Literature DB >> 22275043

Potential of computer-aided diagnosis to improve CT lung cancer screening.

Noah Lee1, Andrew F Laine, Guillermo Márquez, Jeffrey M Levsky, John K Gohagan.   

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.

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Mesh:

Year:  2009        PMID: 22275043     DOI: 10.1109/RBME.2009.2034022

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  2 in total

1.  Illustration of the obstacles in computerized lung segmentation using examples.

Authors:  Xin Meng; Yongqian Qiang; Shaocheng Zhu; Carl Fuhrman; Jill M Siegfried; Jiantao Pu
Journal:  Med Phys       Date:  2012-08       Impact factor: 4.071

Review 2.  Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.

Authors:  Amitava Halder; Debangshu Dey; Anup K Sadhu
Journal:  J Digit Imaging       Date:  2020-06       Impact factor: 4.056

  2 in total

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