Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/27872
Title: Estimation of Non-Small Cell Lung Carcinoma Gross Target Volume Using Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography
Other Titles: تقدير الحجم الاجمالي المستهدف لسرطان الرئة ذو الخلايا غير الصغيرة باستخدام فحص التصىير المقطعي بالانبعاث البىزيتروني بالفلور
Authors: Awadain, Sami Yahia Ibrahim
Supervisor, -Mohammed Elfadil Mohamed Gar Elnabi
Keywords: Medical Radiologic Sciences
Medical Radiation Physics
Non-Small Cell Lung Carcinoma
Gross Target Volume
Fluorine-18 Fluorodeoxyglucose
Positron Emission Tomography
Issue Date: 17-Oct-2021
Publisher: Sudan University of Science & Technology
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
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.
Description: Thesis
URI: https://repository.sustech.edu:8443/handle/123456789/27872
Appears in Collections:PhD theses :Medical Radiologic Science

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