NSTE-ACS中非致死性病变进展和血管重建决策的冠状动脉周围放射学特征
Pericoronary Radiomics Signature for Non-Culprit Lesion Progression and Revascularization Decision in NSTE-ACS.
摘要(中文译文)
本研究旨在建立基于冠状动脉CT血管成像（CCTA）的血管周围脂肪组织（PCAT）影像组学模型，以识别非ST段抬高型急性冠脉综合征（NSTE-ACS）患者中的高危非罪犯病变（NCLs），从而可能有助于早期风险分层。在这项纳入542例NSTE-ACS患者的前瞻性队列中，从基线冠状动脉CTA中提取非罪犯病变（NCLs）的冠状动脉周围脂肪组织（PCAT）影像组学特征。患者被分配至训练队列（n=379）和验证队列（n=163）。应用机器学习算法开发影像组学特征模型（Rad模型）以预测4年NCL相关主要不良心血管事件（MACEs）。构建临床-影像组学联合模型以增强预测性能。此外，在具有系列CCTA的亚队列（n=60）中评估了基线Rad模型与纵向非钙化斑块进展（ΔNCPV%/年）之间的关联。在中位4.0年随访期间，84例患者（15.5%）发生NCL相关MACE。Rad模型（包含九个特征）可独立预测MACE（校正风险比，1.988；95% CI，1.753-2.254；P<0.001）。在验证队列中，联合模型对4年MACE的区分能力高于单独临床模型（AUC，0.793 vs. 0.703；P<0.05）。在系列CCTA亚组中，较高的基线Rad模型与年化非钙化斑块体积进展显著相关（标准化β，0.477；P<0.001）。基于CCTA的PCAT影像组学模型与NSTE-ACS患者未来NCL相关MACE及加速的斑块进展相关。该方法可能作为一种无创工具用于个体化风险分层。
Abstract
: This study aimed to establish a CCTA-based radiomics model of perivascular adipose tissue (PCAT) to identify high-risk NCLs in patients with NSTE-ACS, potentially facilitating early risk stratification. : In this prospective cohort of 542 NSTE-ACS patients, pericoronary adipose tissue (PCAT) radiomic features of non-culprit lesions (NCLs) were extracted from baseline coronary CTA. Patients were assigned to training ( = 379) and validation ( = 163) cohorts. Machine learning algorithms were applied to develop a radiomics signature (Rad model) to predict 4-year NCL-related major adverse cardiovascular events (MACEs). A combined clinic-radiomics model was constructed to enhance predictive performance. Additionally, the association between the baseline Rad model and longitudinal non-calcified plaque progression (ΔNCPV%/year) was evaluated in a subcohort ( = 60) with serial CCTA. : Over a median 4.0-year follow-up, NCL-related MACE occurred in 84 patients (15.5%). The Rad model (comprising nine features) independently predicted MACE (adjusted hazard ratio, 1.988; 95% CI, 1.753-2.254; < 0.001). In the validation cohort, the combined model yielded higher discrimination for 4-year MACE than the clinical model alone (AUC, 0.793 vs. 0.703; < 0.05). In the serial CCTA subgroup, a higher baseline Rad model was significantly associated with annualized non-calcified plaque volume progression (standardized β, 0.477; < 0.001). : A CCTA-based PCAT radiomics model is associated with future NCL-related MACE and accelerated plaque progression in patients with NSTE-ACS. This approach may serve as a non-invasive tool for individualized risk stratification.
如何引用
AMA
Haidan Zhang, Haichu Wen, Yahui Han, Yifan Wang, Jifang Zhang, Zhen Zhou, et al. Pericoronary Radiomics Signature for Non-Culprit Lesion Progression and Revascularization Decision in NSTE-ACS.. Diagnostics (Basel, Switzerland). 2026; doi:10.3390/diagnostics16091341.
APA
Haidan Zhang, Haichu Wen, Yahui Han, Yifan Wang, Jifang Zhang, Zhen Zhou, et al (2026). Pericoronary Radiomics Signature for Non-Culprit Lesion Progression and Revascularization Decision in NSTE-ACS.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16091341
GB/T 7714
Haidan Zhang, Haichu Wen, Yahui Han, Yifan Wang, Jifang Zhang, Zhen Zhou, et al. Pericoronary Radiomics Signature for Non-Culprit Lesion Progression and Revascularization Decision in NSTE-ACS.[J]. Diagnostics (Basel, Switzerland), 2026 doi:10.3390/diagnostics16091341.
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