Literature DB >> 23341178

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

Ronald D Novak1, Nicholas J Novak, Robert Gilkeson, Bahar Mansoori, Gunhild E Aandal.   

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.

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Mesh:

Year:  2013        PMID: 23341178      PMCID: PMC3705010          DOI: 10.1007/s10278-012-9565-4

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  19 in total

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2.  Effects of dual-energy subtraction chest radiography on detection of small pulmonary nodules with varying attenuation: receiver operating characteristic analysis using a phantom study.

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3.  Detection of lung cancer on radiographs: receiver operating characteristic analyses of radiologists', pulmonologists', and anesthesiologists' performance.

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Review 4.  Dual-energy subtraction chest radiography: what to look for beyond calcified nodules.

Authors:  Janet E Kuhlman; Jannette Collins; Gregory N Brooks; Donald R Yandow; Lynn S Broderick
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5.  Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs.

Authors:  Thorsten Alexander Bley; Tobias Baumann; Ulrich Saueressig; Gregor Pache; Markus Treier; Oliver Schaefer; Ulrich Neitzel; Mathias Langer; Elmar Kotter
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6.  Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.

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8.  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

9.  Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect.

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10.  Dual energy subtraction digital radiography improves performance of a next generation computer-aided detection program.

Authors:  Jason D Balkman; Sonali Mehandru; Elena DuPont; Ronald D Novak; Robert C Gilkeson
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  7 in total

1.  A novel bone suppression method that improves lung nodule detection : Suppressing dedicated bone shadows in radiographs while preserving the remaining signal.

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Journal:  Int J Comput Assist Radiol Surg       Date:  2015-09-04       Impact factor: 2.924

2.  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

3.  Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging.

Authors:  Jiefang Wu; Weiguo Chen; Fengxia Zeng; Le Ma; Weimin Xu; Wei Yang; Genggeng Qin
Journal:  Quant Imaging Med Surg       Date:  2021-10

4.  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

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

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Journal:  BMC Cancer       Date:  2021-10-18       Impact factor: 4.430

6.  A novel bone suppression algorithm in intensity-based 2D/3D image registration for real-time tumor motion monitoring: Development and phantom-based validation.

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7.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

Authors:  Pranav Rajpurkar; Jeremy Irvin; Robyn L Ball; Kaylie Zhu; Brandon Yang; Hershel Mehta; Tony Duan; Daisy Ding; Aarti Bagul; Curtis P Langlotz; Bhavik N Patel; Kristen W Yeom; Katie Shpanskaya; Francis G Blankenberg; Jayne Seekins; Timothy J Amrhein; David A Mong; Safwan S Halabi; Evan J Zucker; Andrew Y Ng; Matthew P Lungren
Journal:  PLoS Med       Date:  2018-11-20       Impact factor: 11.069

  7 in total

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