Literature DB >> 14752180

Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance.

Kazuo Awai1, Kohei Murao, Akio Ozawa, Masanori Komi, Haruo Hayakawa, Shinichi Hori, Yasumasa Nishimura.   

Abstract

PURPOSE: To evaluate the effect of computer-aided diagnosis (CAD) on radiologists' detection of pulmonary nodules.
MATERIALS AND METHODS: Fifty chest computed tomographic (CT) examination cases were used. The mean nodule size was 0.81 cm +/- 0.60 (SD) (range, 0.3-2.9 cm). Alternative free-response receiver operating characteristic (ROC) analysis with a continuous rating scale was used to compare the observers' performance in detecting nodules with and without use of CAD. Five board-certified radiologists and five radiology residents participated in an observer performance study. First they were asked to rate the probability of nodule presence without using CAD; then they were asked to rate the probability of nodule presence by using CAD.
RESULTS: For all radiologists, the mean areas under the best-fit alternative free-response ROC curves (Az) without and with CAD were 0.64 +/- 0.08 and 0.67 +/- 0.09, respectively, indicating a significant difference (P <.01). For the five board-certified radiologists, the mean Az values without and with CAD were 0.63 +/- 0.08 and 0.66 +/- 0.09, respectively, indicating a significant difference (P <.01). For the five resident radiologists, the mean Az values without and with CAD were 0.66 +/- 0.04 and 0.68 +/- 0.04, respectively, indicating a significant difference (P =.02). At observer performance analyses, there were no significant differences in Az values obtained either without (P =.61) or with (P =.88) CAD between the board-certified radiologists and the residents. For all radiologists, in the detection of pulmonary nodules 1.0 cm in diameter or smaller, the mean Az values without and with CAD were 0.60 +/- 0.11 and 0.64 +/- 0.11, respectively, indicating a significant difference (P <.01).
CONCLUSION: Use of the CAD system improved the board-certified radiologists' and residents' detection of pulmonary nodules at chest CT. Copyright RSNA, 2004

Mesh:

Year:  2004        PMID: 14752180     DOI: 10.1148/radiol.2302030049

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  56 in total

1.  Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists.

Authors:  Katharina Marten; Tobias Seyfarth; Florian Auer; Edzard Wiener; Andreas Grillhösl; Silvia Obenauer; Ernst J Rummeny; Christoph Engelke
Journal:  Eur Radiol       Date:  2004-07-03       Impact factor: 5.315

2.  Detection of noncalcified pulmonary nodules on low-dose MDCT: comparison of the sensitivity of two CAD systems by using a double reference standard.

Authors:  A R Larici; M Amato; P Ordóñez; F Maggi; L Menchini; A Caulo; L Calandriello; G Vallati; S Giunta; M Crecco; L Bonomo
Journal:  Radiol Med       Date:  2012-02-10       Impact factor: 3.469

3.  Evaluation of a method of computer-aided detection (CAD) of pulmonary nodules with computed tomography.

Authors:  G Foti; N Faccioli; M D'Onofrio; A Contro; T Milazzo; R Pozzi Mucelli
Journal:  Radiol Med       Date:  2010-06-23       Impact factor: 3.469

4.  Computer-aided interpretation approach for optical tomographic images.

Authors:  Christian D Klose; Alexander D Klose; Uwe J Netz; Alexander K Scheel; Jurgen Beuthan; Andreas H Hielscher
Journal:  J Biomed Opt       Date:  2010 Nov-Dec       Impact factor: 3.170

5.  Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices.

Authors:  Ray Valencia; Timm Denecke; Lukas Lehmkuhl; Frank Fischbach; Roland Felix; Friedrich Knollmann
Journal:  Eur Radiol       Date:  2005-08-16       Impact factor: 5.315

Review 6.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

7.  Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images, maximum intensity projections, and computer-assisted detection.

Authors:  A Jankowski; T Martinelli; J F Timsit; C Brambilla; F Thony; M Coulomb; G Ferretti
Journal:  Eur Radiol       Date:  2007-09-01       Impact factor: 5.315

Review 8.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

9.  Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.

Authors:  Hao Han; Lihong Li; Fangfang Han; Bowen Song; William Moore; Zhengrong Liang
Journal:  IEEE J Biomed Health Inform       Date:  2014-06-04       Impact factor: 5.772

10.  Ultra-low-dose MDCT of the chest: influence on automated lung nodule detection.

Authors:  Ji Young Lee; Myung Jin Chung; Chin A Yi; Kyung Soo Lee
Journal:  Korean J Radiol       Date:  2008 Mar-Apr       Impact factor: 3.500

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