Literature DB >> 15537839

Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.

Geoffrey D Rubin1, John K Lyo, David S Paik, Anthony J Sherbondy, Lawrence C Chow, Ann N Leung, Robert Mindelzun, Pamela K Schraedley-Desmond, Steven E Zinck, David P Naidich, Sandy Napel.   

Abstract

PURPOSE: To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans.
MATERIALS AND METHODS: The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader.
RESULTS: The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05).
CONCLUSION: With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading. (c) RSNA, 2004.

Mesh:

Year:  2004        PMID: 15537839     DOI: 10.1148/radiol.2341040589

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


  69 in total

1.  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

2.  Assessing operating characteristics of CAD algorithms in the absence of a gold standard.

Authors:  Kingshuk Roy Choudhury; David S Paik; Chin A Yi; Sandy Napel; Justus Roos; Geoffrey D Rubin
Journal:  Med Phys       Date:  2010-04       Impact factor: 4.071

Review 3.  Content-based image retrieval in radiology: current status and future directions.

Authors:  Ceyhun Burak Akgül; Daniel L Rubin; Sandy Napel; Christopher F Beaulieu; Hayit Greenspan; Burak Acar
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

4.  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

5.  Shape "break-and-repair" strategy and its application to automated medical image segmentation.

Authors:  Jiantao Pu; David S Paik; Xin Meng; Justus E Roos; Geoffrey D Rubin
Journal:  IEEE Trans Vis Comput Graph       Date:  2011-01       Impact factor: 4.579

Review 6.  Management of an incidentally discovered pulmonary nodule.

Authors:  Catherine Beigelman-Aubry; Catherine Hill; Philippe A Grenier
Journal:  Eur Radiol       Date:  2006-10-05       Impact factor: 5.315

7.  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

8.  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

9.  Accuracy of MRI volume measurements of breast lesions: comparison between automated, semiautomated and manual assessment.

Authors:  Marga B Rominger; Daphne Fournell; Beenarose Thanka Nadar; Sarah N M Behrens; Jens H Figiel; Boris Keil; Johannes T Heverhagen
Journal:  Eur Radiol       Date:  2009-01-22       Impact factor: 5.315

Review 10.  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

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