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Journal of Imaging Informatics in Medicine

Published in:

01-08-2013

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

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2013

Abstract

This study aimed to compare the diagnostic effectiveness of computer-aided detection (CAD) software (OnGuard™ 5.2) in combination with hardware-based bone suppression (dual-energy subtraction radiography (DESR)), software-based bone suppression (SoftView™, version 2.4), and standard posteroanterior images with no bone suppression. A retrospective pilot study compared the diagnostic performance of two commercially available methods of bone suppression when used with commercially available CAD software. Chest images from 27 patients with computed tomography (CT) and pathology-proven malignant pulmonary nodules (8–34 mm) and 25 CT-negative patient controls were used for analysis. The Friedman, McNemar, and chi-square tests were used to compare diagnostic performance and the kappa statistic was used to evaluate method agreement. The average number of regions of interest and false-positives per image identified by CAD were not found to be significantly different regardless of the bone suppression methods evaluated. Similarly, the sensitivity, specificity, and test efficiency were not found to be significantly different. Agreement between the methods was between poor and excellent. The accuracy of CAD (OnGuard™, version 5.2) is not statistically different with either DESR or SoftView™ (version 2.4) bone suppression technology in digital chest images for pulmonary nodule identification. Low values for sensitivity (<80 %) and specificity (<50 %) may limit their utility for clinical radiology.

Kobayashi T, Xu XW, MacMahon H, Metz CE, Doi K: Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs. Radiology. 199(3):843–848, 1996PubMed

Bley TA, Baumann T, Saueressig U, Pache G, Treier M, Schaefer O, et al: Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs. Invest Radiol. 43(6):343–348, 2008PubMedCrossRef

Kasai S, Li F, Shiraishi J, Doi K: Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs. AJR Am J Roentgenol. 191(1):260–265, 2008PubMedCrossRef

Meziane M, Obuchowski NA, Lababede O, Lieber ML, Philips M, Mazzone P: A comparison of follow-up recommendations by chest radiologists, general radiologists, and pulmonologists using computer-aided detection to assess radiographs for actionable pulmonary nodules. AJR Am J Roentgenol. 196(5):W542–W549, 2011PubMedCrossRef

De Boo DW, Uffmann M, Weber M, Bipat S, Boorsma EF, Scheerder MJ, et al: Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study. Acad Radiol 18(12):1507–1514, 2011. LID–10.1016/j.aPubMedCrossRef

Eadie LH, Taylor P, Gibson AP: A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur J Radiol. 81(1):e70–e76, 2012PubMedCrossRef

Shah PK, Austin JH, White CS, Patel P, Haramati LB, Pearson GD, et al: Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. Radiology. 226(1):235–241, 2003PubMedCrossRef

Monnier-Cholley L, Carrat F, Cholley BP, Tubiana JM, Arrive L: Detection of lung cancer on radiographs: receiver operating characteristic analyses of radiologists', pulmonologists', and anesthesiologists' performance. Radiology. 233(3):799–805, 2004PubMedCrossRef

Oda S, Awai K, Funama Y, Utsunomiya D, Yanaga Y, Kawanaka K, et al: Effects of dual-energy subtraction chest radiography on detection of small pulmonary nodules with varying attenuation: receiver operating characteristic analysis using a phantom study. Jpn J Radiol. 28(3):214–219, 2010PubMedCrossRef

10.

Li F, Hara T, Shiraishi J, Engelmann R, MacMahon H, Doi K: Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization. AJR Am J Roentgenol. 196(5):W535–W541, 2011PubMedCrossRef

11.

Balkman JD, Mehandru S, DuPont E, Novak RD, Gilkeson RC: Dual energy subtraction digital radiography improves performance of a next generation computer-aided detection program. J Thorac Imaging. 25(1):41–47, 2010PubMedCrossRef

12.

Szucs-Farkas Z, Patak MA, Yuksel-Hatz S, Ruder T, Vock P: Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers. Eur Radiol. 20(6):1289–1296, 2010PubMedCrossRef

13.

Freedman MT, Lo SC, Seibel JC, Bromley CM: Lung nodules: improved detection with software that suppresses the rib and clavicle on chest radiographs. Radiology. 260(1):265–273, 2011PubMedCrossRef

14.

Meziane M, Mazzone P, Novak E, Lieber ML, Lababede O, Phillips M, et al: A comparison of four versions of a computer-aided detection system for pulmonary nodules on chest radiographs. J Thorac Imaging. 27(1):58–64, 2012PubMedCrossRef

15.

Fleiss JL, Levin B, Paik MC. The Measurement of Interrater Agreement. Statistical Methods for Rates and Proportions [Internet]. John Wiley & Sons, Inc.; 2003. p. 598–626. Available from: http://dx.doi.org/10.1002/0471445428.ch18

16.

Kuhlman JE, Collins J, Brooks GN, Yandow DR, Broderick LS: Dual-energy subtraction chest radiography: what to look for beyond calcified nodules. Radiographics. 26(1):79–92, 2006PubMedCrossRef

17.

Gilkeson RC, Novak RD, Sachs P: Digital radiography with dual-energy subtraction: improved evaluation of cardiac calcification. AJR Am J Roentgenol. 183(5):1233–1238, 2004PubMedCrossRef

18.

Mafi JN, Fei B, Roble S, Dota A, Katrapati P, Bezerra HG, et al: Assessment of coronary artery calcium using dual-energy subtraction digital radiography. J Digit Imaging. 25(1):129–136, 2012PubMedCrossRef

19.

Zompatori M, Mascalchi M, Ciccarese F, Sverzellati N, Pastorino U: Screening for lung cancer using low-dose spiral CT: 10 years later, state of the art. Radiol Med. 2013, doi:10.1007/s10278-012-9565-4

20.

Greenberg AK, Lu F, Goldberg JD, Eylers E, Tsay JC, Yie TA, et al: CT scan screening for lung cancer: risk factors for nodules and malignancy in a high-risk urban cohort. PLoS One 7(7):e39403, 2012PubMedCrossRef

Title: 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
Publication date: 01-08-2013
Publisher: Springer US
Published in: Journal of Imaging Informatics in Medicine / Issue 4/2013
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-012-9565-4

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A Comparison of Computer-Aided Detection (CAD) Effectiveness in Pulmonary Nodule Identification Using Different Methods of Bone Suppression in Chest Radiographs

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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