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
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Lung nodule and cancer detection in CT screening.J Thorac Imaging. 2015 Mar; 30: 130-138https://doi.org/10.1097/RTI.0000000000000140
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Article Info
Publication History
Published online: August 08, 2018
Accepted:
July 15,
2018
Received in revised form:
July 14,
2018
Received:
June 14,
2018
Identification
Copyright
© 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
