Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department

effect on workflow and diagnostic accuracy

Amirhossein Mozaffary, Tugce Agirlar Trabzonlu, Pamela Lombardi, Adeel R. Seyal, Rishi Agrawal, Vahid Yaghmai*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Purpose: To assess the feasibility of implementing fully automated computer-aided diagnosis (CAD) for detection of pulmonary nodules on CT pulmonary angiography (CTPA) studies in emergency setting. Materials and methods: CTPA of 48 emergency patients was retrospectively reviewed. Fully automated CAD nodule detection was performed at the scanner and results were automatically submitted to PACS. A third-year radiology resident (RAD1) and a cardiothoracic radiologist with 6 years’ experience (RAD2) reviewed the scans independently to detect pulmonary nodules in two different sessions 8 weeks apart: session 1, CAD was reviewed first and then all images were reviewed; session 2, CAD was reviewed last after all images were reviewed. Time spent by RAD to evaluate image sets was measured for each case. Fisher’s exact test and t test were used. Results: There were 17 male and 31 female patients with mean ± SD age of 48.7 ± 16.4 years. Using CAD at the beginning was associated with lower average reading time for both readers. However, difference in reading time did not reach statistical significance for RAD1 (RAD1 94.6 s vs. 102.7 s, P > 0.05; RAD2 61.1 s vs. 76.5 s, P < 0.05). Using CAD at the end significantly increased rate of RAD1 and RAD2 nodule detection by 34% (2.52 vs. 2.12 nodule/scan, P < 0.05) and 27% (2.23 vs. 1.81 nodule/scan, P < 0.05), respectively. Conclusion: Routine utilization of CAD in emergency setting is feasible and can improve detection rate of pulmonary nodules significantly. Different methods of incorporating CAD in detecting pulmonary nodules can improve both the rate of detection and interpretation speed.

Original languageEnglish (US)
JournalEmergency Radiology
DOIs
StatePublished - Jan 1 2019

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Hospital Emergency Service
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Computed Tomography Angiography
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Keywords

  • Computed tomography
  • Computer-aided diagnosis
  • Pulmonary nodule

ASJC Scopus subject areas

  • Emergency Medicine
  • Radiology Nuclear Medicine and imaging

Cite this

@article{c7b295b18d9b4198a52944ee713b503d,
title = "Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy",
abstract = "Purpose: To assess the feasibility of implementing fully automated computer-aided diagnosis (CAD) for detection of pulmonary nodules on CT pulmonary angiography (CTPA) studies in emergency setting. Materials and methods: CTPA of 48 emergency patients was retrospectively reviewed. Fully automated CAD nodule detection was performed at the scanner and results were automatically submitted to PACS. A third-year radiology resident (RAD1) and a cardiothoracic radiologist with 6 years’ experience (RAD2) reviewed the scans independently to detect pulmonary nodules in two different sessions 8 weeks apart: session 1, CAD was reviewed first and then all images were reviewed; session 2, CAD was reviewed last after all images were reviewed. Time spent by RAD to evaluate image sets was measured for each case. Fisher’s exact test and t test were used. Results: There were 17 male and 31 female patients with mean ± SD age of 48.7 ± 16.4 years. Using CAD at the beginning was associated with lower average reading time for both readers. However, difference in reading time did not reach statistical significance for RAD1 (RAD1 94.6 s vs. 102.7 s, P > 0.05; RAD2 61.1 s vs. 76.5 s, P < 0.05). Using CAD at the end significantly increased rate of RAD1 and RAD2 nodule detection by 34{\%} (2.52 vs. 2.12 nodule/scan, P < 0.05) and 27{\%} (2.23 vs. 1.81 nodule/scan, P < 0.05), respectively. Conclusion: Routine utilization of CAD in emergency setting is feasible and can improve detection rate of pulmonary nodules significantly. Different methods of incorporating CAD in detecting pulmonary nodules can improve both the rate of detection and interpretation speed.",
keywords = "Computed tomography, Computer-aided diagnosis, Pulmonary nodule",
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doi = "10.1007/s10140-019-01707-x",
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Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department : effect on workflow and diagnostic accuracy. / Mozaffary, Amirhossein; Trabzonlu, Tugce Agirlar; Lombardi, Pamela; Seyal, Adeel R.; Agrawal, Rishi; Yaghmai, Vahid.

In: Emergency Radiology, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department

T2 - effect on workflow and diagnostic accuracy

AU - Mozaffary, Amirhossein

AU - Trabzonlu, Tugce Agirlar

AU - Lombardi, Pamela

AU - Seyal, Adeel R.

AU - Agrawal, Rishi

AU - Yaghmai, Vahid

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Purpose: To assess the feasibility of implementing fully automated computer-aided diagnosis (CAD) for detection of pulmonary nodules on CT pulmonary angiography (CTPA) studies in emergency setting. Materials and methods: CTPA of 48 emergency patients was retrospectively reviewed. Fully automated CAD nodule detection was performed at the scanner and results were automatically submitted to PACS. A third-year radiology resident (RAD1) and a cardiothoracic radiologist with 6 years’ experience (RAD2) reviewed the scans independently to detect pulmonary nodules in two different sessions 8 weeks apart: session 1, CAD was reviewed first and then all images were reviewed; session 2, CAD was reviewed last after all images were reviewed. Time spent by RAD to evaluate image sets was measured for each case. Fisher’s exact test and t test were used. Results: There were 17 male and 31 female patients with mean ± SD age of 48.7 ± 16.4 years. Using CAD at the beginning was associated with lower average reading time for both readers. However, difference in reading time did not reach statistical significance for RAD1 (RAD1 94.6 s vs. 102.7 s, P > 0.05; RAD2 61.1 s vs. 76.5 s, P < 0.05). Using CAD at the end significantly increased rate of RAD1 and RAD2 nodule detection by 34% (2.52 vs. 2.12 nodule/scan, P < 0.05) and 27% (2.23 vs. 1.81 nodule/scan, P < 0.05), respectively. Conclusion: Routine utilization of CAD in emergency setting is feasible and can improve detection rate of pulmonary nodules significantly. Different methods of incorporating CAD in detecting pulmonary nodules can improve both the rate of detection and interpretation speed.

AB - Purpose: To assess the feasibility of implementing fully automated computer-aided diagnosis (CAD) for detection of pulmonary nodules on CT pulmonary angiography (CTPA) studies in emergency setting. Materials and methods: CTPA of 48 emergency patients was retrospectively reviewed. Fully automated CAD nodule detection was performed at the scanner and results were automatically submitted to PACS. A third-year radiology resident (RAD1) and a cardiothoracic radiologist with 6 years’ experience (RAD2) reviewed the scans independently to detect pulmonary nodules in two different sessions 8 weeks apart: session 1, CAD was reviewed first and then all images were reviewed; session 2, CAD was reviewed last after all images were reviewed. Time spent by RAD to evaluate image sets was measured for each case. Fisher’s exact test and t test were used. Results: There were 17 male and 31 female patients with mean ± SD age of 48.7 ± 16.4 years. Using CAD at the beginning was associated with lower average reading time for both readers. However, difference in reading time did not reach statistical significance for RAD1 (RAD1 94.6 s vs. 102.7 s, P > 0.05; RAD2 61.1 s vs. 76.5 s, P < 0.05). Using CAD at the end significantly increased rate of RAD1 and RAD2 nodule detection by 34% (2.52 vs. 2.12 nodule/scan, P < 0.05) and 27% (2.23 vs. 1.81 nodule/scan, P < 0.05), respectively. Conclusion: Routine utilization of CAD in emergency setting is feasible and can improve detection rate of pulmonary nodules significantly. Different methods of incorporating CAD in detecting pulmonary nodules can improve both the rate of detection and interpretation speed.

KW - Computed tomography

KW - Computer-aided diagnosis

KW - Pulmonary nodule

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