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Integration of Chest CT CAD into the Clinical Workflow and Impact on Radiologist Efficiency

Published:August 08, 2018DOI:https://doi.org/10.1016/j.acra.2018.07.006

      Rationale and Objectives

      The purpose of this paper is to describe the integration of a commercial chest CT computer-aided detection (CAD) system into the clinical radiology reporting workflow and perform an initial investigation of its impact on radiologist efficiency. It seeks to complement research into CAD sensitivity and specificity of stand-alone systems, by focusing on report generation time when the CAD is integrated into the clinical workflow.

      Materials and Methods

      A commercial chest CT CAD software that provides automated detection and measurement of lung nodules, ascending and descending aorta, and pleural effusion was integrated with a commercial radiology report dictation application. The CAD system automatically prepopulated a radiology report template, thus offering the potential for increased efficiency. The integrated system was evaluated using 40 scans from a publicly available lung nodule database. Each scan was read using two methods: (1) without CAD analytics, i.e., manually populated report with measurements using electronic calipers, and (2) with CAD analytics to prepopulate the report for reader review and editing. Three radiologists participated as readers in this study.

      Results

      CAD assistance reduced reading times by 7%–44%, relative to the conventional manual method, for the three radiologists from opening of the case to signing of the final report.

      Conclusion

      This study provides an investigation of the impact of CAD and measurement on chest CTs within a clinical reporting workflow. Prepopulation of a report with automated nodule and aorta measurements yielded substantial time savings relative to manual measurement and entry.

      Key Words

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      References

        • Rubin GD
        Lung nodule and cancer detection in CT screening.
        J Thorac Imaging. 2015 Mar; 30: 130-138https://doi.org/10.1097/RTI.0000000000000140
        • Naseem R
        • Alimgeer KS
        • Bashir T
        Recent trends in computer aided diagnosis of lung nodules in thorax CT scans.
        in: Innovations in electrical engineering and computational technologies (ICIEECT). 2017 Apr: 1-12https://doi.org/10.1109/ICIEECT.2017.7916548
        • Valente IRS
        • Cortez
        • PC Neto
        • et al.
        Automatic 3D pulmonary nodule detection in CT images: a survey.
        Comput Methods Programs Biomed. 2016 Feb; : 91-107https://doi.org/10.1016/J.CMPB.2015.10.006
        • Beigelman-Aubry C
        • Raffy P
        • Yang W
        Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time.
        Am J Roentgenol. 2007 Oct; 189: 948-955https://doi.org/10.2214/AJR.07.2302
        • Beyer F
        • Zierott L
        • Fallenberg EM
        • et al.
        Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader.
        Eur Radiol. 2007 May; 17: 2941-2947https://doi.org/10.1007/S00330-007-0667-1
        • Armato SGIII
        • McLennan G
        • Bidaut L
        • et al.
        The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans.
        Med Phys. 2011 Feb; 38: 915-931https://doi.org/10.1118/1.3528204
        • MacMahon H
        • Naidich DP
        • Goo JM
        • et al.
        Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017.
        Radiology. 2017 Jul; 284: 228-243https://doi.org/10.1148/radiol.2017161659
        • Brown MS
        • Lo P
        • Goldin JG
        • et al.
        Toward clinically usable CAD for lung cancer screening with computed tomography.
        Eur Radiol. 2014 Nov; 234: 2719-2728https://doi.org/10.1007/s00330-014-33290-0
        • Brown MS
        • Rogers SR
        • Goldin JG
        • et al.
        Computer-aided lung nodule detection in CT: results of large-scale observer test.
        Acad Radiol. 2005; 12: 681-686https://doi.org/10.1016/j.acra.2005.02.041