Lung cancer screening with CT: Evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels

Christe, Andreas; Leidolt, Lars; Huber, Adrian Thomas; Steiger, Philipp; Szücs-Farkas, Zsolt; Roos, Justus E.; Heverhagen, Johannes T.; Ebner, Lukas

doi:10.1016/j.ejrad.2013.08.026

Cited by 55 publications

(33 citation statements)

References 28 publications

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“…The detection rates in this study were similar to a prior lung phantom study that showed a detection rate of 91% for standard CT acquisition and 97% of standard acquisition with CAD [18], and also to studies investigating CT acquisitions of real patients, such as the study of Veronesi et al that showed a sensitivity of 90% with a standard lung CT protocol [27]. A recent study by Doo et al, using exactly the same anthropomorphic chest phantom as the present study, investigated the detectability of 5 mm and 8 mm ground-glass nodules (-630 and -800 HU) with a low-dose acquisition and iterative construction (effective does of 0.47 mSv) [28].…”

Section: Discussionsupporting

confidence: 86%

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Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging

et al. 2016

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Section: Discussionsupporting

confidence: 86%

“…Various secondary read-out tools exist that can increase the detection rate for small lung nodules, such as the use of maximum intensity projections (MIP) [17] or computer-aided detection (CAD) software [18,19].…”

Section: Introductionmentioning

confidence: 99%

Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging

et al. 2016

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“…In this method, anthropomorphic lung phantom and artificial lung nodules were used and studied to simulate screening CT-examination at standard dose and 8 different low dose levels. Two radiologists and 3 different computer aided detection software (CAD) were paired to find the highest sensitivity [18]. Qingzhu Wang et al (2013) detected lung nodules using computeraided detection by SVM based on 3D matrix patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

Rezaie¹,

Habiboghli²

2017

IJIMAI

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“…One way to overcome this limitation is with the use of a lung phantom containing artificial nodules. 9,10 Images of the phantom acquired with the same scan/reconstruction parameters as those used for screening are then subjected to CAD. This approach is effective for assessing site-specific CAD performance.…”

Section: Introductionmentioning

confidence: 99%

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

Kobayashi¹,

Ohkubo²,

Narita³

et al. 2017

The British Journal of Radiology

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Cite this article as: Kobayashi H, Ohkubo M, Narita A, Marasinghe JC, Murao K, Matsumoto T, et al. A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density. Br J Radiol 2017; 90: 20160313. FULL PAPERA method for evaluating the performance of computeraided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density 1,2 HAJIME KOBAYASHI, MS Objective: We propose the application of virtual nodules to evaluate the performance of computer-aided detection (CAD) of lung nodules in cancer screening using lowdose CT. Methods: The virtual nodules were generated based on the spatial resolution measured for a CT system used in an institution providing cancer screening and were fused into clinical lung images obtained at that institution, allowing site specificity. First, we validated virtual nodules as an alternative to artificial nodules inserted into a phantom. In addition, we compared the results of CAD analysis between the real nodules (n 5 6) and the corresponding virtual nodules. Subsequently, virtual nodules of various sizes and contrasts between nodule density and background density (DCT) were inserted into clinical images (n 5 10) and submitted for CAD analysis.Results: In the validation study, 46 of 48 virtual nodules had the same CAD results as artificial nodules (kappa coefficient 5 0.913). Real nodules and the corresponding virtual nodules showed the same CAD results. The detection limits of the tested CAD system were determined in terms of size and density of peripheral lung nodules; we demonstrated that a nodule with a 5-mm diameter was detected when the nodule had a DCT . 220 HU. Conclusion: Virtual nodules are effective in evaluating CAD performance using site-specific scan/reconstruction conditions. Advances in knowledge: Virtual nodules can be an effective means of evaluating site-specific CAD performance. The methodology for guiding the detection limit for nodule size/density might be a useful evaluation strategy. INTRODUCTIONScreening by low-dose CT has been shown to reduce mortality from lung cancer in high-risk individuals.

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Lung cancer screening with CT: Evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels

Cited by 55 publications

References 28 publications

Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging

Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density

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