Electronic nose technology for detecting lung cancer patients in an Egyptian population = (Titelsatznr. 59425)
[ MARC ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 08157nam a22002417a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 181226s2016 ua a|||g bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | SOUL |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | ara |
| Language code of original | eng |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 21 |
| Classification number | 612.014 |
| Item number | E F |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 4817 |
| Personal name | Abdel Mohdy, Taher Saeed. |
| 245 10 - TITLE STATEMENT | |
| Title | Electronic nose technology for detecting lung cancer patients in an Egyptian population = |
| Remainder of title | استخدام تقنية الأنف إلكترونى فى اكتشاف مرضى سرطان الرئة فى مجموعة من المصريين \ |
| Statement of responsibility, etc. | Taher Saeed Abdel Mohdy ; Supervised by Ehab Ibrahim Abdou Mohamed, Hanaa Ahmed Shafiq. |
| 246 03 - VARYING FORM OF TITLE | |
| Title proper/short title | استخدام تقنية الأنف إلكترونى فى اكتشاف مرضى سرطان الرئة فى مجموعة من المصريين |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Alexandria : |
| Name of publisher, distributor, etc. | Alexandria University.Medical Research Institute.Department of Medical Biophysics. |
| Date of publication, distribution, etc. | 2016.. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 96 p. : |
| Other physical details | ill. ; |
| Dimensions | 27 cm + |
| Accompanying material | 1 CD. |
| 500 ## - GENERAL NOTE | |
| General note | Thesis(M.S.)-Alexandria University.Medical Research Institute.Department of Medical Biophysics. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references, index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | the Arab league countries nationals, of which 13,826 cases (79.7%) were males and 2,806 (20.3%) were females. The majority of cases were reported in Arab countries in North Africa such as Egypt (20.6%). LC is the one of the most causes of death in the global population. The disease has become an epidemic as LC death rates have risen dramatically over the last century. Worldwide, there are estimated to be 1.8 million new cases in 2012 (12.9 of the total). It was a rare disease at the start of the 20th century, but being vulnerable to new causes and an increasing life span combined to make LC the most common cancer in the world for several decades. (6) Patients with LC present with: persistent cough, dyspnea, wheeze, hemoptysis (coughing up blood for several days in succession), chest or shoulder pain and discomfort. Adding that, more than half of all patients diagnosed with lung cancer present with advanced disease at the time of diagnosis. The main diagnostic procedures for lung cancer are chest radiography, chest computed tomography (CT)/ PET scan, sputum cytology or pleural fluid cytology and bronchoscopy. Biopsy for histopathological evaluation is the gold standard technique for diagnosing lung cancer. (64) In the past, screening chest X-rays had been evaluated in the effort of early detection of LC in high-risk patients, but no decline in mortality rates has been achieved. Screening low dose CT had been evaluated because of its higher sensitivity; nevertheless, CT may produce false positive results (For each true positive scan, there are about 19 false positive scans), which increase the number of unnecessary invasive diagnostic procedures needed to verify the CT findings. Sputum cytology screening test had not been found to be of diagnostic relevance as well and with no proven effect of screening on mortality rates, there is concern that screening may cause over-diagnosis, unnecessary anxiety, radiation exposure and expense. In order to increase the chances of a successful treatment and prevent the consequences of the disease, LC must be diagnosed as early as possible. (64) There is a need to develop rapid, cheap, convenient, and accurate tests for the diagnosis of infectious diseases, to initiate rapid pathogen detection and subsequent specific treatment. An Electronic Nose is a device use to perceive odors or flavors, likewise the human olfactory system, as a global fingerprint which consists of three essential elements: an array of olfactory receptor cells situated in the roof of the nasal cavity, the olfactory bulb which is situated just above the nasal cavity; and the brain, in an e-nose there are three components; a) a sampling conditioning unit, which delivers the odor volatiles from the headspace above the sample; b) a test chamber in which the sensor array is based; and c) a processing unit which analyzes the sensor responses for pattern recognition. (56) An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an ”expert” in the category of information it had been given to analyze. This expert can then be used to provide projections given new situations of interest. (49) In the present study we applied the E-Nose technology to measure qualitative odor differences of blood, breath, urine from HC participants and LC patients, biopsy samples from histopathologically proven Egyptian patients with LC to estimate the data using an ANN for the classification of all participants and analysis of output results and diagnosis. The results of the blood, breath, urine and biopsy samples of the all study groups were analyzed by the portable E-Nose PEN3 as qualitative analysis and sensor response patterns of the E-Nose were processed by using the Winmuster software package (Version 1.6.2.2, Airsense Analytics GmbH, Schwerin, Germany) for multidimensional plots and further analysis using PCA and LDA classification among LC patients and HC participants. A clear distinction between both groups is evident in Figure (30) A through C for the same biological samples when plotted together, where both principal components #1 and #2 show that variance in signals are 95.46, 82.01, and 91.66%, respectively, which means that E-Nose was capable of identifying samples from each group with less false-positive LC or false-negative HC results. This lends to support the fact that VOCs found in the blood, breath, urine of LC patients are significantly different from those for HC participants. A clear distinction between both groups is evident in Figure (33) A through C for the same biological samples when plotted together, where both Linear discriminant #1 and #2 show that variance in signals are 32.29, 38.86 and 41.22 % respectively, which means that E-Nose was capable of identifying samples from each group with less false-positive HC or false-negative LC results. This also lends to support the fact that the VOCs found in the blood, breath, and urine of LC patients are significantly different from those for HC participants. Matlab Neural Network Pattern Recognition Toolbox version (R2015a) is used to predict and classify and recognize patterns. The confusion matrix, ROC, and performance plot of the results are displayed, the correct predictions together with the percentage of output correct classification, which reached almost 100% for all group samples, with less misclassification. Based on these features, the predictive accuracy, sensitivity, specificity of the proposed diagnosis model of the ANN reached a good percentage with less false positive and less false negative results. That is accurately identified LC Patients from HC participants with great precision, for all measured blood, urine, and breath samples. |
| 546 ## - LANGUAGE NOTE | |
| Language note | English Text and abstracts in Arabic and English. |
| 650 40 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4818 |
| Topical term or geographic name as entry element | Medical Biophysics. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 4819 |
| Personal name | Mohamed, Ehab Ibrahim Abdou |
| Relator term | supervisor |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Alexandria University. |
| Subordinate unit | Medical Research Institute. |
| -- | Department of Medical Biophysics. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Theses |
| Item part | Faculty of Medicine كلية الطب |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent location | Current location | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6october | 6october | 1208 | 2019-01-02 | 612.014 E F | SOULE208TH0769 | 2021-08-25 | 2019-01-02 | Theses |