Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

Noah Lee, Andrew F. Laine, Jeffrey M. Levsky, John K. Gohagan

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)136-146
Number of pages11
JournalIEEE Reviews in Biomedical Engineering
StatePublished - 2009


  • Computer-aided diagnosis
  • lung cancer screening
  • machine learning
  • receiver operating characteristics

ASJC Scopus subject areas

  • Biomedical Engineering


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