Literature DB >> 18496038

Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs.

Thorsten Alexander Bley1, Tobias Baumann, Ulrich Saueressig, Gregor Pache, Markus Treier, Oliver Schaefer, Ulrich Neitzel, Mathias Langer, Elmar Kotter.   

Abstract

OBJECTIVES: Detection of subtle pulmonary nodules on digital radiography is a challenging task for radiologists. The aim of this study was to evaluate the performance of a newly approved computer aided detection (CAD) system.
MATERIALS AND METHODS: The sensitivity of 3 radiologists and of a CAD system for the detection of pulmonary nodules from 5 to 15 mm in size on digital chest radiography of 117 patients was compared. The reference standard was established by consensus reading of computed tomography scans by 2 experienced radiologists. Computed tomography scans and chest radiographs were performed within 4 weeks. Sixty-six pulmonary nodules from 42 patients, with a mean nodule diameter of 7.5 mm (standard deviation: 2.2 mm), were included in the statistical analysis. Seventy-five of the 117 patients did not have nodules from 5 to 15 mm of size.
RESULTS: Two hundred and eighty-eight false-positive detections of the CAD system were found with an average of 2.5 false-positives per image. Sensitivity of the CAD system was 39.4% (95% confidence interval: 11.8%), when compared with 18.2% to 30.3% (95% confidence interval 9.3% to 11.1%) of the 3 radiologists. Substantial agreement for nodule detection ([kappa]N: 0.64-0.73) was found among the 3 radiologists, whereas only moderate agreement was found between the radiologists and the CAD performance ([kappa]N: 0.45-0.52).
CONCLUSIONS: The CAD system's diagnostic sensitivity in detecting pulmonary nodules of 5 to 15 mm of size was superior to the 1 of radiologists. The CAD system may be used for assisting the radiologist in the detection of lung nodules on digital chest radiographs.

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Year:  2008        PMID: 18496038     DOI: 10.1097/RLI.0b013e318168f705

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  9 in total

1.  Analysis of the impact of digital tomosynthesis on the radiological investigation of patients with suspected pulmonary lesions on chest radiography.

Authors:  Emilio Quaia; Elisa Baratella; Stefano Cernic; Arianna Lorusso; Federica Casagrande; Vincenzo Cioffi; Maria Assunta Cova
Journal:  Eur Radiol       Date:  2012-04-27       Impact factor: 5.315

Review 2.  [Conventional and CT diagnostics of bronchial carcinoma].

Authors:  C Schaefer-Prokop
Journal:  Radiologe       Date:  2010-08       Impact factor: 0.635

3.  Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings.

Authors:  Sohee Park; Sang Min Lee; Kyung Hee Lee; Kyu-Hwan Jung; Woong Bae; Jooae Choe; Joon Beom Seo
Journal:  Eur Radiol       Date:  2019-11-20       Impact factor: 5.315

4.  Computer-aided detection of malignant lung nodules on chest radiographs: effect on observers' performance.

Authors:  Kyung Hee Lee; Jin Mo Goo; Chang Min Park; Hyun Ju Lee; Kwang Nam Jin
Journal:  Korean J Radiol       Date:  2012-08-28       Impact factor: 3.500

5.  Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers.

Authors:  Zsolt Szucs-Farkas; Michael A Patak; Seyran Yuksel-Hatz; Thomas Ruder; Peter Vock
Journal:  Eur Radiol       Date:  2009-11-21       Impact factor: 5.315

6.  Chest tomosynthesis: technical principles and clinical update.

Authors:  James T Dobbins; H Page McAdams
Journal:  Eur J Radiol       Date:  2009-07-18       Impact factor: 3.528

7.  A comparison of computer-aided detection (CAD) effectiveness in pulmonary nodule identification using different methods of bone suppression in chest radiographs.

Authors:  Ronald D Novak; Nicholas J Novak; Robert Gilkeson; Bahar Mansoori; Gunhild E Aandal
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

8.  Observer training for computer-aided detection of pulmonary nodules in chest radiography.

Authors:  Diederick W De Boo; François van Hoorn; Joost van Schuppen; Laura Schijf; Maeke J Scheerder; Nicole J Freling; Onno Mets; Michael Weber; Cornelia M Schaefer-Prokop
Journal:  Eur Radiol       Date:  2012-03-25       Impact factor: 5.315

9.  Diagnostic imaging costs before and after digital tomosynthesis implementation in patient management after detection of suspected thoracic lesions on chest radiography.

Authors:  Emilio Quaia; Guido Grisi; Elisa Baratella; Roberto Cuttin; Gabriele Poillucci; Sara Kus; Maria Assunta Cova
Journal:  Insights Imaging       Date:  2014-01-14
  9 in total

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