. In addition, GCN B could effectively target GSCs in GBM evidenced by attenuated formation of tumor spheres and decrease of several markers of GSCs. Furthermore, we performed gene expression microarray followed by Signal-Net analysis. The result revealed that RHOA and PI3K/AKT axis played critical roles for a GCN B-mediated inhibitory effect on GSCs. Altogether, our findings highlighted GCN B as a promising inhibitor for GSCs via targeting RHOA and PI3K/AKT.
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PMID:Grincamycin B Functions as a Potent Inhibitor for Glioblastoma Stem Cell via Targeting RHOA and PI3K/AKT. 3258 47

Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth prediction of these tumors, particularly gliomas which are the most dominant type, can be quite useful to improve treatment planning, quantify tumor aggressiveness, and estimate patients' survival time towards precision medicine. Studying tumor growth prediction basically requires multiple time points of single or multimodal medical images of the same patient. Recent models are based on complex mathematical formulations that basically rely on a system of partial differential equations, e.g. reaction diffusion model, to capture the diffusion and proliferation of tumor cells in the surrounding tissue. However, these models usually have small number of parameters that are insufficient to capture different patterns and other characteristics of the tumors. In addition, such models consider tumor growth independently for each subject, not being able to get benefit from possible common growth patterns existed in the whole population under study. In this paper, we propose a novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, for growth prediction of glioma. Specifically, we use stacked conditional GANs with a novel objective function that includes both l1 and Dice losses. Moreover, we use segmented feature maps to guide the generator for better generated images. Our generator is designed based on a modified 3D U-Net architecture with skip connections to combine hierarchical features and thus have a better generated image. The proposed method is trained and tested on 18 subjects with 3 time points (9 subjects from collaborative hospital and 9 subjects from BRATS 2014 dataset). Results show that our proposed GP-GAN outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.
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PMID:GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images. 3297 77

Gliomas are the most dominant and lethal type of brain tumors. Growth prediction is significant to quantify tumor aggressiveness, improve therapy planning, and estimate patients' survival time. This is commonly addressed in literature using mathematical models guided by multi-time point scans of multi/single-modal data for the same subject. However, these models are mechanism-based and heavily rely on complicated mathematical formulations of partial differential equations with few parameters that are insufficient to capture different patterns and other characteristics of gliomas. In this paper, we propose a 3D generative adversarial networks (GANs) for glioma growth prediction. Specifically, we stack 2 GANs with conditional initialization of segmented feature maps. Furthermore, we employ Dice loss in our objective function and devised 3D U-Net architecture for better image generation. The proposed method is trained and validated using 3D patch-based strategy on real magnetic resonance images of 9 subjects with 3 time points. Experimental results show that the proposed method can be successfully used for glioma growth prediction with satisfactory performance.
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PMID:Glioma Growth Prediction via Generative Adversarial Learning from Multi-Time Points Magnetic Resonance Images. 3301 36


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