Synergistic value of fractional flow reserve and low‑density non‑calcified plaque based on coronary computed tomography angiography for the identification of lesion‑specific ischemia

作者：Linmeng Tang, Feng Liu, Ting-Yu Dong, Fei Yang, Shujun Cui · 发表于：Experimental and Therapeutic Medicine · 年份：2022 · DOI：10.3892/etm.2022.11637 · 被引用次数：5 · 研究领域：Cardiac Imaging and Diagnostics、Coronary Interventions and Diagnostics、Cerebrovascular and Carotid Artery Diseases

Increasing evidence has suggested that plaque characteristics are closely associated with ischemia, and coronary computed tomography (CT) angiography‑derived fractional flow reserve (FFR CT ) based on deep machine learning algorithms has also been used to identify lesion‑specific ischemia. Therefore, the aim of the present study was to explore the predictive ability of plaque characteristics in combination with deep learning‑based FFR CT for lesion‑specific ischemia. To meet this end, invasive FFR was used as a reference standard, with the joint aims of the early prediction of ischemic lesions and guiding clinical treatment. In the present study, the plaque characteristics, including non‑calcified plaque (NCP), low‑density NCP (LD‑NCP), plaque length, total plaque volume (TPV), remodeling index, calcified plaque, fibrous plaque and plaque burden, were obtained using a semi‑automated program. The FFR CT values were derived based on a deep machine learning algorithm. On the basis of the data obtained, differences among the values between the atopic ischemia and the non‑significant lesions groups were analyzed to further determine the predictive value of independent predictors for atopic ischemia. Of the plaque features, FFR CT , LD‑NCP, NCP, TPV and plaque length differed significantly when comparing between the lesion‑specific ischemia and no hemodynamic abnormality groups, and LD‑NCP and FFR CT were both independent predictors for ischemia. Additionally, FFR CT combined w...