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
Link To Document :
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