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Efstathopoulos</title> <meta name="description" content="Search results for: Efstathios Efstathopoulos"> <meta name="keywords" content="Efstathios Efstathopoulos"> <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 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Efstathios Efstathopoulos"> <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> 6</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Efstathios Efstathopoulos</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Leveraging Multimodal Neuroimaging Techniques to in vivo Address Compensatory and Disintegration Patterns in Neurodegenerative Disorders: Evidence from Cortico-Cerebellar Connections in Multiple Sclerosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Efstratios%20Karavasilis">Efstratios Karavasilis</a>, <a href="https://publications.waset.org/abstracts/search?q=Foteini%20Christidi"> Foteini Christidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgios%20Velonakis"> Georgios Velonakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Agapi%20Plousi"> Agapi Plousi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalliopi%20Platoni"> Kalliopi Platoni</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolaos%20Kelekis"> Nikolaos Kelekis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Evdokimidis"> Ioannis Evdokimidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Efstathios%20Efstathopoulos"> Efstathios Efstathopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Advanced structural and functional neuroimaging techniques contribute to the study of anatomical and functional brain connectivity and its role in the pathophysiology and symptoms’ heterogeneity in several neurodegenerative disorders, including multiple sclerosis (MS). Aim: In the present study, we applied multiparametric neuroimaging techniques to investigate the structural and functional cortico-cerebellar changes in MS patients. Material: We included 51 MS patients (28 with clinically isolated syndrome [CIS], 31 with relapsing-remitting MS [RRMS]) and 51 age- and gender-matched healthy controls (HC) who underwent MRI in a 3.0T MRI scanner. Methodology: The acquisition protocol included high-resolution 3D T1 weighted, diffusion-weighted imaging and echo planar imaging sequences for the analysis of volumetric, tractography and functional resting state data, respectively. We performed between-group comparisons (CIS, RRMS, HC) using CAT12 and CONN16 MATLAB toolboxes for the analysis of volumetric (cerebellar gray matter density) and functional (cortico-cerebellar resting-state functional connectivity) data, respectively. Brainance suite was used for the analysis of tractography data (cortico-cerebellar white matter integrity; fractional anisotropy [FA]; axial and radial diffusivity [AD; RD]) to reconstruct the cerebellum tracts. Results: Patients with CIS did not show significant gray matter (GM) density differences compared with HC. However, they showed decreased FA and increased diffusivity measures in cortico-cerebellar tracts, and increased cortico-cerebellar functional connectivity. Patients with RRMS showed decreased GM density in cerebellar regions, decreased FA and increased diffusivity measures in cortico-cerebellar WM tracts, as well as a pattern of increased and mostly decreased functional cortico-cerebellar connectivity compared to HC. The comparison between CIS and RRMS patients revealed significant GM density difference, reduced FA and increased diffusivity measures in WM cortico-cerebellar tracts and increased/decreased functional connectivity. The identification of decreased WM integrity and increased functional cortico-cerebellar connectivity without GM changes in CIS and the pattern of decreased GM density decreased WM integrity and mostly decreased functional connectivity in RRMS patients emphasizes the role of compensatory mechanisms in early disease stages and the disintegration of structural and functional networks with disease progression. Conclusions: In conclusion, our study highlights the added value of multimodal neuroimaging techniques for the in vivo investigation of cortico-cerebellar brain changes in neurodegenerative disorders. An extension and future opportunity to leverage multimodal neuroimaging data inevitably remain the integration of such data in the recently-applied mathematical approaches of machine learning algorithms to more accurately classify and predict patients’ disease course. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20neuroimaging%20techniques" title="advanced neuroimaging techniques">advanced neuroimaging techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=cerebellum" title=" cerebellum"> cerebellum</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20sclerosis" title=" multiple sclerosis"> multiple sclerosis</a> </p> <a href="https://publications.waset.org/abstracts/89592/leveraging-multimodal-neuroimaging-techniques-to-in-vivo-address-compensatory-and-disintegration-patterns-in-neurodegenerative-disorders-evidence-from-cortico-cerebellar-connections-in-multiple-sclerosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89592.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">140</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">5</span> Raman Spectral Fingerprints of Healthy and Cancerous Human Colorectal Tissues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Karnachoriti">Maria Karnachoriti</a>, <a href="https://publications.waset.org/abstracts/search?q=Ellas%20Spyratou"> Ellas Spyratou</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Lykidis"> Dimitrios Lykidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Lambropoulou"> Maria Lambropoulou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiannis%20S.%20Raptis"> Yiannis S. Raptis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Seimenis"> Ioannis Seimenis</a>, <a href="https://publications.waset.org/abstracts/search?q=Efstathios%20P.%20Efstathopoulos"> Efstathios P. Efstathopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Athanassios%20G.%20Kontos"> Athanassios G. Kontos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colorectal cancer is the third most common cancer diagnosed in Europe, according to the latest incidence data provided by the World Health Organization (WHO), and early diagnosis has proved to be the key in reducing cancer-related mortality. In cases where surgical interventions are required for cancer treatment, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. The current study focuses on the ex vivo handling of surgically excised colorectal specimens and the acquisition of their spectral fingerprints using Raman spectroscopy. Acquired data were analyzed in an effort to discriminate, in microscopic scale, between healthy and malignant margins. Raman spectroscopy is a spectroscopic technique with high detection sensitivity and spatial resolution of few micrometers. The spectral fingerprint which is produced during laser-tissue interaction is unique and characterizes the biostructure and its inflammatory or cancer state. Numerous published studies have demonstrated the potential of the technique as a tool for the discrimination between healthy and malignant tissues/cells either ex vivo or in vivo. However, the handling of the excised human specimens and the Raman measurement conditions remain challenging, unavoidably affecting measurement reliability and repeatability, as well as the technique’s overall accuracy and sensitivity. Therefore, tissue handling has to be optimized and standardized to ensure preservation of cell integrity and hydration level. Various strategies have been implemented in the past, including the use of balanced salt solutions, small humidifiers or pump-reservoir-pipette systems. In the current study, human colorectal specimens of 10X5 mm were collected from 5 patients up to now who underwent open surgery for colorectal cancer. A novel, non-toxic zinc-based fixative (Z7) was used for tissue preservation. Z7 demonstrates excellent protein preservation and protection against tissue autolysis. Micro-Raman spectra were recorded with a Renishaw Invia spectrometer from successive random 2 micrometers spots upon excitation at 785 nm to decrease fluorescent background and secure avoidance of tissue photodegradation. A temperature-controlled approach was adopted to stabilize the tissue at 2 °C, thus minimizing dehydration effects and consequent focus drift during measurement. A broad spectral range, 500-3200 cm-1,was covered with five consecutive full scans that lasted for 20 minutes in total. The average spectra were used for least square fitting analysis of the Raman modes.Subtle Raman differences were observed between normal and cancerous colorectal tissues mainly in the intensities of the 1556 cm-1 and 1628 cm-1 Raman modes which correspond to v(C=C) vibrations in porphyrins, as well as in the range of 2800-3000 cm-1 due to CH2 stretching of lipids and CH3 stretching of proteins. Raman spectra evaluation was supported by histological findings from twin specimens. This study demonstrates that Raman spectroscopy may constitute a promising tool for real-time verification of clear margins in colorectal cancer open surgery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colorectal%20cancer" title="colorectal cancer">colorectal cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=Raman%20spectroscopy" title=" Raman spectroscopy"> Raman spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=malignant%20margins" title=" malignant margins"> malignant margins</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20fingerprints" title=" spectral fingerprints"> spectral fingerprints</a> </p> <a href="https://publications.waset.org/abstracts/137104/raman-spectral-fingerprints-of-healthy-and-cancerous-human-colorectal-tissues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137104.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">91</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">4</span> A Novel Framework for User-Friendly Ontology-Mediated Access to Relational Databases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Efthymios%20Chondrogiannis">Efthymios Chondrogiannis</a>, <a href="https://publications.waset.org/abstracts/search?q=Vassiliki%20Andronikou"> Vassiliki Andronikou</a>, <a href="https://publications.waset.org/abstracts/search?q=Efstathios%20Karanastasis"> Efstathios Karanastasis</a>, <a href="https://publications.waset.org/abstracts/search?q=Theodora%20Varvarigou"> Theodora Varvarigou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontologies" title="ontologies">ontologies</a>, <a href="https://publications.waset.org/abstracts/search?q=relational%20databases" title=" relational databases"> relational databases</a>, <a href="https://publications.waset.org/abstracts/search?q=SPARQL" title=" SPARQL"> SPARQL</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20interface" title=" web interface"> web interface</a> </p> <a href="https://publications.waset.org/abstracts/21795/a-novel-framework-for-user-friendly-ontology-mediated-access-to-relational-databases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21795.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">272</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">3</span> Different Types of Amyloidosis Revealed with Positive Cardiac Scintigraphy with Tc-99M DPD-SPECT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Panagiotopoulos">Ioannis Panagiotopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Efstathios%20Kastritis"> Efstathios Kastritis</a>, <a href="https://publications.waset.org/abstracts/search?q=Anastasia%20Katinioti"> Anastasia Katinioti</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgios%20Efthymiadis"> Georgios Efthymiadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Argyrios%20Doumas"> Argyrios Doumas</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Koutelou"> Maria Koutelou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Transthyretin amyloidosis (ATTR) is a rare but serious infiltrative disease. Myocardial scintigraphy with DPD has emerged as the most effective, non-invasive, highly sensitive, and highly specific diagnostic method for cardiac ATTR amyloidosis. However, there are cases in which additional laboratory investigations reveal AL amyloidosis or other diseases despite a positive DPD scintigraphy. We describe the experience from the Onassis Cardiac Surgery Center and the monitoring center for infiltrative myocardial diseases of the cardiology clinic at AHEPA. Materials and Methods: All patients with clinical suspicion of cardiac or extracardiac amyloidosis undergo a myocardial scintigraphy scan with Tc-99m DPD. In this way, over 500 patients have been examined. Further diagnostic approach based on clinical and imaging findings includes laboratory investigation and invasive techniques (e.g., biopsy). Results: Out of 76 patients in total with positive myocardial scintigraphy Grade 2 or 3 according to the Perugini scale, 8 were proven to suffer from AL Amyloidosis during the investigation of paraproteinemia. Among these patients, 3 showed Grade 3 uptake, while the rest were graded as Grade 2, or 2 to 3. Additionally, one patient presented diffuse and unusual radiopharmaceutical uptake in soft tissues throughout the body without cardiac involvement. These findings raised suspicions, leading to the analysis of κ and λ light chains in the serum, as well as immunostaining of proteins in the serum and urine of these specific patients. The final diagnosis was AL amyloidosis. Conclusion: The value of DPD scintigraphy in the diagnosis of cardiac amyloidosis from transthyretin is undisputed. However, positive myocardial scintigraphy with DPD should not automatically lead to the diagnosis of ATTR amyloidosis. Laboratory differentiation between ATTR and AL amyloidosis is crucial, as both prognosis and therapeutic strategy are dramatically altered. Laboratory exclusion of paraproteinemia is a necessary and essential step in the diagnostic algorithm of ATTR amyloidosis for all positive myocardial scintigraphy with diphosphonate tracers since >20% of patients with Grade 3 and 2 uptake may conceal AL amyloidosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AL%20amyloidosis" title="AL amyloidosis">AL amyloidosis</a>, <a href="https://publications.waset.org/abstracts/search?q=amyloidosis" title=" amyloidosis"> amyloidosis</a>, <a href="https://publications.waset.org/abstracts/search?q=ATTR" title=" ATTR"> ATTR</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20scintigraphy" title=" myocardial scintigraphy"> myocardial scintigraphy</a>, <a href="https://publications.waset.org/abstracts/search?q=Tc-99m%20DPD" title=" Tc-99m DPD"> Tc-99m DPD</a> </p> <a href="https://publications.waset.org/abstracts/176715/different-types-of-amyloidosis-revealed-with-positive-cardiac-scintigraphy-with-tc-99m-dpd-spect" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176715.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">81</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">2</span> Experience of Two Major Research Centers in the Diagnosis of Cardiac Amyloidosis from Transthyretin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Panagiotopoulos">Ioannis Panagiotopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Aristidis%20Anastasakis"> Aristidis Anastasakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Toutouzas"> Konstantinos Toutouzas</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Iakovou"> Ioannis Iakovou</a>, <a href="https://publications.waset.org/abstracts/search?q=Charalampos%20Vlachopoulos"> Charalampos Vlachopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasilis%20Voudris"> Vasilis Voudris</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgios%20Tziomalos"> Georgios Tziomalos</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Tsioufis"> Konstantinos Tsioufis</a>, <a href="https://publications.waset.org/abstracts/search?q=Efstathios%20Kastritis"> Efstathios Kastritis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandros%20Briassoulis"> Alexandros Briassoulis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kimon%20Stamatelopoulos"> Kimon Stamatelopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexios%20Antonopoulos"> Alexios Antonopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Paraskevi%20Exadaktylou"> Paraskevi Exadaktylou</a>, <a href="https://publications.waset.org/abstracts/search?q=Evanthia%20Giannoula"> Evanthia Giannoula</a>, <a href="https://publications.waset.org/abstracts/search?q=Anastasia%20Katinioti"> Anastasia Katinioti</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Kalantzi"> Maria Kalantzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Evangelos%20Leontiadis"> Evangelos Leontiadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Eftychia%20Smparouni"> Eftychia Smparouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Malakos"> Ioannis Malakos</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolaos%20Aravanis"> Nikolaos Aravanis</a>, <a href="https://publications.waset.org/abstracts/search?q=Argyrios%20Doumas"> Argyrios Doumas</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Koutelou"> Maria Koutelou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Cardiac amyloidosis from Transthyretin (ATTR-CA) is an infiltrative disease characterized by the deposition of pathological transthyretin complexes in the myocardium. This study describes the characteristics of patients diagnosed with ATTR-CA from 2019 until present at the Nuclear Medicine Department of Onassis Cardiac Surgery Center and AHEPA Hospital. These centers have extensive experience in amyloidosis and modern technological equipment for its diagnosis. Materials and Methods: Records of consecutive patients (N=73) diagnosed with any type of amyloidosis were collected, analyzed, and prospectively followed. The diagnosis of amyloidosis was made using specific myocardial scintigraphy with Tc-99m DPD. Demographic characteristics, including age, gender, marital status, height, and weight, were collected in a database. Clinical characteristics, such as amyloidosis type (ATTR and AL), serum biomarkers (BNP, troponin), electrocardiographic findings, ultrasound findings, NYHA class, aortic valve replacement, device implants, and medication history, were also collected. Some of the most significant results are presented. Results: A total of 73 cases (86% male) were diagnosed with amyloidosis over four years. The mean age at diagnosis was 82 years, and the main symptom was dyspnea. Most patients suffered from ATTR-CA (65 vs. 8 with AL). Out of all the ATTR-CA patients, 61 were diagnosed with wild-type and 2 with two rare mutations. Twenty-eight patients had systemic amyloidosis with extracardiac involvement, and 32 patients had a history of bilateral carpal tunnel syndrome. Four patients had already developed polyneuropathy, and the diagnosis was confirmed by DPD scintigraphy, which is known for its high sensitivity. Among patients with isolated cardiac involvement, only 6 had left ventricular ejection fraction below 40%. The majority of ATTR patients underwent tafamidis treatment immediately after diagnosis. Conclusion: In conclusion, the experiences shared by the two centers and the continuous exchange of information provide valuable insights into the diagnosis and management of cardiac amyloidosis. Clinical suspicion of amyloidosis and early diagnostic approach are crucial, given the availability of non-invasive techniques. Cardiac scintigraphy with DPD can confirm the presence of the disease without the need for a biopsy. The ultimate goal still remains continuous education and awareness of clinical cardiologists so that this systemic and treatable disease can be diagnosed and certified promptly and treatment can begin as soon as possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amyloidosis" title="amyloidosis">amyloidosis</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20scintigraphy" title=" myocardial scintigraphy"> myocardial scintigraphy</a>, <a href="https://publications.waset.org/abstracts/search?q=Tc-99m%20DPD" title=" Tc-99m DPD"> Tc-99m DPD</a>, <a href="https://publications.waset.org/abstracts/search?q=transthyretin" title=" transthyretin"> transthyretin</a> </p> <a href="https://publications.waset.org/abstracts/176716/experience-of-two-major-research-centers-in-the-diagnosis-of-cardiac-amyloidosis-from-transthyretin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176716.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">90</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">1</span> Use of Machine Learning Algorithms to Pediatric MR Images for Tumor Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Stathopoulos">I. Stathopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Syrgiamiotis"> V. Syrgiamiotis</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Karavasilis"> E. Karavasilis</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ploussi"> A. Ploussi</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Nikas"> I. Nikas</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Hatzigiorgi"> C. Hatzigiorgi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Platoni"> K. Platoni</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20P.%20Efstathopoulos"> E. P. Efstathopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title="image classification">image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=pediatric%20MRI" title=" pediatric MRI"> pediatric MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=pediatric%20oncology" title=" pediatric oncology"> pediatric oncology</a> </p> <a href="https://publications.waset.org/abstracts/89712/use-of-machine-learning-algorithms-to-pediatric-mr-images-for-tumor-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89712.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">149</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); 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