DocumentCode
1501476
Title
Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening
Author
Lee, Noah ; Laine, Andrew F. ; Marquez, Guillermo ; Levsky, Jeffrey M. ; Gohagan, John K.
Author_Institution
Dept. of Biomed. Eng., Columbia Univ., New York, NY, USA
Volume
2
fYear
2009
fDate
7/1/1905 12:00:00 AM
Firstpage
136
Lastpage
146
Abstract
The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments.
Keywords
cancer; computerised tomography; diagnostic radiography; learning (artificial intelligence); lung; medical diagnostic computing; sensitivity analysis; tumours; CT lung cancer screening; CT sensitivity; computer-aided diagnosis; low-dose spiral computed tomography; machine learning; receiver operating characteristics; Calculus; Cancer detection; Computed tomography; Computer aided diagnosis; Costs; Lungs; Optimized production technology; Sensitivity and specificity; Spirals; Testing; Computer-aided diagnosis; lung cancer screening; machine learning; receiver operating characteristics; Cost-Benefit Analysis; Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Mass Screening; Radiographic Image Interpretation, Computer-Assisted; Risk Assessment; Sensitivity and Specificity; Tomography, Spiral Computed;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Reviews in
Publisher
ieee
ISSN
1937-3333
Type
jour
DOI
10.1109/RBME.2009.2034022
Filename
5288600
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