Circulation. Cardiovascular imaging · 2026
Automated AI-Based Aortic Measurements From Attenuation Correction CT as an Adjunctive Cardiovascular Risk Biomarker: An International Multicenter Study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Marcinkiewicz Anna M, Shanbhag Aakash, Chareonthaitawee Panithaya, Zhang Wenhao, Al-Jilaihawi Hasanian, Zaid Ryan, Cadet Sebastien, Ramirez Giselle, Lemley Mark, Yi Jirong, Hijazi Waseem, Builoff Valerie, Liang Joanna X, Calsavara Vinicius F, Einstein Andrew J, Miller Edward J, Feher Attila, Ruddy Terrence D, Le Viet T, Mason Steve, Knight Stacey, Alexanderson Erick, Carvajal-Juarez Isabel, Slipczuk Leandro, Travin Mark I, Rosamond Thomas L, Wopperer Samuel B, Berman Daniel S, Dey Damini, Di Carli Marcelo F, Miller Robert J H, Slomka Piotr J
Аннотация
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: CT 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 was calculated by indexing the diameter to body surface area.
RESULTS: A total of 29 339 patients (56% men; 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/m2 (interquartile range, 1.6-2.0) and 1.5 cm/m2 (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/m2; descending >1.6 cm/m2) 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.
METHODS: CT 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 was calculated by indexing the diameter to body surface area.
RESULTS: A total of 29 339 patients (56% men; 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/m2 (interquartile range, 1.6-2.0) and 1.5 cm/m2 (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/m2; descending >1.6 cm/m2) 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.