KLF2 Mediates the Suppressive Effect of Laminar Flow on Vascular Calcification by Inhibiting Endothelial BMP/SMAD1/5 Signaling.

[Figure: see text].

Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression.

BACKGROUND: Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)-based radiomics signature (RS) of plaques for predicting RPP. METHODS: A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method. RESULTS: RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P=0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P=0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32-4.46]; P=0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset (P<0.001). CONCLUSIONS: The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.

Hemodynamics derived from computational fluid dynamics based on magnetic resonance angiography is associated with functional outcomes in atherosclerotic middle cerebral artery stenosis.

BACKGROUND: To investigate the relationship between fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH), hemodynamics, and functional outcome in atherosclerotic middle cerebral artery (MCA) stenosis using a computational fluid dynamics (CFD) model based on magnetic resonance angiography (MRA), according to a modified Rankin Scale (mRS) at 3 months. METHODS: A total of 120 patients with 50-99% atherosclerotic MCA stenosis were included. The training and internal validation groups were composed of 99 participants and 21 participants, respectively. Demographic, imaging data, and functional outcome (mRS at 3 months) were collected. Hemodynamic parameters were obtained from the CFD model. The FVH score was based on the number of territories where FVH is positive, according to the spatial distribution in the Alberta Stroke Program Early Computed Tomography Score (ASPECTS). The prediction models were constructed according to clinical and hemodynamic parameters using multivariate logistic analysis. The DeLong test compared areas under the curves (AUCs) of the models. RESULTS: The multivariable logistic regression analysis showed that the National Institute of Health Stroke Scale (NIHSS) at admission, hypertension, hyperlipidemia, the ratio of wall shear stress before treatment (WSSRbefore), and difference in the ratio of wall shear stress (WSSR) were independently associated with functional outcome (all P<0.05). In the training group before treatment, the AUC of model 1a (only clinical variables) and 2a (clinical variables with addition of WSSRbefore) were 0.750 and 0.802. After treatment, the AUC of model 1b (only clinical variables) and 2b (clinical variables with addition of difference in WSSR) were 0.815 and 0.883, respectively. The AUC of models with hemodynamic parameters was significantly higher than the models based on clinical variables only (all P<0.05, DeLong test). In the internal validation group before treatment, the AUC of the model (clinical variables) was 0.782, and that of the model (clinical variables and WSSRbefore) was 0.800. After treatment, the AUC of the model (clinical variables) was 0.833, and that of the model (clinical variables and difference in WSSR) was 0.861. There were no significant differences between the good and the poor functional outcome group concerning FVHbefore scores before treatment (0.30±0.81 vs. 0.26±0.97; P=0.321) and FVHafter scores after treatment (0.08±0.39 vs. 0.00±0.00; P=0.244). CONCLUSIONS: Hemodynamics was associated with functional outcomes in patients with ischemic stroke attributed to atherosclerotic MCA stenosis, while FVH was not. Hemodynamic parameters were of great importance in the prediction models.