Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: Computed tomography attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index (1) was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% male; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.

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Journal
Circulation Cardiovascular Imaging
Published
2026-07-02
DOI
https://doi.org/10.1161/circimaging.125.019726
Primary Topic
Aortic Disease and Treatment Approaches
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article

Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study

Isabel Carvajal-Juárez, Waseem Hijazi, Leandro N. Slipczuk, Mark H. Lemley et al.
Circulation Cardiovascular Imaging
Aortic Disease and Treatment Approaches
article

Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study

Isabel Carvajal-Juárez, Waseem Hijazi, Leandro N. Slipczuk, Mark H. Lemley, Aakash Shanbhag, Panithaya Chareonthaitawee, Attila Fehér, Robert J.H. Miller, Hasan Jilaihawi, Andrew Jeffrey Einstein, Vinícius F. Calsavara, Viet T. Le, Damini Dey, Sebastien Cadet, Jirong Yi, Terrence David Ruddy, Mark I. Travin, Marcelo F. Di Carli, Stacey Knight, Piotr J. Slomka, Edward Joseph Miller, Thomas L. Rosamond, Daniel S. Berman, Anna M Marcinkiewicz, Joanna X. Liang, Giselle Ramirez, Samuel Wopperer, Wenhao Zhang, Valerie Builoff, Ryan Zaid, Erick Alexanderson, Steve Mason
article en

Abstract

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: Computed tomography attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index (1) was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% male; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.

Circulation Cardiovascular Imaging
University of Southern California (US), Cedars-Sinai Medical Center (US), Brigham and Women's Hospital (US), Albert Einstein College of Medicine (US), Mount Sinai Hospital (US), Mayo Clinic (US), Montefiore Medical Center (US), NewYork–Presbyterian Hospital (US), University of Ottawa (CA), University of Calgary (CA), University of Utah (US), Columbia University Irving Medical Center (US), Intermountain Medical Center (US), Yale University (US), Mayo Clinic in Arizona (US), University of Kansas Medical Center (US), Ministry of Interior and Administration (PL), Mayo Clinic in Florida (US), Instituto Nacional de Cardiología (MX)
Good health and well-being
Openalex Percentile: Top 8%
Aortic Disease and Treatment Approaches
DOISourceOpenAlex
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