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Automated detection of incidental abdominal aortic aneurysms on computed tomography

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software.

Materials and methods

The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs—maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA.

Results

The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively.

Conclusion

Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.

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Abbreviations

AAAs:

Abdominal aortic aneurysms

BTCV:

Beyond the cranial vault

ROC:

Receiver operating characteristic

AUC:

Area under curve

DSC:

Dice similarity coefficient

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Acknowledgments

This research was supported in part by the Intramural Research Program of the National Institutes of Health Clinical Center.

Funding

NIH Clinical Center,Z01 CL040003, Ronald M Summers, Z01 CL040004, Ronald M Summers

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Author notes

  1. Perry J. Pickhardt and Ronald M. Summers are co-senior authors.

Authors and Affiliations

  1. Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, 20892-1182, USA

    Devina Chatterjee, Thomas C. Shen, Pritam Mukherjee, Sungwon Lee & Ronald M. Summers

  2. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53726, USA

    John W. Garrett, Nicholas Zacharias & Perry J. Pickhardt

  3. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bldg. 10 Room 1C224D MSC 1182, Bethesda, MD, 20892-1182, USA

    Ronald M. Summers

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  1. Devina Chatterjee
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Correspondence to Ronald M. Summers.

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Conflict of interest

There are no competing interests or conflicts of interest for any author. The authors wish to disclose the following non-competing interests: PJP: advisor to Bracco, GE Healthcare, and Nano-X. RMS receives royalties from iCAD, ScanMed, PingAn, Philips, Translation Holdings, MGB. His lab received research funding through a Cooperative Research and Development Agreement with PingAn.

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Chatterjee, D., Shen, T.C., Mukherjee, P. et al. Automated detection of incidental abdominal aortic aneurysms on computed tomography. Abdom Radiol 49, 642–650 (2024). https://doi.org/10.1007/s00261-023-04119-1

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  • DOI: https://doi.org/10.1007/s00261-023-04119-1

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