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Estimation of Non-Small Cell Lung Carcinoma Gross Target Volume Using Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography

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dc.contributor.author Awadain, Sami Yahia Ibrahim
dc.contributor.author Supervisor, -Mohammed Elfadil Mohamed Gar Elnabi
dc.date.accessioned 2022-12-05T10:33:05Z
dc.date.available 2022-12-05T10:33:05Z
dc.date.issued 2021-10-17
dc.identifier.citation Awadain, Sami Yahia Ibrahim .Estimation of Non-Small Cell Lung Carcinoma Gross Target Volume Using Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography \ Sami Yahia Ibrahim Awadain ; Mohammed Elfadil Mohamed Gar Elnabi .- Khartoum:Sudan University of Science & Technology,College of Medical Radiologic Science,2021.- 153 p.:ill.;28cm.-Ph.D en_US
dc.identifier.uri https://repository.sustech.edu:8443/handle/123456789/27872
dc.description Thesis en_US
dc.description.abstract The aim of the study is to estimate and identify the GTV of NSCLC with fluorine- 18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) scan instead of using the CT imaging, The PET scan so useful in estimation of sub mucosal extension of the tumor as well as in staging and restaging of lung carcinomas and changing in gross target volume either positively (+) or negatively (-). This study was conducted in Kuwait Cancer Control Center (kccc) in Kuwait City, during the period 2016 to 2018. The study sample included 156 patients with non- small cell lung carcinoma underwent whole body PET/CT scan. The features extracted from PET/CT images using Second order statistic and All these features were calculated for all images and then the data were ready for discrimination which was performed using step-wise technique in order to select the most significant feature that can be used to classify the lung cells from PET/CT images and the results show that The classification showed that the lung cells were classified well from the rest of the tissues although it has characteristics mostly similar to surrounding tissue. The classification score matrix generated by linear discriminate analysis and the overall classification accuracy of lung cells 96.0%, were the classification accuracy of cardiac 91.6%, lung accuracy 100%, the tumor 99.6%, While the submucosal showed a classification accuracy of 91.2%. Texture analysis depending on the relative attenuation coefficient of tissues and can used to avoid invasive technique if the base line for individual tissues being determined and algorithmic aided computer have been applied. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science & Technology en_US
dc.subject Medical Radiologic Sciences en_US
dc.subject Medical Radiation Physics en_US
dc.subject Non-Small Cell Lung Carcinoma en_US
dc.subject Gross Target Volume en_US
dc.subject Fluorine-18 Fluorodeoxyglucose en_US
dc.subject Positron Emission Tomography en_US
dc.title Estimation of Non-Small Cell Lung Carcinoma Gross Target Volume Using Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography en_US
dc.title.alternative تقدير الحجم الاجمالي المستهدف لسرطان الرئة ذو الخلايا غير الصغيرة باستخدام فحص التصىير المقطعي بالانبعاث البىزيتروني بالفلور en_US
dc.type Thesis en_US


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