Screening for lung cancer with low-dose spiral computed tomography.

By S. Swensen, J. Jett, J. Sloan, D. Midthun, T. Hartman, A. Sykes, G. Aughenbaugh, F. Zink, S. Hillman, Gayle R. Noetzel and 3 more. American Journal of Respiratory and Critical Care Medicine, 2000

709 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d5328ffa966ed95b15df08e7bb301065125fd407

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. (2004, background): “In a retrospective study of first annual CT examinations, Swensen et al (7) found that nodules were missed in 26% of patients.”
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification (2009, background): “Studies have shown that radiologists frequently fail to detect all visible nodules in CT scans (Gurney, 1996; Kakinuma et al., 1999; Swensen et al., 2002; White et al., 1996).”
Current status and future potential of computer-aided diagnosis in medical imaging. (2005, background): “The false positive rate at screening has been reported as 87–93% with low dose, single detector CT at 10 mm slice thickness [92–94], and 98–99% with single detector or multidetector CT at 5 mm slice thickness [98, 99].”
Machine Learning and Radiology (2012, background): “In current lung cancer diagnosis, computed tomography (CT) screening is a standard procedure which is superior to traditional chest radiography in the detection of lung nodules (potential lung cancers) (Kaneko et al., 1996; Swensen et al., 2002).”