Literature DB >> 21963532

Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study.

Diederick W De Boo1, Martin Uffmann, Michael Weber, Shandra Bipat, Eelco F Boorsma, Maeke J Scheerder, Nicole J Freling, Cornelia M Schaefer-Prokop.   

Abstract

RATIONALE AND
OBJECTIVES: To evaluate the impact of computer-aided detection (CAD, IQQA-Chest; EDDA Technology, Princeton Junction, NJ) used as second reader on the detection of small pulmonary nodules in chest radiography (CXR).
MATERIALS AND METHODS: A total of 113 patients (mean age 62 years) with CT and CXR within 6 weeks were selected. Fifty-nine patients showed 101 pulmonary nodules (diameter 5-15mm); the remaining 54 patients served as negative controls. Six readers of varying experience individually evaluated the CXR without and with CAD as second reader in two separate reading sessions. The sensitivity per lesion, figure of merit (FOM), and mean false positive per image (mFP) were calculated. Institutional review board approval was waived.
RESULTS: With CAD, the sensitivity increased for inexperienced readers (39% vs. 45%, P < .05) and remained unchanged for experienced readers (50% vs. 51%). The mFP nonsignificantly increased for both inexperienced and experienced readers (0.27 vs. 0.34 and 0.16 vs. 0.21). The mean FOM did not significantly differ for readings without and with CAD irrespective of reader experience (0.71 vs. 0.71 and 0.84 vs. 0.87). All readers together dismissed 33% of true-positive CAD candidates. False-positive candidates by CAD provoked 40% of all false-positive marks made by the readers.
CONCLUSION: CAD improves the sensitivity of inexperienced readers for the detection of small nodules at the expense of loss of specificity. Overall performance by means of FOM was therefore not affected. To use CAD more beneficial, readers need to improve their ability to differentiate true from false-positive CAD candidates. Copyright Â
© 2011 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21963532     DOI: 10.1016/j.acra.2011.08.008

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

1.  New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.

Authors:  S Schalekamp; B van Ginneken; Bgf Heggelman; M Imhof-Tas; I Somers; M Brink; M Spee; Cm Schaefer-Prokop; N Karssemeijer
Journal:  Br J Radiol       Date:  2014-02-17       Impact factor: 3.039

2.  [Detection of lung nodules. New opportunities in chest radiography].

Authors:  S Pötter-Lang; S Schalekamp; C Schaefer-Prokop; M Uffmann
Journal:  Radiologe       Date:  2014-05       Impact factor: 0.635

Review 3.  The imaging of small pulmonary nodules.

Authors:  Zejun Zhou; Ping Zhan; Jiajia Jin; Yafang Liu; Qian Li; Chenhui Ma; Yingying Miao; Qingqing Zhu; Panwen Tian; Tangfeng Lv; Yong Song
Journal:  Transl Lung Cancer Res       Date:  2017-02

4.  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

5.  Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph.

Authors:  Nikolaos Dellios; Ulf Teichgraeber; Robert Chelaru; Ansgar Malich; Ismini E Papageorgiou
Journal:  J Clin Imaging Sci       Date:  2017-02-20

Review 6.  Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.

Authors:  Eui Jin Hwang; Chang Min Park
Journal:  Korean J Radiol       Date:  2020-05       Impact factor: 3.500

7.  Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study.

Authors:  Daiju Ueda; Akira Yamamoto; Akitoshi Shimazaki; Shannon Leigh Walston; Toshimasa Matsumoto; Nobuhiro Izumi; Takuma Tsukioka; Hiroaki Komatsu; Hidetoshi Inoue; Daijiro Kabata; Noritoshi Nishiyama; Yukio Miki
Journal:  BMC Cancer       Date:  2021-10-18       Impact factor: 4.430

  7 in total

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