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tumor</title> <meta name="description" content="Search results for: tumor"> <meta name="keywords" content="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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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="tumor"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 802</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: tumor</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">802</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">478</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">801</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">800</span> Evaluation of Tumor-Infiltrating Lymphocytes in Breast Carcinoma: Correlation with Molecular Subtypes and Clinicopathological Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arundhathi%20S.">Arundhathi S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Poongodi%20R."> Poongodi R.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tumor-infiltrating lymphocytes (TILs) are indicative of the local immune response against tumor proliferation and metastasis. Emerging as a significant marker of immune reactivity, TILs are utilized to evaluate prognostic outcomes across various malignancies, including colon, ovarian, lung, bladder, and breast cancers. In breast cancer (BC), TILs are particularly relevant for assessing tumor response to therapy in both adjuvant and neoadjuvant settings, with a prominent role in triple-negative breast cancer (TNBC), where they have been associated with improved outcomes. As such, TILs are recognized as an independent marker of favorable prognosis in several tumor types, underscoring their potential as a tool in personalized cancer therapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=intratumoral%20TIL" title=" intratumoral TIL"> intratumoral TIL</a>, <a href="https://publications.waset.org/abstracts/search?q=stromal%20TIL" title=" stromal TIL"> stromal TIL</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20infiltrating%20lymphocytes" title=" tumor infiltrating lymphocytes"> tumor infiltrating lymphocytes</a> </p> <a href="https://publications.waset.org/abstracts/194529/evaluation-of-tumor-infiltrating-lymphocytes-in-breast-carcinoma-correlation-with-molecular-subtypes-and-clinicopathological-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194529.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">8</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">799</span> MicroRNA Expression Distinguishes Neutrophil Subtypes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20I.%20You">R. I. You</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20L.%20Ho"> C. L. Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Dai"> M. S. Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20M.%20Hung"> H. M. Hung</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20F.%20Yen"> S. F. Yen</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Chen"> C. S. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Y.%20Chao"> T. Y. Chao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neutrophils are the most abundant innate immune cells to against invading microorganisms. Numerous data shown neutrophils have plasticity in response to physiological and pathological conditions. Tumor-associated neutrophils (TAN) exist in distinct types of tumor and play an important role in cancer biology. Different transcriptomic profiles of neutrophils in tumor and non-tumor samples have been identified. Several miRNAs have been recognized as regulators of gene expression in neutrophil, which may have key roles in neutrophil activation. However, the miRNAs expression patterns in TAN are not well known. To address this question, magnetic bead isolated neutrophils from tumor-bearing mice were used in this study. We analyzed production of reactive oxygen species (ROS) by luminol-dependent chemiluminescence assay. The expression of miRNAs targeting NADPH oxidase, ROS generation and autophagy was explored using quantitative real-time polymerase chain reaction. Our data suggest that tumor environment influence neutrophil develop to differential states of activation via miRNAs regulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tumor-associated%20neutrophil" title="tumor-associated neutrophil">tumor-associated neutrophil</a>, <a href="https://publications.waset.org/abstracts/search?q=miRNAs" title=" miRNAs"> miRNAs</a>, <a href="https://publications.waset.org/abstracts/search?q=neutrophil" title=" neutrophil"> neutrophil</a>, <a href="https://publications.waset.org/abstracts/search?q=ROS" title=" ROS "> ROS </a> </p> <a href="https://publications.waset.org/abstracts/13682/microrna-expression-distinguishes-neutrophil-subtypes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13682.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">470</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">798</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">797</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">511</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">796</span> Mage Fusion Based Eye Tumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Ashit">Ahmed Ashit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image fusion is a significant and efficient image processing method used for detecting different types of tumors. This method has been used as an effective combination technique for obtaining high quality images that combine anatomy and physiology of an organ. It is the main key in the huge biomedical machines for diagnosing cancer such as PET-CT machine. This thesis aims to develop an image analysis system for the detection of the eye tumor. Different image processing methods are used to extract the tumor and then mark it on the original image. The images are first smoothed using median filtering. The background of the image is subtracted, to be then added to the original, results in a brighter area of interest or tumor area. The images are adjusted in order to increase the intensity of their pixels which lead to clearer and brighter images. once the images are enhanced, the edges of the images are detected using canny operators results in a segmented image comprises only of the pupil and the tumor for the abnormal images, and the pupil only for the normal images that have no tumor. The images of normal and abnormal images are collected from two sources: “Miles Research” and “Eye Cancer”. The computerized experimental results show that the developed image fusion based eye tumor detection system is capable of detecting the eye tumor and segment it to be superimposed on the original image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=eye%20tumor" title=" eye tumor"> eye tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=canny%20operators" title=" canny operators"> canny operators</a>, <a href="https://publications.waset.org/abstracts/search?q=superimposed" title=" superimposed"> superimposed</a> </p> <a href="https://publications.waset.org/abstracts/30750/mage-fusion-based-eye-tumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30750.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">363</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">795</span> Effects of Aerobic Training on MicroRNA Let-7a Expression and Levels of Tumor Tissue IL-6 in Mice With Breast Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Anoosheh">Leila Anoosheh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aim: The aim of this study was to assess The effects of aerobic training on microRNA let-7a expression and levels of tumor tissue IL-6 in mice with breast cancer. Method: Twenty BALB/c c mice (4-5 weeks,17 gr mass) were cancerous by injection of estrogen-dependent receptor breast cancer cells MC4-L2 and divided into two groups: tumor-training(TT) and tumor-control(TC) group. Then TT group completed aerobic training for 6 weeks, 5 days per week (14-18 m/min). After tumor emersion, tumor width and length were measured by digital caliper every week. 48 hours after the last exercise subjects were killed. Tissue sampling were collected and stored in -70ᵒ. Tumor tissue was homogenized and let-7a expression and IL-6 levels were accounted with Real time-PCR and ELISA Kit respectively. Statistical analysis of let-7a was conducted by the REST software. Repeated measures and independent tests were used to assess tumor size and IL-6, respectively. Results: Tumor size and IL-6 levels were significantly decreased in TT group compare with TC group (p<0.05). microRNA let-7a was increased significantly in TT against control group respectively (p=0/000). Conclusion: Reduction in tumor size, followed by aerobic exercise can be attributed to the loss of inflammatory factors such as IL-6; It seems that regarding to up regulation effects of aerobic exercise training on let-7a and down regulation effects of that on IL-6 in mice with breast cancer, This type of training can be used as adjuvant therapy in conjunction with other therapies for breast cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=aerobic%20training" title=" aerobic training"> aerobic training</a>, <a href="https://publications.waset.org/abstracts/search?q=microRNA%20%20let-7a" title=" microRNA let-7a"> microRNA let-7a</a>, <a href="https://publications.waset.org/abstracts/search?q=IL-6" title=" IL-6"> IL-6</a> </p> <a href="https://publications.waset.org/abstracts/16519/effects-of-aerobic-training-on-microrna-let-7a-expression-and-levels-of-tumor-tissue-il-6-in-mice-with-breast-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16519.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">431</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">794</span> An Insight into Early Stage Detection of Malignant Tumor by Microwave Imaging </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Hassan%20Khalil">Muhammad Hassan Khalil</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Jiadong"> Xu Jiadong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection of malignant tumor inside the breast of women is a challenging field for the researchers. MWI (Microwave imaging) for breast cancer diagnosis has been of interest for last two decades, newly it suggested for finding cancerous tissues of women breast. A simple and basic idea of the mathematical modeling is used throughout this paper for imaging of malignant tumor. In this paper, the authors explained inverse scattering method in the microwave imaging and also present some simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20detection" title="breast cancer detection">breast cancer detection</a>, <a href="https://publications.waset.org/abstracts/search?q=microwave%20imaging" title=" microwave imaging"> microwave imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=tomography" title=" tomography"> tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor" title=" tumor"> tumor</a> </p> <a href="https://publications.waset.org/abstracts/2718/an-insight-into-early-stage-detection-of-malignant-tumor-by-microwave-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2718.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">793</span> Metastatic Ovarian Tumor Discovered Accidentally during Cesarean Section in a 34 Year Old Woman: A Case Report</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20E.%20Esheba">Ghada E. Esheba</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghufran%20Kheshaifaty"> Ghufran Kheshaifaty</a>, <a href="https://publications.waset.org/abstracts/search?q=Kholoud%20%20Al-Harbi"> Kholoud Al-Harbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wafa%27a%20Al-Harbi"> Wafa'a Al-Harbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ala%27a%20Al-Orabi"> Ala'a Al-Orabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Moayad%20Turkistani"> Moayad Turkistani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Krukenberg tumor is a rare metastatic ovarian carcinoma that usually occurs in female between 30 - 40 year old and rarely seen after menopause. Stomach is the most common primary site. Histopathological features of krukenberg tumors appear as diffuse stromal proliferation, mucus-production, and numerous signet-cells and these tumors spread mostly by lymphatic route. Treatment and prognostic factors are not well established. This study describes a 34 year old female with a unilateral ovarian mass discovered accidentally during cesarean section delivery and it was misdiagnosed as luteoma of pregnancy, but histopathological examination showed a diffuse infiltration of the ovary and omentum by signet ring cells. These findings were not correlated with luteoma of pregnancy or any other types of primary ovarian tumors like surface epithelial tumor, sex cord stromal tumor or germ cell tumor. However, after the analysis of immunohistochemical results (negative CK7, positive CK20 and CDX-2), the finding was the diagnostic of metastatic krukenberg tumor. Two weeks later, the patient was evaluated and a large gastric tumor was found in her stomach and she underwent gastrectomy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CK7" title="CK7">CK7</a>, <a href="https://publications.waset.org/abstracts/search?q=CK20" title=" CK20"> CK20</a>, <a href="https://publications.waset.org/abstracts/search?q=CDX-2" title=" CDX-2"> CDX-2</a>, <a href="https://publications.waset.org/abstracts/search?q=Krukenburg%20tumor" title=" Krukenburg tumor"> Krukenburg tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=metastatic%20ovarian%20tumor" title=" metastatic ovarian tumor"> metastatic ovarian tumor</a> </p> <a href="https://publications.waset.org/abstracts/59354/metastatic-ovarian-tumor-discovered-accidentally-during-cesarean-section-in-a-34-year-old-woman-a-case-report" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59354.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">315</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">792</span> Liver Tumor Detection by Classification through FD Enhancement of CT Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Ghatwary">N. Ghatwary</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ahmed"> A. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Jalab"> H. Jalab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20differential%20%28FD%29" title="fractional differential (FD)">fractional differential (FD)</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography%20%28CT%29" title=" computed tomography (CT)"> computed tomography (CT)</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=aplha" title=" aplha"> aplha</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20features." title=" texture features."> texture features.</a> </p> <a href="https://publications.waset.org/abstracts/39719/liver-tumor-detection-by-classification-through-fd-enhancement-of-ct-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39719.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">358</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">791</span> Stability Analysis of Tumor-Immune Fractional Order Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadia%20Arshad">Sadia Arshad</a>, <a href="https://publications.waset.org/abstracts/search?q=Yifa%20Tang"> Yifa Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Dumitru%20Baleanu"> Dumitru Baleanu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fractional order mathematical model is proposed that incorporate CD8+ cells, natural killer cells, cytokines and tumor cells. The tumor cells growth in the absence of an immune response is modeled by logistic law as it was the simplest form for which predictions also agreed with the experimental data. Natural Killer Cells are our first line of defense. NK cells directly kill tumor cells through several mechanisms, including the release of cytoplasmic granules containing perforin and granzyme, expression of tumor necrosis factor (TNF) family members. The effect of the NK cells on the tumor cell population is expressed with the product term. Rational form is used to describe interaction between CD8+ cells and tumor cells. A number of cytokines are produced by NKs, including tumor necrosis factor TNF, IFN, and interleukin (IL-10). Source term for cytokines is modeled by Michaelis-Menten form to indicate the saturated effects of the immune response. Stability of the equilibrium points is discussed for biologically significant values of bifurcation parameters. We studied the treatment of fractional order system by investigating analytical conditions of tumor eradication. Numerical simulations are presented to illustrate the analytical results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20model" title="cancer model">cancer model</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20calculus" title=" fractional calculus"> fractional calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulations" title=" numerical simulations"> numerical simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20analysis" title=" stability analysis"> stability analysis</a> </p> <a href="https://publications.waset.org/abstracts/52821/stability-analysis-of-tumor-immune-fractional-order-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52821.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">315</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">790</span> Evaluation of Tumor Microenvironment Using Molecular Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fakhrosadat%20Sajjadian">Fakhrosadat Sajjadian</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Ghasemi%20Shayan"> Ramin Ghasemi Shayan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The tumor microenvironment plays an fundamental part in tumor start, movement, metastasis, and treatment resistance. It varies from ordinary tissue in terms of its extracellular network, vascular and lymphatic arrange, as well as physiological conditions. The clinical application of atomic cancer imaging is regularly prevented by the tall commercialization costs of focused on imaging operators as well as the constrained clinical applications and little showcase measure of a few operators. . Since numerous cancer types share comparable characteristics of the tumor microenvironment, the capacity to target these biomarkers has the potential to supply clinically translatable atomic imaging advances for numerous types encompassing cancer and broad clinical applications. Noteworthy advance has been made in focusing on the tumor microenvironment for atomic cancer imaging. In this survey, we summarize the standards and methodologies of later progresses in atomic imaging of the tumor microenvironment, utilizing distinctive imaging modalities for early discovery and conclusion of cancer. To conclude, The tumor microenvironment (TME) encompassing tumor cells could be a profoundly energetic and heterogeneous composition of safe cells, fibroblasts, forerunner cells, endothelial cells, flagging atoms and extracellular network (ECM) components. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=molecular" title="molecular">molecular</a>, <a href="https://publications.waset.org/abstracts/search?q=imaging" title=" imaging"> imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=TME" title=" TME"> TME</a>, <a href="https://publications.waset.org/abstracts/search?q=medicine" title=" medicine"> medicine</a> </p> <a href="https://publications.waset.org/abstracts/182733/evaluation-of-tumor-microenvironment-using-molecular-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182733.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">45</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">789</span> Predictive Value of Primary Tumor Depth for Cervical Lymphadenopathy in Squamous Cell Carcinoma of Buccal Mucosa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zohra%20Salim">Zohra Salim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objective: To access the relationship of primary tumor thickness with cervical lymphadenopathy in squamous cell carcinoma of buccal mucosa. Methodology: A cross-sectional observational study was carried out on 80 Patients with biopsy-proven oral squamous cell carcinoma of buccal mucosa at Dow University of Health Sciences. All the study participants were treated with wide local excision of the primary tumor with elective neck dissection. Patients with prior head and neck malignancy or those with prior radiotherapy or chemotherapy were excluded from the study. Data was entered and analyzed on SPSS 21. Chi-squared test with 95% C.I and 80% power of the test was used to evaluate the relationship of tumor depth with cervical lymph nodes. Results: 50 participants were male, and 30 patients were female. 30 patients were in the age range of 20-40 years, 36 patients in the range of 40-60 years, while 14 patients were beyond age 60 years. Tumor size ranged from 0.3cm to 5cm with a mean of 2.03cm. Tumor depth ranged from 0.2cm to 5cm. 20% of the participants reported with tumor depth greater than 2.5cm, while 80% of patients reported with tumor depth less than 2.5cm. Out of 80 patients, 27 reported with negative lymph nodes, while 53 patients reported with positive lymph nodes. Conclusion: Our study concludes that relationship exists between the depth of primary tumor and cervical lymphadenopathy in squamous cell carcinoma of buccal mucosa. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=squamous%20cell%20carcinoma" title="squamous cell carcinoma">squamous cell carcinoma</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20depth" title=" tumor depth"> tumor depth</a>, <a href="https://publications.waset.org/abstracts/search?q=cervical%20lymphadenopathy" title=" cervical lymphadenopathy"> cervical lymphadenopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=buccal%20mucosa" title=" buccal mucosa"> buccal mucosa</a> </p> <a href="https://publications.waset.org/abstracts/85223/predictive-value-of-primary-tumor-depth-for-cervical-lymphadenopathy-in-squamous-cell-carcinoma-of-buccal-mucosa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85223.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">237</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">788</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">787</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">33</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">786</span> Identification of Functional T Cell Receptors Reactive to Tumor Antigens from the T Cell Repertoire of Healthy Donors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Quiros-Fernandez">Isaac Quiros-Fernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Angel%20Cid-Arregui"> Angel Cid-Arregui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tumor-reactive T cell receptors (TCRs) are being subject of intense investigation since they offer great potential in adoptive cell therapies against cancer. However, the identification of tumor-specific TCRs has proven challenging, for instance, due to the limited expansion capacity of tumor-infiltrating T cells (TILs) and the extremely low frequencies of tumor-reactive T cells in the repertoire of patients and healthy donors. We have developed an approach for rapid identification and characterization of neoepitope-reactive TCRs from the T cell repertoire of healthy donors. CD8 T cells isolated from multiple donors are subjected to a first sorting step after staining with HLA multimers carrying the peptide of interest. The isolated cells are expanded for two weeks, after which a second sorting is performed using the same peptide-HLA multimers. The cells isolated in this way are then processed for single-cell sequencing of their TCR alpha and beta chains. Newly identified TCRs are cloned in appropriate expression vectors for functional analysis on Jurkat, NK92, and primary CD8 T cells and tumor cells expressing the appropriate antigen. We have identified TCRs specifically binding HLA-A2 presenting epitopes of tumor antigens, which are capable of inducing TCR-mediated cell activation and cytotoxicity in target cancer cell lines. This method allows the identification of tumor-reactive TCRs in about two to three weeks, starting from peripheral blood samples of readily available healthy donors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer" title="cancer">cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=TCR" title=" TCR"> TCR</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20antigens" title=" tumor antigens"> tumor antigens</a>, <a href="https://publications.waset.org/abstracts/search?q=immunotherapy" title=" immunotherapy"> immunotherapy</a> </p> <a href="https://publications.waset.org/abstracts/153990/identification-of-functional-t-cell-receptors-reactive-to-tumor-antigens-from-the-t-cell-repertoire-of-healthy-donors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153990.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">69</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">785</span> Effects of a Bioactive Subfraction of Strobilanthes Crispus on the Tumour Growth, Body Weight and Haematological Parameters in 4T1-Induced Breast Cancer Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusha%27u%20Shu%27aibu%20Baraya">Yusha'u Shu'aibu Baraya</a>, <a href="https://publications.waset.org/abstracts/search?q=Kah%20Keng%20%20Wong"> Kah Keng Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Nik%20Soriani%20Yaacob"> Nik Soriani Yaacob</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Strobilanthes crispus (S. crispus), is a Malaysian herb locally known as ‘Pecah kaca’ or ‘Jin batu’ which have demonstrated potent anticancer effects in both in vitro and in vivo models. In particular, S. crispus subfraction (SCS) significantly reduced tumor growth in N-methyl-N-Nitrosourea-induced breast cancer rat model. However, there is paucity of information on the effects of SCS in breast cancer metastasis. Thus, in this study, the antimetastatic effects of SCS (100 mg/kg) was investigated following 30 days of treatment in 4T1-induced mammary tumor (n = 5) model. The response to treatment was assessed based on the outcome of the tumour growth, body weight and hematological parameters. The results demonstrated that tumor bearing mice treated with SCS (TM-S) had significant (p<0.05) reduction in the mean tumor number and tumor volume as well as tumor weight compared to the tumor bearing mice (TM), i.e. tumor untreated group. Also, there was no secondary tumor formation or tumor-associated lesions in the major organs of TM-S compared to the TM group. Similarly, comparable body weights were observed among the TM-S, normal (uninduced) mice treated with SCS and normal (untreated/control) mice (NM) groups compared to the TM group (p<0.05). Furthermore, SCS administration does not cause significant changes in the hematological parameters as compared to the NM group, which indicates no sign of anemia and toxicity related effects. In conclusion, SCS significantly inhibited the overall tumor growth and metastasis in 4T1-induced breast cancer mouse model suggesting its promising potentials as therapeutic agent for breast cancer treatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=4T1-cells" title="4T1-cells">4T1-cells</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=metastasis" title=" metastasis"> metastasis</a>, <a href="https://publications.waset.org/abstracts/search?q=Strobilanthes%20crispus" title=" Strobilanthes crispus "> Strobilanthes crispus </a> </p> <a href="https://publications.waset.org/abstracts/119710/effects-of-a-bioactive-subfraction-of-strobilanthes-crispus-on-the-tumour-growth-body-weight-and-haematological-parameters-in-4t1-induced-breast-cancer-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119710.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">151</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">784</span> Drug Delivery to Solid Tumor: Effect of Dynamic Capillary Network Induced by Tumor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Sefidgar">Mostafa Sefidgar</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Bazmara"> Hossein Bazmara</a>, <a href="https://publications.waset.org/abstracts/search?q=Madjid%20Soltani"> Madjid Soltani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, and drug extravasation from microvascular. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to show how capillary network structure induced by tumor affects drug delivery. The effect of heterogeneous capillary network induced by tumor on interstitial fluid flow and drug delivery is investigated by this multi scale method. The sprouting angiogenesis model is used for generating capillary network induced by tumor. Fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network and fluid flow in normal and tumor tissues. The Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. Finally, convection-diffusion-reaction equation is used to simulate drug delivery. The dynamic approach which changes the capillary network structure based on signals sent by hemodynamic and metabolic stimuli is used in this study for more realistic assumption. The study indicates that drug delivery to solid tumors depends on the tumor induced capillary network structure. The dynamic approach generates the irregular capillary network around the tumor and predicts a higher interstitial pressure in the tumor region. This elevated interstitial pressure with irregular capillary network leads to a heterogeneous distribution of drug in the tumor region similar to in vivo observations. The investigation indicates that the drug transport properties have a significant role against the physiological barrier of drug delivery to a solid tumor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solid%20tumor" title="solid tumor">solid tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20barriers%20to%20drug%20delivery" title=" physiological barriers to drug delivery"> physiological barriers to drug delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=angiogenesis" title=" angiogenesis"> angiogenesis</a>, <a href="https://publications.waset.org/abstracts/search?q=microvascular%20network" title=" microvascular network"> microvascular network</a>, <a href="https://publications.waset.org/abstracts/search?q=solute%20transport" title=" solute transport"> solute transport</a> </p> <a href="https://publications.waset.org/abstracts/37128/drug-delivery-to-solid-tumor-effect-of-dynamic-capillary-network-induced-by-tumor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37128.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">312</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">783</span> MSIpred: A Python 2 Package for the Classification of Tumor Microsatellite Instability from Tumor Mutation Annotation Data Using a Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen%20Wang">Chen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun%20Liang"> Chun Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite (MS) length due to a deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for tumor prognostic. Conventional clinical diagnosis of MSI examines PCR products of a panel of MS markers using electrophoresis (MSI-PCR) which is laborious, time consuming, and less reliable. MSIpred, a python 2 package for automatic classification of MSI was released by this study. It computes important somatic mutation features from files in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these to predict tumor MSI status with a support vector machine (SVM) classifier trained by MAF files of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent 358-tumor test set achieved overall accuracy of over 98% and area under receiver operating characteristic (ROC) curve of 0.967. These results indicated that MSIpred is a robust pan-cancer MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microsatellite%20instability" title="microsatellite instability">microsatellite instability</a>, <a href="https://publications.waset.org/abstracts/search?q=pan-cancer%20classification" title=" pan-cancer classification"> pan-cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=somatic%20mutation" title=" somatic mutation"> somatic mutation</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/93236/msipred-a-python-2-package-for-the-classification-of-tumor-microsatellite-instability-from-tumor-mutation-annotation-data-using-a-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93236.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">173</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">782</span> Collision Tumor of Plasmacytoma with Hematological and Non-Hematological Malignancies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arati%20Inamdar">Arati Inamdar</a>, <a href="https://publications.waset.org/abstracts/search?q=Siddharth%20Bhattacharyya"> Siddharth Bhattacharyya</a>, <a href="https://publications.waset.org/abstracts/search?q=Kester%20Haye"> Kester Haye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collision tumors are rare entities characterized by neoplasms of two different cell populations with distinct separating boundaries. Such tumors could be benign, malignant, or a combination of both. The exact mechanism of origin for collision tumors is predicted to be tumor heterogeneity or concurrent occurrence of neoplasm in the same organ. We present two cases of plasmacytoma presenting as a collision tumor, one with a tumor of hematological origin and another with a non-hematological origin, namely Chronic Lymphocytic Leukemia and Adenocarcinoma of the colon, respectively. The immunohistochemical stains and flowcytometry analysis performed on the specimens aided incorrect diagnosis. Interestingly, neoplastic cells of plasmacytoma in the first case demonstrated strong cytokeratin along with weak Epithelial Specific Antigen/ Epithelial cell adhesion molecule Monoclonal Antibody (MOC31) positivity, indicating that the tumor may influence the microenvironment of the tumor in the vicinity. Furthermore, the next-generation sequencing studies performed on the specimen with plasmacytoma and chronic lymphocytic lymphoma demonstrated BReast CAncer gene (BRCA2) and Tumor Necrosis Factor Alpha Induced Protein 3 (TNFAIP3) as a disease associated variants suggestive of risk for multiple tumors including collision tumors. Our reports highlight the unique collision tumors involving plasmacytoma, which have never been reported previously, as well as provide necessary insights about the underline genetic aberrations and tumor heterogeneity through sequencing studies and allow clonality assessment for subsequent tumors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BRCA2" title="BRCA2">BRCA2</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20tumor" title=" collision tumor"> collision tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=chronic%20lymphocytic%20leukemia" title=" chronic lymphocytic leukemia"> chronic lymphocytic leukemia</a>, <a href="https://publications.waset.org/abstracts/search?q=plasmacytoma" title=" plasmacytoma"> plasmacytoma</a> </p> <a href="https://publications.waset.org/abstracts/162721/collision-tumor-of-plasmacytoma-with-hematological-and-non-hematological-malignancies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162721.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">189</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">781</span> Development and Characterization of Site Specific Peptide Conjugated Polymeric Nanoparticles for Efficient Delivery of Paclitaxel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madhu%20Gupta">Madhu Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Vikas%20Sharma"> Vikas Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Suresh%20P.%20Vyas"> Suresh P. Vyas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CD13 receptors are abundantly overexpressed in tumor cells as well as in neovasculature. The CD13 receptors were selected as a targeted site and polymeric nanoparticles (NPs) as a targeted delivery system. By combining these, a cyclic NGR (cNGR) peptide ligand was coupled on the terminal end of polyethylene glycol-b-poly(lactic-co-glycolic acid) (PEG-b-PLGA) and prepared the dual targeted-NPs (cNGR-PEG-PTX-NPs) to enhance the intracellular delivery of anticancer drug to tumor cells and tumor endothelial cells via ligand-receptor interaction. In-vitro cytotoxicity studies confirmed that the presence of cNGR enhanced the cytotoxic efficiency by 2.8 folds in Human Umbilical Vein Endothelial (HUVEC) cells, while cytotoxicity was improved by 2.6 folds in human fibrosarcoma (HT-1080) cells as compared to non-specific stealth NPs. Compared with other tested NPs, cNGR-PEG-PTX-NPs revealed more cytotoxicity by inducing more apoptosis and higher intracellular uptake. The tumor volume inhibition rate was 59.7% in case of cNGR-PEG-PTX-NPs that was comparatively more with other formulations, indicating that cNGR-PEG-PTX-NPs could more effectively inhibit tumor growth. As a consequence, the cNGR-PEG-PTX-NPs play a key role in enhancing tumor therapeutic efficiency for treatment of CD13 receptor specific solid tumor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyclic%20NGR" title="cyclic NGR">cyclic NGR</a>, <a href="https://publications.waset.org/abstracts/search?q=CD13%20receptor" title=" CD13 receptor"> CD13 receptor</a>, <a href="https://publications.waset.org/abstracts/search?q=targeted%20polymeric%20NPs" title=" targeted polymeric NPs"> targeted polymeric NPs</a>, <a href="https://publications.waset.org/abstracts/search?q=solid%20tumor" title=" solid tumor"> solid tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=intracellular%20delivery" title=" intracellular delivery "> intracellular delivery </a> </p> <a href="https://publications.waset.org/abstracts/30544/development-and-characterization-of-site-specific-peptide-conjugated-polymeric-nanoparticles-for-efficient-delivery-of-paclitaxel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30544.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">437</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">780</span> Zinc Oxide Nanoparticles as Support for Classical Anti-cancer Therapies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadine%20Wiesmann">Nadine Wiesmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Melanie%20Viel"> Melanie Viel</a>, <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Buhr"> Christoph Buhr</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachel%20Tanner"> Rachel Tanner</a>, <a href="https://publications.waset.org/abstracts/search?q=Wolfgang%20Tremel"> Wolfgang Tremel</a>, <a href="https://publications.waset.org/abstracts/search?q=Juergen%20Brieger"> Juergen Brieger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recidivation of tumors and the development of resistances against the classical anti-tumor approaches represent a major challenge we face when treating cancer. In order to master this challenge, we are in desperate need of new treatment options beyond the beaten tracks. Zinc oxide nanoparticles (ZnO NPs) represent such an innovative approach. Zinc oxide is characterized by a high level of biocompatibility, concurrently ZnO NPs are able to exert anti-tumor effects. By concentration of the nanoparticles at the tumor site, tumor cells can specifically be exposed to the nanoparticles while low zinc concentrations at off-target sites are tolerated well and can be excreted easily. We evaluated the toxicity of ZnO NPs in vitro with the help of immortalized tumor cell lines and primary cells stemming from healthy tissue. Additionally, the Chorioallantoic Membrane Assay (CAM Assay) was employed to gain insights into the in vivo behavior of the nanoparticles. We could show that ZnO NPs interact with tumor cells as nanoparticulate matter. Furthermore, the extensive release of zinc ions from the nanoparticles nearby and within the tumor cells results in overload with zinc. Beyond that, ZnO NPs were found to further the generation of reactive oxygen species (ROS). We were able to show that tumor cells were more prone to the toxic effects of ZnO NPs at intermediate concentrations compared to fibroblasts. With the help of ZnO NPs covered by a silica shell in which FITC dye was incorporated, we were able to track ZnO NPs within tumor cells as well as within a whole organism in the CAM assay after injection into the bloodstream. Depending on the applied concentrations, selective tumor cell killing seems feasible. Furthermore, the combinational treatment of tumor cells with radiotherapy and ZnO NPs shows promising results. Still, further investigations are needed to gain a better understanding of the interaction between ZnO NPs and the human body to be able to pave the way for their application as an innovative anti-tumor agent in the clinics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metal%20oxide%20nanoparticles" title="metal oxide nanoparticles">metal oxide nanoparticles</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomedicine" title=" nanomedicine"> nanomedicine</a>, <a href="https://publications.waset.org/abstracts/search?q=overcome%20resistances%20against%20classical%20treatment%20options" title=" overcome resistances against classical treatment options"> overcome resistances against classical treatment options</a>, <a href="https://publications.waset.org/abstracts/search?q=zinc%20oxide%20nanoparticles" title=" zinc oxide nanoparticles"> zinc oxide nanoparticles</a> </p> <a href="https://publications.waset.org/abstracts/126048/zinc-oxide-nanoparticles-as-support-for-classical-anti-cancer-therapies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126048.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">128</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">779</span> Iterative Method for Lung Tumor Localization in 4D CT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarah%20K.%20Hagi">Sarah K. Hagi</a>, <a href="https://publications.waset.org/abstracts/search?q=Majdi%20Alnowaimi"> Majdi Alnowaimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20algorithm" title="automated algorithm ">automated algorithm </a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20tumor" title=" lung tumor"> lung tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20localization" title=" tumor localization"> tumor localization</a> </p> <a href="https://publications.waset.org/abstracts/34015/iterative-method-for-lung-tumor-localization-in-4d-ct" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34015.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">602</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">778</span> Co-Registered Identification and Treatment of Skin Tumor with Optical Coherence Tomography-Guided Laser Therapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo-Huei%20Huang">Bo-Huei Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Hsun%20Yang"> Chih-Hsun Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng-Tsan%20Tsai"> Meng-Tsan Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical coherence tomography (OCT) enables to provide advantages of noninvasive imaging, high resolution, and high imaging speed. In this study, we integrated OCT and a CW laser for tumor diagnosis and treatment. The axial and transverse resolutions of the developed OCT system are 3 μm and 1 μm, respectively. The frame rate of OCT system is 30 frames/s. In this study, the tumor cells were implanted into the mice skin and scanned by OCT to observe the morphological and angiographic changes. With OCT imaging, 3D microstructures and skin angiography of mice skin can be simultaneously acquired, which can be utilized for identification of the tumor distribution. Then, the CW laser beam can be accurately controlled to expose on the center of the tumor, according to the OCT results. Moreover, OCT was used to monitor the induced photothermolysis and to evaluate the treatment outcome. The results showed that OCT-guided laser therapy could efficiently improve the treatment outcome and the extra damage induced by CW can be greatly reduced. Such OCT-guided laser therapy system could be a potential tool for dermatological applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20coherence%20tomography" title="optical coherence tomography">optical coherence tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=laser%20therapy" title=" laser therapy"> laser therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20tumor" title=" skin tumor"> skin tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=position%20guide" title=" position guide"> position guide</a> </p> <a href="https://publications.waset.org/abstracts/55100/co-registered-identification-and-treatment-of-skin-tumor-with-optical-coherence-tomography-guided-laser-therapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55100.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">280</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">777</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">776</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">775</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">774</span> Electro-Thermal Imaging of Breast Phantom: An Experimental Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Feza%20Carlak">H. Feza Carlak</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20G.%20Gencer"> N. G. Gencer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To increase the temperature contrast in thermal images, the characteristics of the electrical conductivity and thermal imaging modalities can be combined. In this experimental study, it is objected to observe whether the temperature contrast created by the tumor tissue can be improved just due to the current application within medical safety limits. Various thermal breast phantoms are developed to simulate the female breast tissue. In vitro experiments are implemented using a thermal infrared camera in a controlled manner. Since experiments are implemented in vitro, there is no metabolic heat generation and blood perfusion. Only the effects and results of the electrical stimulation are investigated. Experimental study is implemented with two-dimensional models. Temperature contrasts due to the tumor tissues are obtained. Cancerous tissue is determined using the difference and ratio of healthy and tumor images. 1 cm diameter single tumor tissue causes almost 40 °mC temperature contrast on the thermal-breast phantom. Electrode artifacts are reduced by taking the difference and ratio of background (healthy) and tumor images. Ratio of healthy and tumor images show that temperature contrast is increased by the current application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20diagnostic%20imaging" title="medical diagnostic imaging">medical diagnostic imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20phantom" title=" breast phantom"> breast phantom</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20thermography" title=" active thermography"> active thermography</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20detection" title=" breast cancer detection"> breast cancer detection</a> </p> <a href="https://publications.waset.org/abstracts/7912/electro-thermal-imaging-of-breast-phantom-an-experimental-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7912.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">428</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">773</span> Consolidated Predictive Model of the Natural History of Breast Cancer Considering Primary Tumor and Secondary Distant Metastases Growth</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ella%20Tyuryumina">Ella Tyuryumina</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexey%20Neznanov"> Alexey Neznanov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20growth%20model" title=" exponential growth model"> exponential growth model</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title=" mathematical model"> mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=metastases%20in%20lymph%20nodes" title=" metastases in lymph nodes"> metastases in lymph nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20tumor" title=" primary tumor"> primary tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=survival" title=" survival"> survival</a> </p> <a href="https://publications.waset.org/abstracts/65182/consolidated-predictive-model-of-the-natural-history-of-breast-cancer-considering-primary-tumor-and-secondary-distant-metastases-growth" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65182.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> <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=tumor&page=2">2</a></li> <li class="page-item"><a class="page-link" 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