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tumor</title> <meta name="description" content="Search results for: brain tumor"> <meta name="keywords" content="brain tumor"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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style="font-size:.9rem"><span class="badge badge-info">1889</span> Recent Advancement in Dendrimer Based Nanotechnology for the Treatment of Brain Tumor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nitin%20Dwivedi">Nitin Dwivedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jigna%20Shah"> Jigna Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumor is metastatic neoplasm of central nervous system, in most of cases it is life threatening disease with low survival rate. Despite of enormous efforts in the development of therapeutics and diagnostic tools, the treatment of brain tumors and gliomas remain a considerable challenge in the area of neuro-oncology. The most reason behind of this the presence of physiological barriers including blood brain barrier and blood brain tumor barrier, lead to insufficient reach ability of therapeutic agents at the site of tumor, result of inadequate destruction of gliomas. So there is an indeed need empowerment of brain tumor imaging for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional different generations of dendrimer offer an improved effort for potentiate drug delivery at the site of brain tumor and gliomas. So this article emphasizes the innovative dendrimer approaches in tumor targeting, tumor imaging and delivery of therapeutic agent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20brain%20barrier" title="blood brain barrier">blood brain barrier</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrimer" title=" dendrimer"> dendrimer</a>, <a href="https://publications.waset.org/abstracts/search?q=gliomas" title=" gliomas"> gliomas</a>, <a href="https://publications.waset.org/abstracts/search?q=nanotechnology" title=" nanotechnology"> nanotechnology</a> </p> <a href="https://publications.waset.org/abstracts/30047/recent-advancement-in-dendrimer-based-nanotechnology-for-the-treatment-of-brain-tumor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">561</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1888</span> Computer Aided Diagnostic System for Detection and Classification of a Brain Tumor through MRI Using Level Set Based Segmentation Technique and ANN Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atanu%20K%20Samanta">Atanu K Samanta</a>, <a href="https://publications.waset.org/abstracts/search?q=Asim%20Ali%20Khan"> Asim Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title="brain tumor">brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnostic%20%28CAD%29%20system" title=" computer-aided diagnostic (CAD) system"> computer-aided diagnostic (CAD) system</a>, <a href="https://publications.waset.org/abstracts/search?q=gray-level%20co-occurrence%20matrix%20%28GLCM%29" title=" gray-level co-occurrence matrix (GLCM)"> gray-level co-occurrence matrix (GLCM)</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20segmentation" title=" tumor segmentation"> tumor segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20set%20method" title=" level set method"> level set method</a> </p> <a href="https://publications.waset.org/abstracts/61237/computer-aided-diagnostic-system-for-detection-and-classification-of-a-brain-tumor-through-mri-using-level-set-based-segmentation-technique-and-ann-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61237.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">512</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1887</span> A Distinct Method Based on Mamba-Unet for Brain Tumor Image Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Djallel%20Bouamama">Djallel Bouamama</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20R.%20Haddadi"> Yasser R. Haddadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate brain tumor segmentation is crucial for diagnosis and treatment planning, yet it remains a challenging task due to the variability in tumor shapes and intensities. This paper introduces a distinct approach to brain tumor image segmentation by leveraging an advanced architecture known as Mamba-Unet. Building on the well-established U-Net framework, Mamba-Unet incorporates distinct design enhancements to improve segmentation performance. Our proposed method integrates a multi-scale attention mechanism and a hybrid loss function to effectively capture fine-grained details and contextual information in brain MRI scans. We demonstrate that Mamba-Unet significantly enhances segmentation accuracy compared to conventional U-Net models by utilizing a comprehensive dataset of annotated brain MRI scans. Quantitative evaluations reveal that Mamba-Unet surpasses traditional U-Net architectures and other contemporary segmentation models regarding Dice coefficient, sensitivity, and specificity. The improvements are attributed to the method's ability to manage class imbalance better and resolve complex tumor boundaries. This work advances the state-of-the-art in brain tumor segmentation and holds promise for improving clinical workflows and patient outcomes through more precise and reliable tumor detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor%20classification" title="brain tumor classification">brain tumor classification</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=U-NET" title=" U-NET"> U-NET</a> </p> <a href="https://publications.waset.org/abstracts/192073/a-distinct-method-based-on-mamba-unet-for-brain-tumor-image-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">34</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1886</span> Tumor Boundary Extraction Using Intensity and Texture-Based on Gradient Vector</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Namita%20Mittal">Namita Mittal</a>, <a href="https://publications.waset.org/abstracts/search?q=Himakshi%20Shekhawat"> Himakshi Shekhawat</a>, <a href="https://publications.waset.org/abstracts/search?q=Ankit%20Vidyarthi"> Ankit Vidyarthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In medical research study, doctors and radiologists face lot of complexities in analysing the brain tumors in Magnetic Resonance (MR) images. Brain tumor detection is difficult due to amorphous tumor shape and overlapping of similar tissues in nearby region. So, radiologists require one such clinically viable solution which helps in automatic segmentation of tumor inside brain MR image. Initially, segmentation methods were used to detect tumor, by dividing the image into segments but causes loss of information. In this paper, a hybrid method is proposed which detect Region of Interest (ROI) on the basis of difference in intensity values and texture values of tumor region using nearby tissues with Gradient Vector Flow (GVF) technique in the identification of ROI. Proposed approach uses both intensity and texture values for identification of abnormal section of the brain MR images. Experimental results show that proposed method outperforms GVF method without any loss of information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title="brain tumor">brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=GVF" title=" GVF"> GVF</a>, <a href="https://publications.waset.org/abstracts/search?q=intensity" title=" intensity"> intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=MR%20images" title=" MR images"> MR images</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=texture" title=" texture"> texture</a> </p> <a href="https://publications.waset.org/abstracts/22986/tumor-boundary-extraction-using-intensity-and-texture-based-on-gradient-vector" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22986.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1885</span> LGG Architecture for Brain Tumor Segmentation Using Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajeeha%20Ansar">Sajeeha Ansar</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Ali%20Safi"> Asad Ali Safi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheikh%20Ziauddin"> Sheikh Ziauddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20R.%20Shahid"> Ahmad R. Shahid</a>, <a href="https://publications.waset.org/abstracts/search?q=Faraz%20Ahsan"> Faraz Ahsan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coeﬃcient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor%20segmentation" title="brain tumor segmentation">brain tumor segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LGG" title=" LGG"> LGG</a> </p> <a href="https://publications.waset.org/abstracts/89567/lgg-architecture-for-brain-tumor-segmentation-using-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89567.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1884</span> Development of a Computer Aided Diagnosis Tool for Brain Tumor Extraction and Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fathi%20Kallel">Fathi Kallel</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulelah%20Alabd%20Uljabbar"> Abdulelah Alabd Uljabbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20Aldukhail"> Abdulrahman Aldukhail</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulaziz%20Alomran"> Abdulaziz Alomran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title=" brain tumor"> brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=CAD" title=" CAD"> CAD</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/81523/development-of-a-computer-aided-diagnosis-tool-for-brain-tumor-extraction-and-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81523.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1883</span> Magnetic Resonance Imaging in Children with Brain Tumors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20R.%20Ashrapov">J. R. Ashrapov</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20A.%20Alihodzhaeva"> G. A. Alihodzhaeva</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20E.%20Abdullaev"> D. E. Abdullaev</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20R.%20Kadirbekov"> N. R. Kadirbekov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosis of brain tumors is one of the challenges, as several central nervous system diseases run the same symptoms. Modern diagnostic techniques such as CT, MRI helps to significantly improve the surgery in the operating period, after surgery, after allowing time to identify postoperative complications in neurosurgery. Purpose: To study the MRI characteristics and localization of brain tumors in children and to detect the postoperative complications in the postoperative period. Materials and methods: A retrospective study of treatment of 62 children with brain tumors in age from 2 to 5 years was performed. Results of the review: MRI scan of the brain of the 62 patients 52 (83.8%) case revealed a brain tumor. Distribution on MRI of brain tumors found in 15 (24.1%) - glioblastomas, 21 (33.8%) - astrocytomas, 7 (11.2%) - medulloblastomas, 9 (14.5%) - a tumor origin (craniopharyngiomas, chordoma of the skull base). MRI revealed the following characteristic features: an additional sign of the heterogeneous MRI signal of hyper and hypointensive T1 and T2 modes with a different perifocal swelling degree with involvement in the process of brain vessels. The main objectives of postoperative MRI study are the identification of early or late postoperative complications, evaluation of radical surgery, the identification of the extended-growing tumor that (in terms of 3-4 weeks). MRI performed in the following cases: 1. Suspicion of a hematoma (3 days or more) 2. Suspicion continued tumor growth (in terms of 3-4 weeks). Conclusions: Magnetic resonance tomography is a highly informative method of diagnostics of brain tumors in children. MRI also helps to determine the effectiveness and tactics of treatment and the follow up in the postoperative period. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title="brain tumors">brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment" title=" treatment"> treatment</a> </p> <a href="https://publications.waset.org/abstracts/116321/magnetic-resonance-imaging-in-children-with-brain-tumors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1882</span> Tumor Detection of Cerebral MRI by Multifractal Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Oudjemia">S. Oudjemia</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Alim"> F. Alim</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Seddiki"> S. Seddiki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper shows the application of multifractal analysis for additional help in cancer diagnosis. The medical image processing is a very important discipline in which many existing methods are in search of solutions to real problems of medicine. In this work, we present results of multifractal analysis of brain MRI images. The purpose of this analysis was to separate between healthy and cancerous tissue of the brain. A nonlinear method based on multifractal detrending moving average (MFDMA) which is a generalization of the detrending fluctuations analysis (DFA) is used for the detection of abnormalities in these images. The proposed method could make separation of the two types of brain tissue with success. It is very important to note that the choice of this non-linear method is due to the complexity and irregularity of tumor tissue that linear and classical nonlinear methods seem difficult to characterize completely. In order to show the performance of this method, we compared its results with those of the conventional method box-counting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=irregularity" title="irregularity">irregularity</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinearity" title=" nonlinearity"> nonlinearity</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI%20brain%20images" title=" MRI brain images"> MRI brain images</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20analysis" title=" multifractal analysis"> multifractal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title=" brain tumor"> brain tumor</a> </p> <a href="https://publications.waset.org/abstracts/12704/tumor-detection-of-cerebral-mri-by-multifractal-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12704.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1881</span> Fast Tumor Extraction Method Based on Nl-Means Filter and Expectation Maximization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandabad%20Sara">Sandabad Sara</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayd%20Tahri%20Yassine"> Sayd Tahri Yassine</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammouch%20Ahmed"> Hammouch Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of science has allowed computer scientists to touch the medicine and bring aid to radiologists as we are presenting it in our article. Our work focuses on the detection and localization of tumors areas in the human brain; this will be a completely automatic without any human intervention. In front of the huge volume of MRI to be treated per day, the radiologist can spend hours and hours providing a tremendous effort. This burden has become less heavy with the automation of this step. In this article we present an automatic and effective tumor detection, this work consists of two steps: the first is the image filtering using the filter Nl-means, then applying the expectation maximization algorithm (EM) for retrieving the tumor mask from the brain MRI and extracting the tumor area using the mask obtained from the second step. To prove the effectiveness of this method multiple evaluation criteria will be used, so that we can compare our method to frequently extraction methods used in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=Em%20algorithm" title=" Em algorithm"> Em algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=brain" title=" brain"> brain</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor" title=" tumor"> tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=Nl-means" title=" Nl-means"> Nl-means</a> </p> <a href="https://publications.waset.org/abstracts/56745/fast-tumor-extraction-method-based-on-nl-means-filter-and-expectation-maximization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1880</span> An Advanced Automated Brain Tumor Diagnostics Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Berkan%20Ural">Berkan Ural</a>, <a href="https://publications.waset.org/abstracts/search?q=Arif%20Eser"> Arif Eser</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinan%20Apaydin"> Sinan Apaydin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing%20algorithms" title="image processing algorithms">image processing algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/69471/an-advanced-automated-brain-tumor-diagnostics-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69471.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1879</span> A Comparison between Different Segmentation Techniques Used in Medical Imaging </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibtihal%20D.%20Mustafa">Ibtihal D. Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mawia%20A.%20Hassan"> Mawia A. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tumor segmentation from MRI image is important part of medical images experts. This is particularly a challenging task because of the high assorting appearance of tumor tissue among different patients. MRI images are advance of medical imaging because it is give richer information about human soft tissue. There are different segmentation techniques to detect MRI brain tumor. In this paper, different procedure segmentation methods are used to segment brain tumors and compare the result of segmentations by using correlation and structural similarity index (SSIM) to analysis and see the best technique that could be applied to MRI image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MRI" title="MRI">MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20similarity" title=" structural similarity"> structural similarity</a> </p> <a href="https://publications.waset.org/abstracts/51091/a-comparison-between-different-segmentation-techniques-used-in-medical-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51091.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">410</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1878</span> An Accurate Brain Tumor Segmentation for High Graded Glioma Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajeeha%20Ansar">Sajeeha Ansar</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Ali%20Safi"> Asad Ali Safi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheikh%20Ziauddin"> Sheikh Ziauddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20R.%20Shahid"> Ahmad R. Shahid</a>, <a href="https://publications.waset.org/abstracts/search?q=Faraz%20Ahsan"> Faraz Ahsan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coeﬃcient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor%20segmentation" title="brain tumor segmentation">brain tumor segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=HGG" title=" HGG"> HGG</a> </p> <a href="https://publications.waset.org/abstracts/89564/an-accurate-brain-tumor-segmentation-for-high-graded-glioma-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1877</span> Brain Tumor Segmentation Based on Minimum Spanning Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simeon%20Mayala">Simeon Mayala</a>, <a href="https://publications.waset.org/abstracts/search?q=Ida%20Herdlev%C3%A6r"> Ida Herdlevær</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonas%20Bull%20Haugs%C3%B8en"> Jonas Bull Haugsøen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamundeeswari%20Anandan"> Shamundeeswari Anandan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonia%20Gavasso"> Sonia Gavasso</a>, <a href="https://publications.waset.org/abstracts/search?q=Morten%20Brun"> Morten Brun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the standard gold segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title="brain tumor">brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor%20segmentation" title=" brain tumor segmentation"> brain tumor segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20spanning%20tree" title=" minimum spanning tree"> minimum spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/148591/brain-tumor-segmentation-based-on-minimum-spanning-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148591.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1876</span> Computer-Aided Diagnosis System Based on Multiple Quantitative Magnetic Resonance Imaging Features in the Classification of Brain Tumor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih%20Jou%20Hsiao">Chih Jou Hsiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chung%20Ming%20Lo"> Chung Ming Lo</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Chun%20Hsieh"> Li Chun Hsieh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title="brain tumor">brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnosis" title=" computer-aided diagnosis"> computer-aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=gliomas" title=" gliomas"> gliomas</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a> </p> <a href="https://publications.waset.org/abstracts/70083/computer-aided-diagnosis-system-based-on-multiple-quantitative-magnetic-resonance-imaging-features-in-the-classification-of-brain-tumor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70083.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1875</span> Clinical Applications of Amide Proton Transfer Magnetic Resonance Imaging: Detection of Brain Tumor Proliferative Activity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fumihiro%20Ima">Fumihiro Ima</a>, <a href="https://publications.waset.org/abstracts/search?q=Shinichi%20Watanabe"> Shinichi Watanabe</a>, <a href="https://publications.waset.org/abstracts/search?q=Shingo%20Maeda"> Shingo Maeda</a>, <a href="https://publications.waset.org/abstracts/search?q=Haruna%20Imai"> Haruna Imai</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroki%20Niimi"> Hiroki Niimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is important to know growth rate of brain tumors before surgery because it influences treatment planning including not only surgical resection strategy but also adjuvant therapy after surgery. Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging (MRI) technique based on chemical exchange saturation transfer without administration of contrast medium. The underlying assumption in APT imaging of tumors is that there is a close relationship between the proliferative activity of the tumor and mobile protein synthesis. We aimed to evaluate the diagnostic performance of APT imaging of pre-and post-treatment brain tumors. Ten patients with brain tumor underwent conventional and APT-weighted sequences on a 3.0 Tesla MRI before clinical intervention. The maximum and the minimum APT-weighted signals (APTWmax and APTWmin) in each solid tumor region were obtained and compared before and after clinical intervention. All surgical specimens were examined for histopathological diagnosis. Eight of ten patients underwent adjuvant therapy after surgery. Histopathological diagnosis was glioma in 7 patients (WHO grade 2 in 2 patients, WHO grade 3 in 3 patients and WHO grade 4 in 2 patients), meningioma WHO grade1 in 2 patients and primary lymphoma of the brain in 1 patient. High-grade gliomas showed significantly higher APTW-signals than that in low-grade gliomas. APTWmax in one huge parasagittal meningioma infiltrating into the skull bone was higher than that in glioma WHO grade 4. On the other hand, APTWmax in another convexity meningioma was the same as that in glioma WHO grade 3. Diagnosis of primary lymphoma of the brain was possible with APT imaging before pathological confirmation. APTW-signals in residual tumors decreased dramatically within one year after adjuvant therapy in all patients. APT imaging demonstrated excellent diagnostic performance for the planning of surgery and adjuvant therapy of brain tumors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amides" title="amides">amides</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title=" brain tumors"> brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20proliferation" title=" cell proliferation"> cell proliferation</a> </p> <a href="https://publications.waset.org/abstracts/157244/clinical-applications-of-amide-proton-transfer-magnetic-resonance-imaging-detection-of-brain-tumor-proliferative-activity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1874</span> Biophysical Modeling of Anisotropic Brain Tumor Growth</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mutaz%20Dwairy">Mutaz Dwairy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solid tumors have high interstitial fluid pressure (IFP), high mechanical stress, and low oxygen levels. Solid stresses may induce apoptosis, stimulate the invasiveness and metastasis of cancer cells, and lower their proliferation rate, while oxygen concentration may affect the response of cancer cells to treatment. Although tumors grow in a nonhomogeneous environment, many existing theoretical models assume homogeneous growth and tissue has uniform mechanical properties. For example, the brain consists of three primary materials: white matter, gray matter, and cerebrospinal fluid (CSF). Therefore, tissue inhomogeneity should be considered in the analysis. This study established a physical model based on convection-diffusion equations and continuum mechanics principles. The model considers the geometrical inhomogeneity of the brain by including the three different matters in the analysis: white matter, gray matter, and CSF. The model also considers fluid-solid interaction and explicitly describes the effect of mechanical factors, e.g., solid stresses and IFP, chemical factors, e.g., oxygen concentration, and biological factors, e.g., cancer cell concentration, on growing tumors. In this article, we applied the model on a brain tumor positioned within the white matter, considering the brain inhomogeneity to estimate solid stresses, IFP, the cancer cell concentration, oxygen concentration, and the deformation of the tissues within the neoplasm and the surrounding. Tumor size was estimated at different time points. This model might be clinically crucial for cancer detection and treatment planning by measuring mechanical stresses, IFP, and oxygen levels in the tissue. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomechanical%20model" title="biomechanical model">biomechanical model</a>, <a href="https://publications.waset.org/abstracts/search?q=interstitial%20fluid%20pressure" title=" interstitial fluid pressure"> interstitial fluid pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=solid%20stress" title=" solid stress"> solid stress</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20microenvironment" title=" tumor microenvironment"> tumor microenvironment</a> </p> <a href="https://publications.waset.org/abstracts/186318/biophysical-modeling-of-anisotropic-brain-tumor-growth" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186318.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1873</span> Clinical Applications of Amide Proton Transfer Magnetic Resonance Imaging: Detection of Brain Tumor Proliferative Activity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fumihiro%20Imai">Fumihiro Imai</a>, <a href="https://publications.waset.org/abstracts/search?q=Shinichi%20Watanabe"> Shinichi Watanabe</a>, <a href="https://publications.waset.org/abstracts/search?q=Shingo%20Maeda"> Shingo Maeda</a>, <a href="https://publications.waset.org/abstracts/search?q=Haruna%20Imai"> Haruna Imai</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroki%20Niimi"> Hiroki Niimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is important to know the growth rate of brain tumors before surgery because it influences treatment planning, including not only surgical resection strategy but also adjuvant therapy after surgery. Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging (MRI) technique based on chemical exchange saturation transfer without the administration of a contrast medium. The underlying assumption in APT imaging of tumors is that there is a close relationship between the proliferative activity of the tumor and mobile protein synthesis. We aimed to evaluate the diagnostic performance of APT imaging of pre-and post-treatment brain tumors. Ten patients with brain tumor underwent conventional and APT-weighted sequences on a 3.0 Tesla MRI before clinical intervention. The maximum and the minimum APT-weighted signals (APTWmax and APTWmin) in each solid tumor region were obtained and compared before and after a clinical intervention. All surgical specimens were examined for histopathological diagnosis. Eight of ten patients underwent adjuvant therapy after surgery. Histopathological diagnosis was glioma in 7 patients (WHO grade 2 in 2 patients, WHO grade 3 in 3 patients, and WHO grade 4 in 2 patients), meningioma WHO grade 1 in 2 patients, and primary lymphoma of the brain in 1 patient. High-grade gliomas showed significantly higher APTW signals than that low-grade gliomas. APTWmax in one huge parasagittal meningioma infiltrating into the skull bone was higher than that in glioma WHO grade 4. On the other hand, APTWmax in another convexity meningioma was the same as that in glioma WHO grade 3. Diagnosis of primary lymphoma of the brain was possible with APT imaging before pathological confirmation. APTW signals in residual tumors decreased dramatically within one year after adjuvant therapy in all patients. APT imaging demonstrated excellent diagnostic performance for the planning of surgery and adjuvant therapy of brain tumors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amides" title="amides">amides</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title=" brain tumors"> brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20proliferation" title=" cell proliferation"> cell proliferation</a> </p> <a href="https://publications.waset.org/abstracts/164452/clinical-applications-of-amide-proton-transfer-magnetic-resonance-imaging-detection-of-brain-tumor-proliferative-activity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164452.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1872</span> Dexamethasone Treatment Deregulates Proteoglycans Expression in Normal Brain Tissue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Y.%20Tsidulko">A. Y. Tsidulko</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20M.%20Pankova"> T. M. Pankova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20V.%20Grigorieva"> E. V. Grigorieva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High-grade gliomas are the most frequent and most aggressive brain tumors which are characterized by active invasion of tumor cells into the surrounding brain tissue, where the extracellular matrix (ECM) plays a crucial role. Disruption of ECM can be involved in anticancer drugs effectiveness, side-effects and also in tumor relapses. The anti-inflammatory agent dexamethasone is a common drug used during high-grade glioma treatment for alleviating cerebral edema. Although dexamethasone is widely used in the clinic, its effects on normal brain tissue ECM remain poorly investigated. It is known that proteoglycans (PGs) are a major component of the extracellular matrix in the central nervous system. In our work, we studied the effects of dexamethasone on the ECM proteoglycans (syndecan-1, glypican-1, perlecan, versican, brevican, NG2, decorin, biglican, lumican) using RT-PCR in the experimental animal model. It was shown that proteoglycans in rat brain have age-specific expression patterns. In early post-natal rat brain (8 days old rat pups) overall PGs expression was quite high and mainly expressed PGs were biglycan, decorin, and syndecan-1. The overall transcriptional activity of PGs in adult rat brain is 1.5-fold decreased compared to post-natal brain. The expression pattern was changed as well with biglycan, decorin, syndecan-1, glypican-1 and brevican becoming almost equally expressed. PGs expression patterns create a specific tissue microenvironment that differs in developing and adult brain. Dexamethasone regimen close to the one used in the clinic during high-grade glioma treatment significantly affects proteoglycans expression. It was shown that overall PGs transcription activity is 1.5-2-folds increased after dexamethasone treatment. The most up-regulated PGs were biglycan, decorin, and lumican. The PGs expression pattern in adult brain changed after treatment becoming quite close to the expression pattern in developing brain. It is known that microenvironment in developing tissues promotes cells proliferation while in adult tissues proliferation is usually suppressed. The changes occurring in the adult brain after dexamethasone treatment may lead to re-activation of cell proliferation due to signals from changed microenvironment. Taken together obtained data show that dexamethasone treatment significantly affects the normal brain ECM, creating the appropriate microenvironment for tumor cells proliferation and thus can reduce the effectiveness of anticancer treatment and promote tumor relapses. This work has been supported by a Russian Science Foundation (RSF Grant 16-15-10243) <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dexamthasone" title="dexamthasone">dexamthasone</a>, <a href="https://publications.waset.org/abstracts/search?q=extracellular%20matrix" title=" extracellular matrix"> extracellular matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=glioma" title=" glioma"> glioma</a>, <a href="https://publications.waset.org/abstracts/search?q=proteoglycan" title=" proteoglycan"> proteoglycan</a> </p> <a href="https://publications.waset.org/abstracts/53326/dexamethasone-treatment-deregulates-proteoglycans-expression-in-normal-brain-tissue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53326.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1871</span> Identifying the True Extend of Glioblastoma Based on Preoperative FLAIR Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Shukir">B. Shukir</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Szivos"> L. Szivos</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Kis"> D. Kis</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Barzo"> P. Barzo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glioblastoma is the most malignant brain tumor. In general, the survival rate varies between (14-18) months. Glioblastoma consists a solid and infiltrative part. The standard therapeutic management of glioblastoma is maximum safe resection followed by chemo-radiotherapy. It’s hypothesized that the pretumoral hyperintense region in fluid attenuated inversion recovery (FLAIR) images includes both vasogenic edema and infiltrated tumor cells. In our study, we aimed to define the sensitivity and specificity of hyperintense FLAIR images preoperatively to examine how well it can define the true extent of glioblastoma. (16) glioblastoma patients included in this study. Hyperintense FLAIR region were delineated preoperatively as tumor mask. The infiltrative part of glioblastoma considered the regions where the tumor recurred on the follow up MRI. The recurrence on the CE-T1 images was marked as the recurrence masks. According to (AAL3) and (JHU white matter labels) atlas, the brain divided into cortical and subcortical regions respectively. For calculating specificity and sensitivity, the FLAIR and the recurrence masks overlapped counting how many regions affected by both . The average sensitivity and specificity was 83% and 85% respectively. Individually, the sensitivity and specificity varied between (31-100)%, and (100-58)% respectively. These results suggest that despite FLAIR being as an effective radiologic imaging tool its prognostic value remains controversial and probabilistic tractography remain more reliable available method for identifying the true extent of glioblastoma. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title="brain tumors">brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=glioblastoma" title=" glioblastoma"> glioblastoma</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=FLAIR" title=" FLAIR"> FLAIR</a> </p> <a href="https://publications.waset.org/abstracts/185362/identifying-the-true-extend-of-glioblastoma-based-on-preoperative-flair-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185362.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">53</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1870</span> Malignancy Assessment of Brain Tumors Using Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chung-Ming%20Lo">Chung-Ming Lo</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Li-Chun%20Hsieh"> Kevin Li-Chun Hsieh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnosis" title=" computer-aided diagnosis"> computer-aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=glioblastoma" title=" glioblastoma"> glioblastoma</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a> </p> <a href="https://publications.waset.org/abstracts/108847/malignancy-assessment-of-brain-tumors-using-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108847.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1869</span> Nanoscale Mapping of the Mechanical Modifications Occurring in the Brain Tumour Microenvironment by Atomic Force Microscopy: The Case of the Highly Aggressive Glioblastoma and the Slowly Growing Meningioma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriele%20Ciasca">Gabriele Ciasca</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanya%20E.%20Sassun"> Tanya E. Sassun</a>, <a href="https://publications.waset.org/abstracts/search?q=Eleonora%20Minelli"> Eleonora Minelli</a>, <a href="https://publications.waset.org/abstracts/search?q=Manila%20Antonelli"> Manila Antonelli</a>, <a href="https://publications.waset.org/abstracts/search?q=Massimiliano%20Papi"> Massimiliano Papi</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Santoro"> Antonio Santoro</a>, <a href="https://publications.waset.org/abstracts/search?q=Felice%20Giangaspero"> Felice Giangaspero</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberto%20Delfini"> Roberto Delfini</a>, <a href="https://publications.waset.org/abstracts/search?q=Marco%20De%20Spirito"> Marco De Spirito</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glioblastoma multiforme (GBM) is an extremely aggressive brain tumor, characterized by a diffuse infiltration of neoplastic cells into the brain parenchyma. Although rarely considered, mechanical cues play a key role in the infiltration process that is extensively mediated by the tumor microenvironment stiffness and, more in general, by the occurrence of aberrant interactions between neoplastic cells and the extracellular matrix (ECM). Here we provide a nano-mechanical characterization of the viscoelastic response of human GBM tissues by indentation-type atomic force microscopy. High-resolution elasticity maps show a large difference between the biomechanics of GBM tissues and the healthy peritumoral regions, opening possibilities to optimize the tumor resection area. Moreover, we unveil the nanomechanical signature of necrotic regions and anomalous vasculature, that are two major hallmarks useful for glioma staging. Actually, the morphological grading of GBM relies mainly on histopathological findings that make extensive use of qualitative parameters. Our findings have the potential to positively impact on the development of novel quantitative methods to assess the tumor grade, which can be used in combination with conventional histopathological examinations. In order to provide a more in-depth description of the role of mechanical cues in tumor progression, we compared the nano-mechanical fingerprint of GBM tissues with that of grade-I (WHO) meningioma, a benign lesion characterized by a completely different growth pathway with the respect to GBM, that, in turn hints at a completely different role of the biomechanical interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AFM" title="AFM">AFM</a>, <a href="https://publications.waset.org/abstracts/search?q=nano-mechanics" title=" nano-mechanics"> nano-mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomedicine" title=" nanomedicine"> nanomedicine</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title=" brain tumors"> brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=glioblastoma" title=" glioblastoma"> glioblastoma</a> </p> <a href="https://publications.waset.org/abstracts/63186/nanoscale-mapping-of-the-mechanical-modifications-occurring-in-the-brain-tumour-microenvironment-by-atomic-force-microscopy-the-case-of-the-highly-aggressive-glioblastoma-and-the-slowly-growing-meningioma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63186.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1868</span> Site-Specific Delivery of Hybrid Upconversion Nanoparticles for Photo-Activated Multimodal Therapies of Glioblastoma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-Chung%20Tsai">Yuan-Chung Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Masao%20Kamimura"> Masao Kamimura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kohei%20Soga"> Kohei Soga</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsin-Cheng%20Chiu"> Hsin-Cheng Chiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to enhance the photodynamic/photothermal therapeutic efficacy on glioblastoma, the functionized upconversion nanoparticles with the capability of converting the deep tissue penetrating near-infrared light into visible wavelength for activating photochemical reaction were developed. The drug-loaded nanoparticles (NPs) were obtained from the self-assembly of oleic acid-coated upconversion nanoparticles along with maleimide-conjugated poly(ethylene glycol)-cholesterol (Mal-PEG-Chol), as the NP stabilizer, and hydrophobic photosensitizers, IR-780 (for photothermal therapy, PTT) and mTHPC (for photodynamic therapy, PDT), in aqueous phase. Both the IR-780 and mTHPC were loaded into the hydrophobic domains within NPs via hydrophobic association. The peptide targeting ligand, angiopep-2, was further conjugated with the maleimide groups at the end of PEG adducts on the NP surfaces, enabling the affinity coupling with the low-density lipoprotein receptor-related protein-1 of tumor endothelial cells and malignant astrocytes. The drug-loaded NPs with the size of ca 80 nm in diameter exhibit a good colloidal stability in physiological conditions. The in vitro data demonstrate the successful targeting delivery of drug-loaded NPs toward the ALTS1C1 cells (murine astrocytoma cells) and the pronounced cytotoxicity elicited by combinational effect of PDT and PTT. The in vivo results show the promising brain orthotopic tumor targeting of drug-loaded NPs and sound efficacy for brain tumor dual-modality treatment. This work shows great potential for improving photodynamic/photothermal therapeutic efficacy of brain cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20delivery" title="drug delivery">drug delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=orthotopic%20brain%20tumor" title=" orthotopic brain tumor"> orthotopic brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=photodynamic%2Fphotothermal%20therapies" title=" photodynamic/photothermal therapies"> photodynamic/photothermal therapies</a>, <a href="https://publications.waset.org/abstracts/search?q=upconversion%20nanoparticles" title=" upconversion nanoparticles"> upconversion nanoparticles</a> </p> <a href="https://publications.waset.org/abstracts/78103/site-specific-delivery-of-hybrid-upconversion-nanoparticles-for-photo-activated-multimodal-therapies-of-glioblastoma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1867</span> Localization of Frontal and Temporal Speech Areas in Brain Tumor Patients by Their Structural Connections with Probabilistic Tractography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.Shukir">B.Shukir</a>, <a href="https://publications.waset.org/abstracts/search?q=H.Woo"> H.Woo</a>, <a href="https://publications.waset.org/abstracts/search?q=P.Barzo"> P.Barzo</a>, <a href="https://publications.waset.org/abstracts/search?q=D.Kis"> D.Kis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Preoperative brain mapping in tumors involving the speech areas has an important role to reduce surgical risks. Functional magnetic resonance imaging (fMRI) is the gold standard method to localize cortical speech areas preoperatively, but its availability in clinical routine is difficult. Diffusion MRI based probabilistic tractography is available in head MRI. It’s used to segment cortical subregions by their structural connectivity. In our study, we used probabilistic tractography to localize the frontal and temporal cortical speech areas. 15 patients with left frontal tumor were enrolled to our study. Speech fMRI and diffusion MRI acquired preoperatively. The standard automated anatomical labelling atlas 3 (AAL3) cortical atlas used to define 76 left frontal and 118 left temporal potential speech areas. 4 types of tractography were run according to the structural connection of these regions to the left arcuate fascicle (FA) to localize those cortical areas which have speech functions: 1, frontal through FA; 2, frontal with FA; 3, temporal to FA; 4, temporal with FA connections were determined. Thresholds of 1%, 5%, 10% and 15% applied. At each level, the number of affected frontal and temporal regions by fMRI and tractography were defined, the sensitivity and specificity were calculated. At the level of 1% threshold showed the best results. Sensitivity was 61,631,4% and 67,1523,12%, specificity was 87,210,4% and 75,611,37% for frontal and temporal regions, respectively. From our study, we conclude that probabilistic tractography is a reliable preoperative technique to localize cortical speech areas. However, its results are not feasible that the neurosurgeon rely on during the operation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20mapping" title="brain mapping">brain mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20tumor" title=" brain tumor"> brain tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20tractography" title=" probabilistic tractography"> probabilistic tractography</a> </p> <a href="https://publications.waset.org/abstracts/165964/localization-of-frontal-and-temporal-speech-areas-in-brain-tumor-patients-by-their-structural-connections-with-probabilistic-tractography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165964.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">166</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1866</span> Brain Tumor Detection and Classification Using Pre-Trained Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Karade">Aditya Karade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharada%20Falane"> Sharada Falane</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhananjay%20Deshmukh"> Dhananjay Deshmukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijaykumar%20Mantri"> Vijaykumar Mantri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumour" title="brain tumour">brain tumour</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI%20image" title=" MRI image"> MRI image</a>, <a href="https://publications.waset.org/abstracts/search?q=detecting%20and%20classifying%20tumour" title=" detecting and classifying tumour"> detecting and classifying tumour</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-trained%20models" title=" pre-trained models"> pre-trained models</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a> </p> <a href="https://publications.waset.org/abstracts/178879/brain-tumor-detection-and-classification-using-pre-trained-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1865</span> Dosimetric Analysis of Intensity Modulated Radiotherapy versus 3D Conformal Radiotherapy in Adult Primary Brain Tumors: Regional Cancer Centre, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravi%20Kiran%20Pothamsetty">Ravi Kiran Pothamsetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Radha%20Rani%20Ghosh"> Radha Rani Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Baby%20Paul%20Thaliath"> Baby Paul Thaliath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radiation therapy has undergone many advancements and evloved from 2D to 3D. Recently, with rapid pace of drug discoveries, cutting edge technology, and clinical trials has made innovative advancements in computer technology and treatment planning and upgraded to intensity modulated radiotherapy (IMRT) which delivers in homogenous dose to tumor and normal tissues. The present study was a hospital-based experience comparing two different conformal radiotherapy techniques for brain tumors. This analytical study design has been conducted at Regional Cancer Centre, India from January 2014 to January 2015. Ten patients have been selected after inclusion and exclusion criteria. All the patients were treated on Artiste Siemens Linac Accelerator. The tolerance level for maximum dose was 6.0 Gyfor lenses and 54.0 Gy for brain stem, optic chiasm and optical nerves as per RTOG criteria. Mean and standard deviation values of PTV98%, PTV 95% and PTV 2% in IMRT were 93.16±2.9, 95.01±3.4 and 103.1±1.1 respectively; for 3DCRT were 91.4±4.7, 94.17±2.6 and 102.7±0.39 respectively. PTV max dose (%) in IMRT and 3D-CRT were 104.7±0.96 and 103.9±1.0 respectively. Maximum dose to the tumor can be delivered with IMRT with acceptable toxicity limits. Variables such as expertise, location of tumor, patient condition, and TPS influence the outcome of the treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumors" title="brain tumors">brain tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=intensity%20modulated%20radiotherapy%20%28IMRT%29" title=" intensity modulated radiotherapy (IMRT)"> intensity modulated radiotherapy (IMRT)</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20dimensional%20conformal%20radiotherapy%20%283D-CRT%29" title=" three dimensional conformal radiotherapy (3D-CRT)"> three dimensional conformal radiotherapy (3D-CRT)</a>, <a href="https://publications.waset.org/abstracts/search?q=radiation%20therapy%20oncology%20group%20%28RTOG%29" title=" radiation therapy oncology group (RTOG)"> radiation therapy oncology group (RTOG)</a> </p> <a href="https://publications.waset.org/abstracts/74706/dosimetric-analysis-of-intensity-modulated-radiotherapy-versus-3d-conformal-radiotherapy-in-adult-primary-brain-tumors-regional-cancer-centre-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74706.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">239</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1864</span> Targeting Glucocorticoid Receptor Eliminate Dormant Chemoresistant Cancer Stem Cells in Glioblastoma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aoxue%20Yang">Aoxue Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Weili%20Tian"> Weili Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonghe%20Wu"> Yonghe Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Haikun%20Liu"> Haikun Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumor stem cells (BTSCs) are resistant to therapy and give rise to recurrent tumors. These rare and elusive cells are likely to disseminate during cancer progression, and some may enter dormancy, remaining viable but not increasing. The identification of dormant BTSCs is thus necessary to design effective therapies for glioblastoma (GBM) patients. Little progress has been made in therapeutic treatment of glioblastoma in the last decade despite rapid progress in molecular understanding of brain tumors1. Here we show that the stress hormone glucocorticoid is essential for the maintenance of brain tumor stem cells (BTSCs), which are resistant to conventional therapy. The glucocorticoid receptor (GR) regulates metabolic plasticity and chemoresistance of the dormant BTSC via controlling expression of GPD1 (glycerol-3-phosphate dehydrogenase 1), which is an essential regulator of lipid metabolism in BTSCs. Genomic, lipidomic and cellular analysis confirm that GR/GPD1 regulation is essential for BTSCs metabolic plasticity and survival. We further demonstrate that the GR agonist dexamethasone (DEXA), which is commonly used to control edema in glioblastoma, abolishes the effect of chemotherapy drug temozolomide (TMZ) by upregulating GPD1 and thus promoting tumor cell dormancy in vivo, this provides a mechanistic explanation and thus settle the long-standing debate of usage of steroid in brain tumor patient edema control. Pharmacological inhibition of GR/GPD1 pathway disrupts metabolic plasticity of BTSCs and prolong animal survival, which is superior to standard chemotherapy. Patient case study shows that GR antagonist mifepristone blocks tumor progression and leads to symptomatic improvement. This study identifies an important mechanism regulating cancer stem cell dormancy and provides a new opportunity for glioblastoma treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20stem%20cell" title="cancer stem cell">cancer stem cell</a>, <a href="https://publications.waset.org/abstracts/search?q=dormancy" title=" dormancy"> dormancy</a>, <a href="https://publications.waset.org/abstracts/search?q=glioblastoma" title=" glioblastoma"> glioblastoma</a>, <a href="https://publications.waset.org/abstracts/search?q=glycerol-3-phosphate%20dehydrogenase%201" title=" glycerol-3-phosphate dehydrogenase 1"> glycerol-3-phosphate dehydrogenase 1</a>, <a href="https://publications.waset.org/abstracts/search?q=glucocorticoid%20receptor" title=" glucocorticoid receptor"> glucocorticoid receptor</a>, <a href="https://publications.waset.org/abstracts/search?q=dexamethasone" title=" dexamethasone"> dexamethasone</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA-sequencing" title=" RNA-sequencing"> RNA-sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=phosphoglycerides." title=" phosphoglycerides."> phosphoglycerides.</a> </p> <a href="https://publications.waset.org/abstracts/150825/targeting-glucocorticoid-receptor-eliminate-dormant-chemoresistant-cancer-stem-cells-in-glioblastoma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150825.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1863</span> Riesz Mixture Model for Brain Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouna%20Zitouni">Mouna Zitouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariem%20Tounsi">Mariem Tounsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research introduces an application of the Riesz mixture model for medical image segmentation for accurate diagnosis and treatment of brain tumors. We propose a pixel classification technique based on the Riesz distribution, derived from an extended Bartlett decomposition. To our knowledge, this is the first study addressing this approach. The Expectation-Maximization algorithm is implemented for parameter estimation. A comparative analysis, using both synthetic and real brain images, demonstrates the superiority of the Riesz model over a recent method based on the Wishart distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title="EM algorithm">EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=Riesz%20probability%20distribution" title=" Riesz probability distribution"> Riesz probability distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Wishart%20probability%20distribution" title=" Wishart probability distribution"> Wishart probability distribution</a> </p> <a href="https://publications.waset.org/abstracts/192126/riesz-mixture-model-for-brain-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">18</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1862</span> PCR Based DNA Analysis in Detecting P53 Mutation in Human Breast Cancer (MDA-468)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debbarma%20Asis">Debbarma Asis</a>, <a href="https://publications.waset.org/abstracts/search?q=Guha%20Chandan"> Guha Chandan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tumor Protein-53 (P53) is one of the tumor suppressor proteins. P53 regulates the cell cycle that conserves stability by preventing genome mutation. It is named so as it runs as 53-kilodalton (kDa) protein on Polyacrylamide gel electrophoresis although the actual mass is 43.7 kDa. Experimental evidence has indicated that P53 cancer mutants loses tumor suppression activity and subsequently gain oncogenic activities to promote tumourigenesis. Tumor-specific DNA has recently been detected in the plasma of breast cancer patients. Detection of tumor-specific genetic materials in cancer patients may provide a unique and valuable tumor marker for diagnosis and prognosis. Commercially available MDA-468 breast cancer cell line was used for the proposed study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tumor%20protein%20%28P53%29" title="tumor protein (P53)">tumor protein (P53)</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20mutants" title=" cancer mutants"> cancer mutants</a>, <a href="https://publications.waset.org/abstracts/search?q=MDA-468" title=" MDA-468"> MDA-468</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20suppressor%20gene" title=" tumor suppressor gene"> tumor suppressor gene</a> </p> <a href="https://publications.waset.org/abstracts/43690/pcr-based-dna-analysis-in-detecting-p53-mutation-in-human-breast-cancer-mda-468" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1861</span> Computational Screening of Secretory Proteins with Brain-Specific Expression in Glioblastoma Multiforme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumera">Sumera</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanila%20Amber"> Sanila Amber</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Javed%20Mirza"> Fatima Javed Mirza</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Ali"> Amjad Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Saadia%20Zahid"> Saadia Zahid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glioblastoma multiforme (GBM) is a widely spread and fatal primary brain tumor with an increased risk of relapse in spite of aggressive treatment. The current procedures for GBM diagnosis include invasive procedures i.e. resection or biopsy, to acquire tumor mass. Implementation of negligibly invasive tests as a potential diagnostic technique and biofluid-based monitoring of GBM stresses on discovering biomarkers in CSF and blood. Therefore, we performed a comprehensive in silico analysis to identify potential circulating biomarkers for GBM. Initially, six gene and protein databases were utilized to mine brain-specific proteins. The resulting proteins were filtered using a channel of five tools to predict the secretory proteins. Subsequently, the expression profile of the secreted proteins was verified in the brain and blood using two databases. Additional verification of the resulting proteins was done using Plasma Proteome Database (PPD) to confirm their presence in blood. The final set of proteins was searched in literature for their relationship with GBM, keeping a special emphasis on secretome proteome. 2145 proteins were firstly mined as brain-specific, out of which 69 proteins were identified as secretory in nature. Verification of expression profile in brain and blood eliminated 58 proteins from the 69 proteins, providing a final list of 11 proteins. Further verification of these 11 proteins further eliminated 2 proteins, giving a final set of nine secretory proteins i.e. OPCML, NPTX1, LGI1, CNTN2, LY6H, SLIT1, CREG2, GDF1 and SERPINI1. Out of these 9 proteins, 7 were found to be linked to GBM, whereas 2 proteins are not investigated in GBM so far. We propose that these secretory proteins can serve as potential circulating biomarker signatures of GBM and will facilitate the development of minimally invasive diagnostic methods and novel therapeutic interventions for GBM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=glioblastoma%20multiforme" title="glioblastoma multiforme">glioblastoma multiforme</a>, <a href="https://publications.waset.org/abstracts/search?q=secretory%20proteins" title=" secretory proteins"> secretory proteins</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20secretome" title=" brain secretome"> brain secretome</a>, <a href="https://publications.waset.org/abstracts/search?q=biomarkers" title=" biomarkers"> biomarkers</a> </p> <a href="https://publications.waset.org/abstracts/144723/computational-screening-of-secretory-proteins-with-brain-specific-expression-in-glioblastoma-multiforme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1860</span> Ultra Wideband Breast Cancer Detection by Using SAR for Indication the Tumor Location</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wittawat%20Wasusathien">Wittawat Wasusathien</a>, <a href="https://publications.waset.org/abstracts/search?q=Samran%20Santalunai"> Samran Santalunai</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanaset%20Thosdeekoraphat"> Thanaset Thosdeekoraphat</a>, <a href="https://publications.waset.org/abstracts/search?q=Chanchai%20Thongsopa"> Chanchai Thongsopa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents breast cancer detection by observing the specific absorption rate (SAR) intensity for identification tumor location, the tumor is identified in coordinates (x,y,z) system. We examined the frequency between 4-8 GHz to look for the most appropriate frequency. Results are simulated in frequency 4-8 GHz, the model overview include normal breast with 50 mm radian, 5 mm diameter of tumor, and ultra wideband (UWB) bowtie antenna. The models are created and simulated in CST Microwave Studio. For this simulation, we changed antenna to 5 location around the breast, the tumor can be detected when an antenna is close to the tumor location, which the coordinate of maximum SAR is approximated the tumor location. For reliable, we experiment by random tumor location to 3 position in the same size of tumor and simulation the result again by varying the antenna position in 5 position again, and it also detectable the tumor position from the antenna that nearby tumor position by maximum value of SAR, which it can be detected the tumor with precision in all frequency between 4-8 GHz. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=specific%20absorption%20rate%20%28SAR%29" title="specific absorption rate (SAR)">specific absorption rate (SAR)</a>, <a href="https://publications.waset.org/abstracts/search?q=ultra%20wideband%20%28UWB%29" title=" ultra wideband (UWB)"> ultra wideband (UWB)</a>, <a href="https://publications.waset.org/abstracts/search?q=coordinates" title=" coordinates"> coordinates</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20detection" title=" cancer detection"> cancer detection</a> </p> <a href="https://publications.waset.org/abstracts/10465/ultra-wideband-breast-cancer-detection-by-using-sar-for-indication-the-tumor-location" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10465.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=brain%20tumor&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=brain%20tumor&page=3">3</a></li> <li class="page-item"><a class="page-link" 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