• 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