Authors
Hyun Jin Ahn, Seokhun Yang, Jae Wook Jung, Sang-Hyeon Park, Jinlong Zhang, Keehwan Lee, Doyeon Hwang, Kyu-Sun Lee, Sang-Hoon Na, Joon-Hyung Doh, Chang-Wook Nam, Tae Hyun Kim, Eun-Seok Shin, Eun Ju Chun, Su-Yeon Choi, Hyun Kuk Kim, Young Joon Hong, Hun-Jun Park, Song-Yi Kim, Mirza Husic, Jess Lambrechtsen, Jesper M Jensen, Bjarne L Nørgaard, Daniele Andreini, Pal Maurovich-Horvat, Bela Merkely, Martin Penicka, Bernard de Bruyne, Abdul Ihdayhid, Brian Ko, Georgios Tzimas, Jonathon Leipsic, Javier Sanz, Mark G Rabbat, Farhan Katchi, Moneal Shah, Nobuhiro Tanaka, Ryo Nakazato, Taku Asano, Mitsuyasu Terashima, Hiroaki Takashima, Tetsuya Amano, Yoshihiro Sobue, Hitoshi Matsuo, Hiromasa Otake, Takashi Kubo, Masahiro Takahata, Takashi Akasaka, Teruhito Kido, Teruhito Mochizuki, Hiroyoshi Yokoi, Taichi Okonogi, Tomohiro Kawasaki, Koichi Nakao, Tomohiro Sakamoto, Taishi Yonetsu, Tsunekazu Kakuta, Yohei Yamauchi, Charles A Taylor, Jeroen J Bax, Peter H Stone, Jagat Narula, Leslee J Shaw, Bon-Kwon Koo
Published in
European heart journal. Cardiovascular Imaging. Oct 06, 2026. Epub Oct 06, 2026.
Abstract
Acute coronary syndrome (ACS) exhibits marked sex-related differences in clinical presentation and underlying pathophysiology. However, whether these differences translate into variations in culprit lesion prediction remains unclear. We investigated the sex-specific performance of conventional coronary computed tomography angiography (CCTA) and artificial intelligence-enabled quantitative coronary plaque and hemodynamic assessment (AI-QCPHA) for ACS culprit detection.
In this sub-study of the EMERALD II trial (NCT03591328), patients with ACS who underwent CCTA 1 month to 3 years prior to the event were analyzed. Culprit and non-culprit lesions were defined by invasive coronary angiography at the time of ACS. Diagnostic performance of obstructive stenosis (CAD-RADS ≥3) and high-risk plaque (HRP) criteria was compared between women and men. Information gain-based feature selection identified sex-specific AI-QCPHA best predictors across five feature clusters, and their incremental predictive value was evaluated using receiver operating characteristics reported as area under the curve (AUC). A total of 351 patients (90 women, 261 men) with 2,451 lesions were included. Conventional CCTA performed similarly across sexes for obstructive stenosis, whereas HRP differed by sex. AI-QCPHA identified the same best features for ACS culprit prediction in both sexes, and integration of these features improved discriminatory performance beyond conventional CCTA (women AUC 0.84 vs. 0.78, p = 0.002; men 0.82 vs. 0.75, p < 0.001). Among individual predictors, delta FFRCT yielded the highest discriminatory performance in both women and men.
AI-QCPHA enhances culprit lesion prediction in obstructive ACS beyond conventional CCTA in both sexes, supporting its potential role for improved culprit-lesion discrimination.
PMID:
42834459
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.
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