Abstract
Background
The Carotid Plaque Reporting and Data System (Plaque-RADS) is a system for stroke risk assessment. However, the clinical utility of ultrasound (US)-based Plaque-RADS remains unclear. This study aimed to assess the incremental prognostic value and risk stratification utility of US-based Plaque-RADS in patients with prior stroke and low-to-moderate-grade carotid stenosis.
Methods
Between January 2020 and December 2022, 231 patients with carotid plaque ipsilateral to the prior stroke and low-to-moderate-grade carotid stenosis were retrospectively screened, of whom 158 were included in the final analysis. US-based Plaque-RADS classification was assigned based on duplex US assessment of plaque features. Cox regression and subgroup analyses were performed to evaluate the association between high-risk US-based Plaque-RADS categories and recurrent ischemic stroke. Model discrimination was assessed using receiver operating characteristic curves and area under the curve (AUC), and clinical utility was evaluated using decision curve analysis. The association between increasing US-based Plaque-RADS categories and the risk of recurrent ischemic stroke was assessed by trend analysis. The incremental risk-stratification value of refined high-risk Plaque-RADS thresholds was quantified with net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Internal validation was performed using bootstrap resampling.
Results
During the 3-year follow-up period, recurrent ischemic stroke occurred in 33 patients. High-risk US-based Plaque-RADS categories were significantly associated with recurrent ischemic stroke in patients with prior stroke and low-to-moderate-grade carotid stenosis, after adjusting for smoking, homocysteine, and intraplaque neovascularization [adjusted hazard ratio: 3.56; 95% confidence interval (CI): 1.07–11.83, P=0.038]. No statistically significant interactions were observed across the subgroups (all P for interaction >0.05). The Plaque-RADS ≥3 model demonstrated a higher AUC than the conventional US model [0.816 (95% CI: 0.731–0.901) vs. 0.810 (95% CI: 0.720–0.899)] and provided a slight net benefit across selected threshold probabilities. Additionally, the risk of recurrent ischemic stroke increased with higher Plaque-RADS categories (P for trend <0.001). Among the models using different high-risk Plaque-RADS thresholds, the Plaque-RADS ≥3b model showed the best predictive performance (0.865, 95% CI: 0.799–0.930), compared with the Plaque-RADS ≥3 model and the Plaque-RADS ≥3c model [0.824 (95% CI: 0.740–0.906)]. The Plaque-RADS ≥3b model also showed moderate improvements in NRI [0.250; (95% CI: 0.088–0.557), P=0.017] and IDI [0.090; (95% CI: 0.000–0.185), P=0.049], compared with the Plaque-RADS ≥3 model. Bootstrap internal validation indicated limited optimism across all models.
Conclusions
In patients with prior stroke and low-to-moderate-grade carotid stenosis, high-risk US-based Plaque-RADS categories were significantly associated with recurrent ischemic stroke. The US-based Plaque-RADS may help identify plaque phenotypes associated with an increased risk of recurrent ipsilateral ischemic stroke, while refined high-risk thresholds, particularly Plaque-RADS ≥3b, may provide additional value for risk stratification.
Keywords: Carotid Plaque Reporting and Data System (Plaque-RADS), carotid plaque vulnerability, recurrent ischemic stroke, ultrasonography
Introduction
Stroke is the second leading cause of death worldwide and a major cause of disability (1). Of all stroke events, approximately 85% are ischemic, and carotid atherosclerosis is closely related to their occurrence (2). The main mechanisms underlying ischemic stroke primarily include embolization from unstable plaques and hemodynamic insufficiency secondary to severe carotid stenosis. The degree of stenosis has traditionally been used as a marker of prognosis (3,4) and to assess the risk of thromboembolism (5-8). Surgical treatment such as carotid endarterectomy can significantly reduce the risk of stroke in symptomatic patients with 70–99% carotid stenosis (9,10) and has been widely accepted as the standard of care. However, its benefit is limited in patients with <70% stenosis, who still have a considerable risk of recurrent ischemic stroke (11). Increasing evidence suggests that high-risk plaque features may better predict ischemic stroke than the degree of stenosis alone in patients with low-to-moderate-grade carotid stenosis (12,13). Thus, the early identification of high-risk plaques in these patients is crucial to support individualized treatment and improve overall prognosis.
In recent years, noninvasive carotid imaging techniques have become important tools for assessing plaque vulnerability (14). Features including a vulnerable fibrous cap (FC), intraplaque neovascularization (IPN), intraplaque hemorrhage (IPH), and lipid-rich necrotic core (LRNC) are considered key markers of vulnerable plaques (14-18). Nevertheless, multiparametric assessment is complex, lacks standardization, and may not be feasible in clinical practice and research settings.
Recently, a novel stroke risk classification system, termed the Carotid Plaque Reporting and Data System (Plaque-RADS), was proposed (19). The system integrates information from multiple imaging modalities, including ultrasound (US), computed tomography angiography (CTA), and magnetic resonance imaging (MRI), and categorizes the severity of atherosclerotic lesions based on morphological and compositional plaque features (20). Studies have shown the potential value of MRI- and CTA-based Plaque-RADS for ischemic stroke assessment (21-23). However, MRI is expensive, time-consuming, and not universally available, while CTA may be limited by cost, radiation exposure, and workflow constraints in clinical practice.
US is a commonly used imaging modality for evaluating carotid disease and is simple, noninvasive, and cost-effective (15). However, the clinical value of a US-based Plaque-RADS classification for prognostic assessment and risk stratification in this population remains unclear. Therefore, this study aimed to determine whether high-risk US-based Plaque-RADS categories are associated with recurrent ischemic stroke and to assess the incremental prognostic and risk-stratification value of US-based Plaque-RADS for predicting recurrent ischemic stroke in these patients. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1216/rc).
Methods
Patients
A total of 231 patients with prior stroke, low-to-moderate-grade carotid stenosis (<70%), and complete clinical, imaging, and follow-up outcome data were screened at Beijing Tiantan Hospital, Capital Medical University. All patients underwent both duplex US and contrast-enhanced ultrasound (CEUS) examinations between January 2020 and December 2022. Prior stroke was defined as an MRI-confirmed index cerebral infarction occurring within 6 months before enrollment in the vascular territory ipsilateral to the evaluated carotid plaque (24). The exclusion criteria were as follows: (I) poor image quality; (II) intracranial atherosclerotic disease, including atherosclerotic stenosis or occlusion of the intracranial internal carotid artery, middle cerebral artery, posterior cerebral artery, intracranial vertebral artery, or basilar artery; (III) prior carotid revascularization; (IV) a history of malignancy or neck radiation therapy; (V) severe systemic disease; and/or (VI) nonatherosclerotic or cardioembolic stroke etiologies, including large-vessel vasculitis, moyamoya disease, atrial fibrillation, or valvular heart disease (Figure 1). Ultimately, 158 patients were included in the final analysis.
Figure 1.
Participant selection process for the study.
The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Beijing Tiantan Hospital (approval No. KY2022-015-04). The Ethics Committee waived the requirement for informed consent because this retrospective study involved only the analysis of clinical and imaging data obtained during routine clinical care and posed no more than minimal risk to participants. All patient data were anonymized and de-identified before analysis.
US acquisition and assessment
Transverse and longitudinal scans of the carotid arteries ipsilateral to the ischemic stroke, including the carotid bifurcations and internal carotid arteries, were performed with patients in the supine position and the neck fully exposed. Imaging was performed using a Canon Aplio i900 (Canon Medical Systems, Japan) and an Epiq 7 (Philips Healthcare, the Netherlands). The acquired images were stored in the system for subsequent review. Stenosis severity was categorized as normal, <50%, 50%–69%, ≥70% to near occlusion, near occlusion, or total occlusion according to the Society of Radiologists in Ultrasound consensus criteria (25) (Table S1).
Plaque echogenicity was visually assessed using two-dimensional B-mode US, with the adjacent sternocleidomastoid muscle and carotid adventitia serving as reference structures. Based on these reference structures, plaques were classified into five types according to the Gray-Weale system: (I) uniformly hypoechoic plaques with echogenic FC; (II) predominantly hypoechoic plaques with <25% echogenic areas; (III) predominantly echogenic plaques with <25% anechoic areas; (IV) predominantly hyperechoic plaques; and (V) heavily calcified plaques with acoustic shadowing (26,27).
CEUS was performed after an intravenous injection of SonoVue® (Bracco, Milan, Italy), a sulfur hexafluoride microbubble contrast agent. IPN was semi-quantitatively graded based on the presence, extent, and distribution of intraplaque microbubble enhancement as follows: 0, no contrast enhancement in the plaque; 1, limited enhancement involving the shoulder, the adventitial side, or both; 2, marked enhancement in the plaque (28) (Figure S1).
Drawing on the low-to-high-risk stratification framework of the original Plaque-RADS system, we developed a US-based classification based on directly measured maximum plaque thickness (MPT) and plaque echogenicity (29), as well as indirectly assessed FC status, plaque surface integrity, and suspected intraluminal thrombus on duplex US. The classification was defined as follows: 1, normal vessel wall; 2, MPT <3 mm; 3a, MPT ≥3 mm with a thick FC; 3b, MPT ≥3 mm with a thin FC; 3c, ulceration; and 4, FC rupture with or without suspected intraluminal thrombus, regardless of MPT. High-risk plaque categories were defined as US-based Plaque-RADS categories 3 and 4.
The US-assessed plaque features included the following: (I) MPT, defined as the maximum distance from the intimal surface to the point of maximal plaque protrusion, with ≥3 mm used as the thickness threshold (19); (II) FC status, assessed based on the continuity and clarity of the echogenic luminal surface line. A continuous echogenic line indicated a thick FC, whereas attenuation or poor visualization of the line over a hypoechoic core suggested a thin FC (30); (III) plaque surface ulceration, defined as a focal luminal depression or cavity with a visible back wall and Doppler flow entering the cavity, with possible flow reversal or swirling flow; (IV) FC rupture, defined as definite discontinuity of the luminal surface with Doppler flow entering the plaque cavity; (V) suspected intraluminal thrombus, defined as a hypoechoic or isoechoic surface-adherent mass protruding into the lumen, without internal color flow and with a possible filling defect or flow diversion; and (VI) juxtaluminal black area (JBA), defined as a hypoechoic to anechoic area adjacent to the lumen without a clearly visible echogenic FC (29).
Figure 2 shows representative examples of the US-based Plaque-RADS classification. Before outcome analysis, all plaque images were visually assessed and classified by two experienced physicians, each with more than 15 years of experience in carotid US, who were blinded to the clinical information and outcomes. One physician repeated the blinded evaluation after 4 weeks using a randomized image order. Any discrepancies were resolved by consensus.
Figure 2.
Representative neck US images of US-based Plaque-RADS categories. (A-C) Category 1: US demonstrates a thin, homogeneous vessel wall without plaque in the CCA and carotid bifurcation. (D-F) Category 2: US demonstrates an eccentric plaque with an MPT <3 mm (arrows): (D) axial image; (E) longitudinal image; (F) color Doppler image. (G-I) Category 3a: US demonstrates a sizable plaque involving the carotid bifurcation, with predominantly homogeneous isoechoic plaque and a continuous, clearly visible echogenic luminal surface line (arrow), suggestive of a thick FC: (G,H) B-mode images; (I) color Doppler image. (J-L) Category 3b: US demonstrates a heterogeneous plaque at the carotid bifurcation, with JBA along the far wall side: (J) the echogenic line between the plaque and the lumen is attenuated and indistinct, suggesting a thin FC; (K) color Doppler imaging confirms preserved flow adjacent to the plaque; (L) outline of the posterior plaque component. (M-O) Category 3c: US demonstrates a hypoechoic plaque with surface irregularity (asterisks in M and N) in the carotid bulb; (O) ulceration (arrow) is identified on Superb Microvascular imaging. (P-R) Category 4: US demonstrates a mixed isoechoic and hyperechoic plaque with calcification (arrows in P and Q) in the proximal carotid artery: (P,Q) discontinuity of the plaque surface (triangular arrows) suggests FC rupture; (R) power Doppler shows blood flow filling the plaque surface defect (triangular arrow), with adjacent circumferential flow signals (arrow), suggesting FC rupture with suspected intraluminal thrombus. CCA, common carotid artery; ECA, external carotid artery; FC, fibrous cap; ICA, internal carotid artery; JBA, juxtaluminal black area; MPT, maximum plaque thickness; Plaque-RADS, Carotid Plaque Reporting and Data System; RT, right; US, ultrasound.
Clinical data and follow-up
The clinical data were collected from the electronic medical records, including demographic characteristics (age and sex), risk factors (hypertension, diabetes, smoking, alcohol use, and coronary artery disease), laboratory indicators [triglycerides, cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, fasting plasma glucose, and homocysteine (Hcy)], and vascular risk-reduction treatment status. The endpoint was recurrent ischemic stroke ipsilateral to the index carotid plaque, defined by the presence of a new neurological deficit persisting for >24 hours and corresponding MRI evidence of cerebral infarction.
Patients were followed from the date of the baseline carotid US examination until the occurrence of ipsilateral recurrent ischemic stroke or 3 years of follow-up, whichever occurred first. Patients who did not experience recurrent ischemic stroke were followed for the full 3-year observation period, with no loss to follow-up or death during follow-up. Follow-up clinical outcome data were obtained from telephone interviews, electronic medical records, and follow-up imaging.
Statistical analyses
The distribution of continuous variables was assessed using the Kolmogorov-Smirnov test and Q-Q plots. Normally distributed continuous variables are presented as mean ± standard deviation and were compared using Student’s t-test. Non-normally distributed continuous variables are presented as median (interquartile range) and were compared using the Wilcoxon rank-sum test. Categorical variables are presented as counts (percentages) and were compared using the χ2 test or Fisher’s exact test, as appropriate. A P value <0.05 was considered statistically significant. Weighted kappa statistics were used to assess interobserver and intraobserver agreement for plaque echogenicity, US-based Plaque-RADS classification, and IPN grading.
Cox regression was used to assess the association between high-risk US-based Plaque-RADS categories and ipsilateral recurrent ischemic stroke. Model variables were prespecified based on clinical relevance and previous evidence. The proportional hazards assumption was tested using Schoenfeld residuals. Clinical, conventional US, and Plaque-RADS models were constructed to evaluate their predictive performance for ipsilateral recurrent ischemic stroke. Model discrimination was quantified using receiver operating characteristic curves and the area under the curve (AUC), while clinical utility was evaluated using decision curve analysis (DCA). AUC differences were tested using the DeLong test. In accordance with clinical practice, the threshold probability range for DCA was set at 0–0.5.
The association between high-risk US-based Plaque-RADS categories and recurrent ischemic stroke was assessed using trend analysis. The incremental value of refined high-risk Plaque-RADS thresholds (≥3b and ≥3c) for risk stratification and reclassification was assessed relative to Plaque-RADS ≥3 using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Bootstrap resampling was applied for internal validation and model stability assessment. Internal validation was performed using 1,000 bootstrap resamples, with all model coefficients re-estimated in each resample. Optimism was calculated as the difference between the concordance index (C-index) of each model evaluated in the bootstrap sample and that obtained when the same model was applied to the original dataset. Mean optimism was subtracted from the apparent C-index, and the 95% confidence interval (CI) was estimated from the 2.5th and 97.5th percentiles of the distribution of optimism-corrected C-indices. Subgroup analysis was conducted according to baseline vascular risk-reduction treatment status, and multiplicative interaction terms between high-risk US-based Plaque-RADS categories and treatment variables were included separately in the Cox models to assess potential effect modification. All statistical analyses were conducted using IBM SPSS Statistics (version 27.0, IBM) and R software (version 4.5.2, R Foundation for Statistical Computing).
Results
Of the 231 patients screened, 73 were excluded for the following reasons: poor image quality (n=4), intracranial atherosclerotic disease (n=12), prior carotid revascularization (n=25), a history of malignancy or neck radiation therapy (n=5), severe systemic disease (n=2), and nonatherosclerotic or cardioembolic stroke etiologies (n=25). Thus, 158 patients were included in the final analysis, of whom 141 were male (89%) and 17 were female (11%). During the 3-year follow-up period, 33 patients (20.8%) experienced recurrent ischemic stroke, all of which occurred ipsilateral to the investigated carotid artery, whereas 125 (79.2%) did not experience recurrent ischemic stroke. Of the 33 recurrent events, 7, 11, and 15 occurred during the first, second, and third years after the US examination, respectively.
Plaque characteristics and observer reliability
The prevalence of hypoechoic plaques, vulnerable FC, and IPN grade 2 differed significantly between the two groups (all P<0.05). Conversely, no statistically significant difference in the degree of stenosis was observed between the two groups (Table 1). The two observers disagreed on the classification in 21 of 158 cases (13.2%), including disagreement in relation to FC status (n=7), plaque ulceration or rupture (n=11), and suspected intraluminal thrombus (n=3). The distributions of US-based Plaque-RADS categories were as follows: 2, n=44 (27.8%); 3, n=104 (65.8%) [3a, n=60 (37.9%); 3b, n=27 (17.1%); 3c, n=17 (10.8%)]; and 4, n=10 (6.4%). The rates of recurrent ischemic stroke in each category were as follows: 2, n=3 (6.8%); 3a, n=6 (10.0%); 3b, n=12 (44.4%); 3c, n=7 (41.1%); and 4, n=5 (50%). The frequencies of IPN grades were as follows: 0, n=20 (12.6%); 1, n=75 (47.4%); and 2, n=63 (39.8%). Further, plaque echogenicity, the US-based Plaque-RADS classification, and IPN grading demonstrated substantial interobserver and intraobserver agreement, with weighted κ values of 0.798 and 0.843 for plaque echogenicity, 0.756 and 0.814 for the US-based Plaque-RADS classification, and 0.740 and 0.796 for IPN grading, respectively.
Table 1. Baseline characteristics and recurrent ischemic stroke events in the study population (N=158).
| Variables | Non-recurrent stroke group (n=125) | Recurrent stroke group (n=33) | P value |
|---|---|---|---|
| Demographics | |||
| Age, years | 62 [58–67] | 65 [57–68] | 0.265 |
| Sex, male | 112 (89.6) | 29 (87.9) | 0.777 |
| Risk factors | |||
| Hypertension | 81 (64.8) | 25 (75.8) | 0.299 |
| Diabetes | 35 (28.0) | 8 (24.2) | 0.827 |
| Smoking | 56 (44.8) | 24 (72.7) | 0.006* |
| Alcohol use | 66 (52.8) | 19 (57.6) | 0.697 |
| Coronary heart disease | 27 (21.6) | 9 (27.3) | 0.491 |
| Laboratory indicators | |||
| Triglycerides, mmol/L | 1.23 [0.88–1.75] | 1.34 [1.06–1.78] | 0.157 |
| Cholesterol, mmol/L | 3.57 [3.08–4.11] | 3.87 [3.50–4.21] | 0.061 |
| HDL-C, mmol/L | 1.23 [1.06–1.47] | 1.15 [1.01–1.30] | 0.057 |
| LDL-C, mmol/L | 2.38 [1.71–2.97] | 3.27 [2.27–3.76] | 0.002* |
| FPG, mmol/L | 5.10 [4.60–5.93] | 5.45 [5.04–6.22] | 0.074 |
| Hcy, μmol/L | 13.38 [10.70–17.98] | 19.02 [14.61–22.41] | <0.001* |
| Treatment | |||
| Antiplatelet therapy | 63 (50.4) | 23 (69.7) | 0.052 |
| Lipid-lowering therapy | 79 (63.2) | 27 (81.8) | 0.060 |
| Antihypertensive therapy | 64 (51.2) | 18 (54.5) | 0.845 |
| Glucose-lowering therapy | 29 (23.2) | 10 (30.3) | 0.496 |
| Ultrasonographic characteristics | |||
| Severity of carotid stenosis | 0.807 | ||
| <50% | 101 (80.8) | 26 (78.8) | |
| 50–69% | 24 (19.2) | 7 (21.2) | |
| MPT, mm | 3.60 [3.10–4.10] | 3.60 [3.10–4.50] | 0.469 |
| Plaque echogenicity | 0.038* | ||
| Types I and II | 78 (62.4) | 14 (42.4) | |
| Types III and IV | 47 (37.6) | 19 (57.6) | |
| FC | 0.048* | ||
| Intact | 97 (77.6) | 20 (60.6) | |
| Vulnerable | 28 (22.4) | 13 (39.4) | |
| Plaque calcification | 59 (47.2) | 10 (30.3) | 0.114 |
| IPN | <0.001* | ||
| Grades 0 and 1 | 80 (64.0) | 10 (30.3) | |
| Grade 2 | 45 (36.0) | 23 (69.7) | |
| US-based Plaque-RADS | 0.008* | ||
| Low-risk classification | 41 (32.8) | 3 (9.1) | |
| High-risk classification | 84 (67.2) | 30 (90.9) |
Median [interquartile range] is used for continuous variables, and number (percentage) is expressed for categorical variables. *, P<0.05. FC, fibrous cap; FPG, fasting plasma glucose; Hcy, homocysteine; HDL-C, high-density lipoprotein cholesterol; IPN, intraplaque neovascularization; LDL-C, low-density lipoprotein cholesterol; MPT, maximum plaque thickness; Plaque-RADS, Carotid Plaque Reporting and Data System; US, ultrasound.
Clinical characteristics
Demographic characteristics were comparable between the two groups. Compared with the non-recurrent group, the recurrent stroke group had a higher prevalence of smoking and higher levels of LDL-C and Hcy (P=0.006, P=0.002, and P<0.001, respectively). Baseline characteristics and recurrent ischemic stroke events are presented in Table 1.
Cox proportional hazards model outcomes
In the Cox proportional hazards regression analysis, a statistically significant association between high-risk US-based Plaque-RADS categories and recurrent ischemic stroke was observed after adjusting for smoking, Hcy, and IPN [adjusted hazard ratio (HR): 3.56; 95% CI: 1.07–11.83, P=0.038]. No significant violation of the proportional hazards assumption was detected for the overall model (Table S2). The clinical model included smoking and Hcy, while the conventional US model included smoking, Hcy, plaque echogenicity, FC, and IPN. The Plaque-RADS model included smoking, Hcy, IPN, and high-risk US-based Plaque-RADS categories. Figure 3 shows the AUC values and DCA results for the models. The clinical model had an AUC of 0.747 (95% CI: 0.652–0.842). After incorporating conventional US features, the AUC increased to 0.810 (95% CI: 0.720–0.899), while the AUC of the Plaque-RADS model reached 0.816 (95% CI: 0.731–0.901) (Table 2). Compared with the clinical model, both imaging models demonstrated improved predictive performance. Although no significant difference was observed between the two imaging models (P=0.813), the Plaque-RADS model showed a slight advantage over the conventional US model across selected threshold probabilities.
Figure 3.
Comprehensive evaluation of multivariable prediction models and comparison of US-based Plaque-RADS models with different high-risk thresholds for predicting recurrent ischemic stroke risk (A-D). (A) ROC curves of the multivariable models. The Plaque-RADS model showed the highest AUC among the three models. (B) ROC curves of the Plaque-RADS threshold models. The Plaque-RADS ≥3b model showed the highest AUC among the three models. (C) DCA of the multivariable models. The Plaque-RADS model showed a higher net benefit than the clinical model and a slight advantage over the conventional US model across certain threshold probabilities. (D) DCA of the Plaque-RADS threshold models. The Plaque-RADS ≥3b model demonstrated a modest net benefit over the other models across certain threshold probabilities. AUC, area under the curve; DCA, decision curve analysis; Plaque-RADS, Carotid Plaque Reporting and Data System; ROC, receiver operating characteristic; US, ultrasound.
Table 2. Comparative performance of multivariable models for predicting recurrent ischemic stroke.
| Model | HR (95% CI) | P value | AUC (95% CI) |
|---|---|---|---|
| Clinical model | 0.747 (0.652–0.842) | ||
| Smoking | 2.40 (1.09–5.28) | 0.029* | |
| Hcy | 1.06 (1.02–1.10) | 0.001* | |
| Conventional US model | 0.810 (0.720–0.899) | ||
| Smoking | 2.53 (1.18–5.75) | 0.026* | |
| Hcy | 1.06 (1.02–1.10) | 0.001* | |
| Plaque echogenicity | 2.17 (1.08–4.37) | 0.030* | |
| FC | 1.72 (0.83–3.55) | 0.141 | |
| IPN | 3.29 (1.53–7.05) | 0.002* | |
| Plaque-RADS model | 0.816 (0.731–0.901) | ||
| Smoking | 2.66 (1.17–6.01) | 0.018* | |
| Hcy | 1.05 (1.01–1.09) | 0.007* | |
| IPN | 3.29 (1.56–6.96) | 0.002* | |
| High-risk Plaque-RADS (≥3) | 3.56 (1.07–11.83) | 0.038* | |
| Plaque-RADS ≥3b model | 0.865 (0.799–0.930) | ||
| Smoking | 2.55 (1.14–5.74) | 0.023* | |
| Hcy | 1.05 (1.01–1.09) | 0.007* | |
| IPN | 3.41 (1.61–7.22) | 0.001* | |
| Plaque-RADS ≥3b | 5.27 (2.42–11.44) | <0.001* | |
| Plaque-RADS ≥3c model | 0.824 (0.740–0.906) | ||
| Smoking | 2.78 (1.22–6.32) | 0.015* | |
| Hcy | 1.05 (1.01–1.09) | 0.012* | |
| IPN | 3.13 (1.46–6.69) | 0.003* | |
| Plaque-RADS ≥3c | 2.74 (1.29–5.79) | 0.008* |
*, P<0.05. AUC, area under the curve; CI, confidence interval; FC, fibrous cap; Hcy, homocysteine; HR, hazard ratio; IPN, intraplaque neovascularization; Plaque-RADS, Plaque Reporting and Data System; US, ultrasound.
Incremental utility of US-based Plaque-RADS classification
Additionally, the risk of recurrent ischemic stroke increased across higher Plaque-RADS categories (P for trend <0.001). The adjusted HRs of the refined Plaque-RADS thresholds (≥3b and ≥3c) were 5.27 (95% CI: 2.42–11.44, P <0.001) and 2.74 (95% CI: 1.29–5.79, P=0.008), respectively. Comparisons of the US-based Plaque-RADS models with refined high-risk thresholds for predicting recurrent ischemic stroke risk are shown in Figure 3. The Plaque-RADS ≥3b model had the highest AUC among all models (0.865; 95% CI: 0.799–0.930), while the AUC of the Plaque-RADS ≥3c model was 0.824 (95% CI: 0.740–0.906) (Table 2). Similarly, a modest advantage of the Plaque-RADS ≥3b model over the Plaque-RADS ≥3 model and Plaque-RADS ≥3c model was also observed across some threshold probabilities. The Plaque-RADS ≥3b model did not differ significantly from either the Plaque-RADS model or the Plaque-RADS ≥3c model (P=0.078 and P=0.153, respectively). However, compared with the Plaque-RADS ≥3 model, the refined high-risk threshold of the Plaque-RADS ≥3b model provided improved risk stratification for identifying individuals at high risk of recurrent ischemic stroke. Specifically, a moderate improvement in risk classification was observed, with an NRI of 0.250 (95% CI: 0.088–0.557, P=0.017) and an IDI of 0.090 (95% CI: 0.000–0.185, P=0.049) (Table 3). Bootstrap internal validation suggested limited optimism across all models, as the optimism-corrected C-index values remained close to the apparent values (Table S3).
Table 3. Incremental value of refined high-risk US-based Plaque-RADS models for risk stratification of recurrent ischemic stroke.
| Model | NRI (95% CI) | P value for NRI | IDI (95% CI) | P value for IDI |
|---|---|---|---|---|
| Plaque-RADS model | References | References | References | References |
| Plaque-RADS ≥3b model | 0.250 (0.088, 0.557) | 0.017 | 0.090 (0.000, 0.185) | 0.049 |
| Plaque-RADS ≥3c model | 0.006 (–0.217, 0.256) | 0.843 | –0.096 (–0.189, –0.005) | 0.939 |
Plaque-RADS model: including smoking, Hcy, high-risk US-based Plaque-RADS categories (≥3a), and IPN; Plaque-RADS ≥3b model: including smoking, Hcy, high-risk US-based Plaque-RADS categories (≥3b), and IPN; Plaque-RADS ≥3c model: including smoking, Hcy, high-risk US-based Plaque-RADS categories (≥3c), and IPN. CI, confidence interval; Hcy, homocysteine; IDI, integrated discrimination improvement; IPN, intraplaque neovascularization; NRI, net reclassification improvement; Plaque-RADS, Carotid Plaque Reporting and Data System; US, ultrasound.
Subgroup analysis
No significant interactions were observed between high-risk US-based Plaque-RADS categories and vascular risk-reduction treatments across the strata of antiplatelet therapy, antihypertensive therapy, lipid-lowering therapy, and glucose-lowering therapy (all P for interaction >0.05) (Figure 4).
Figure 4.
Subgroup analysis of the association between high-risk US-based Plaque-RADS categories and recurrent ischemic stroke. CI, confidence interval; HR, hazard ratio; Plaque-RADS, Carotid Plaque Reporting and Data System; NE, not estimable due to sparse events; US, ultrasound.
Discussion
For patients with ischemic stroke in the ipsilateral carotid territory and low-to-moderate-grade carotid stenosis, the efficacy of a US-based Plaque-RADS classification for prognostic assessment and risk stratification has not been well established. Our study suggests that the US-based Plaque-RADS classification may characterize plaque vulnerability, facilitate the identification of high-risk patients and support more precise risk stratification, thereby potentially enabling earlier clinical assessment and timely intervention in this population.
Plaque vulnerability is a major imaging marker of ischemic stroke (14). A greater burden of vulnerable plaque components, including LRNC, IPH, a thin or ruptured FC, surface ulceration, and luminal thrombus, is often accompanied by inflammation and neovascularization (14,31). These characteristics may contribute to plaque destabilization, increasing the risk of rupture, hemorrhage, or thrombus formation, and ultimately predisposing patients to embolization and ischemic stroke (32). A substantial body of evidence has shown that, beyond the degree of stenosis, plaques with hypoechoic features, irregular surfaces, and IPN are predictors of ischemic stroke (15,31,33-37). Our results are consistent with previous reports.
In the present study, we found that high-risk US-based Plaque-RADS categories were significantly associated with ipsilateral recurrent ischemic stroke, suggesting that this classification may be used to characterize plaque phenotypes associated with stroke recurrence in patients with low-to-moderate-grade carotid stenosis and a history of stroke. Two previous studies have reported similar findings in relation to risk stratification. In an MRI cohort study that included patients with carotid stenosis ≥50%, the proportion of high-risk Plaque-RADS categories was higher in the symptomatic than the asymptomatic carotid artery (P<0.001) (38). Another CTA-based Plaque-RADS study showed that, in an embolic stroke of undetermined source cohort, carotid plaques ipsilateral to the stroke were significantly associated with high-risk Plaque-RADS categories (P=0.01) (21). Our results are consistent with these reports.
In our study, the conventional US model was constructed in accordance with previous reports (37,39). The difference between the two imaging models was limited; however, this may be because the US-based Plaque-RADS classification provides an integrated assessment of two-dimensional US plaque features. Nevertheless, the US-based Plaque-RADS classification still demonstrated a slight benefit in risk prediction. Therefore, the US-based Plaque-RADS classification may offer a practical and simplified approach for characterizing vulnerable plaques and identifying high-risk patients. Given the workflow and resource limitations associated with MRI/computed tomography-based plaque subtyping, particularly in patients with claustrophobia or certain implanted metal devices, a US-based classification may facilitate the broader implementation of Plaque-RADS in clinical practice.
We also found that Plaque-RADS ≥3b provided a more refined threshold for identifying high-risk patients than Plaque-RADS ≥3. This finding is of clinical significance, as it may represent an improvement over the previously established threshold. Most previous studies recommend Plaque-RADS ≥3 as a practical threshold for identifying high-risk plaques (20,40). Our findings support this approach; however, accumulating evidence indicates that higher Plaque-RADS categories reflect greater plaque instability, highlighting the importance of more refined risk stratification.
This study provided additional insights into risk stratification by evaluating different high-risk thresholds (≥3, ≥3b, and ≥3c). Given the limited sample size, the Plaque-RADS 4 subtype was not included as a separate threshold in the stratified analysis. Notably, a key finding of this study was that Plaque-RADS ≥3b provided a more nuanced and accurate threshold for risk stratification, particularly in identifying patients at high risk of recurrent ischemic stroke. This may be attributable to the thin FC characteristic of the Plaque-RADS 3b subtype, which may facilitate exposure of the LRNC and thrombogenic components, such as tissue factor, to the bloodstream, thereby promoting thrombus formation and subsequent ischemic stroke through artery-to-artery embolism (41,42). Thus, the refined threshold of Plaque-RADS ≥3b may support more precise and effective strategies for stroke prevention and management while preserving predictive performance and enabling more refined risk stratification. However, it is essential to balance this approach against the need to maximize the detection of high-risk individuals. The challenge lies in optimizing risk stratification while minimizing the likelihood of missing at-risk patients.
This study did not directly replicate or validate the original Plaque-RADS system. Instead, in patients with low-to-moderate-grade carotid stenosis, we drew on the overall framework of Plaque-RADS, which stratifies plaques from low to high risk, and incorporated plaque features that can be directly or indirectly identified by conventional US to develop a more clinically accessible and practical risk classification. This approach is broadly consistent with that adopted in a recent study (43). The original Plaque-RADS system includes IPH as an important criterion for category 4a; however, LRNC and IPH may both appear as hypoechoic or echolucent regions on conventional US and cannot be reliably distinguished from each other (44,45). Hypoechoic features were still assessed based on plaque echogenicity and JBA but were not interpreted as specific histopathological components such as LRNC or IPH. Therefore, IPH was not used as an independent classification criterion in this study. Instead, category 4 was defined as a US identifiable complex, high-risk plaque phenotype characterized by FC rupture, with or without suspected intraluminal thrombus, thereby reducing potential misclassification and improving classification agreement. This classification makes full use of the information available from conventional US; however, it remains exploratory in patients with low-to-moderate-grade carotid stenosis and requires further validation using higher-resolution imaging and external cohorts.
Clinical practice during follow-up is often more complex than that observed in research settings. In our study, the relatively high recurrence rate and predominance of males may be attributable to the tertiary referral setting of our center and the high baseline vascular risk among patients with a history of ipsilateral ischemic stroke (46) (Table S4). In addition, higher baseline Hcy and LDL-C levels and a higher prevalence of lipid-lowering therapy were observed in the recurrence group. Hcy is influenced by nutritional status, renal function, vitamin deficiencies, genetic factors, and other systemic conditions and should therefore be interpreted as a nonspecific risk marker rather than as evidence of a direct causal mechanism (47). The higher LDL-C levels observed in the patients with recurrent stroke may reflect a greater lipid burden. However, insufficient treatment intensity or adherence cannot be excluded because detailed baseline treatment information was unavailable. Meanwhile, the higher prevalence of lipid-lowering therapy may likewise reflect greater baseline vascular risk and corresponding treatment indications.
Further, previous studies have reported that vascular risk-reduction therapies, such as statins, may increase plaque echogenicity without altering plaque volume while reducing the risk of stroke (48,49). To explore whether these treatments modified the association between the US-based Plaque-RADS classification and recurrent ischemic stroke, subgroup and interaction analyses were performed. Although no statistically significant interactions were observed, this finding should not be interpreted as evidence that the association was consistent across treatment groups. Because of the small number of outcome events in some strata, HRs could not be reliably estimated for several subgroups. In addition, these analyses were based on baseline treatment status and did not account for potential changes in treatment exposure during follow-up. Therefore, these analyses should be considered exploratory, and potential treatment-related effect modification for vascular risk factor control requires further evaluation in larger studies with more outcome events and more detailed treatment data.
Several limitations of this study should be acknowledged. First, the limited number of outcome events and the small number of cases in some high-risk plaque categories (3c and 4) may have resulted in unstable model estimates and an increased risk of overfitting, despite efforts to limit model complexity and the use of bootstrap internal validation. In addition, the lack of external validation limits the generalizability of the findings. Second, this retrospective study included a predominantly male population. Although all consecutive eligible patients were included without selection based on sex, the generalizability of the findings to female patients should be interpreted with caution. Third, the single-center setting at a specialized cerebrovascular referral center and the restriction of initial screening to patients with complete clinical, imaging, and follow-up outcome data may have introduced selection bias and limited the representativeness of the study population. Fourth, although alternative stroke etiologies, including intracranial atherosclerosis and other cardioembolic sources, were systematically assessed and excluded at baseline, recurrent ischemic stroke events were not reassessed for etiology during follow-up. Given the complex mechanisms of recurrent ischemic stroke, other potential causes cannot be completely excluded, and not all recurrent events could be definitively attributed to carotid plaques. Thus, the observed findings should be interpreted as an association between high-risk plaque phenotypes and recurrent ischemic stroke rather than evidence of a direct causal relationship. Fifth, conventional US has limited ability to distinguish plaque components such as LRNC and IPH. Without MRI, CTA, or histopathological validation, some plaque features may be misclassified. Additionally, the study period overlapped with the coronavirus disease 2019 pandemic. Severe Acute Respiratory Syndrome Coronavirus 2 infection may increase inflammation, thrombosis, and plaque instability, potentially affecting the risk of recurrent ischemic stroke (50,51). However, infection status was not systematically recorded, preventing a comprehensive assessment of its effect.
In conclusion, US-based high-risk plaque phenotypes may aid in risk stratification for recurrent ischemic stroke. However, these exploratory findings do not constitute definitive validation of the proposed classification. Larger, multicenter, prospective cohorts with more outcome events and multimodal imaging or histopathological reference standards are needed to confirm the reliability and generalizability of these findings.
Conclusions
This study explored a US-based Plaque-RADS classification for assessing plaque phenotypes associated with ipsilateral recurrent ischemic stroke in patients with prior stroke and low-to-moderate-grade carotid stenosis. These findings are exploratory and hypothesis-generating, suggesting that this classification may have potential value for risk stratification and may guide early clinical intervention.
Supplementary
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Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Beijing Tiantan Hospital (approval No. KY2022-015-04). The Ethics Committee waived the requirement for informed consent because this retrospective study involved only the analysis of clinical and imaging data obtained during routine clinical care and posed no more than minimal risk to participants.
Footnotes
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1216/rc
Funding: This study was supported by the National Natural Science Foundation of China (Nos. 82271995 and 82527804).
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1216/coif). The authors have no conflicts of interest to declare.
Data Sharing Statement
Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1216/dss
References
- 1. Cheng F, Cheng P, Xie S, Wang H, Tang Y, Liu Y, Xiao Z, Zhang G, Yuan G, Wang K, Feng C, Zhou Y, Xia H, Wang Y, Wu Y. Epidemiological trends and age-period-cohort effects on ischemic stroke burden across the BRICS-plus from 1992 to 2021. BMC Public Health 2025;25:137.. 10.1186/s12889-025-21310-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Adams HP Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE 3rd. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993;24:35-41. [DOI] [PubMed] [Google Scholar]
- 3. Howard DPJ, Gaziano L, Rothwell PM, Oxford Vascular Study . Risk of stroke in relation to degree of asymptomatic carotid stenosis: a population-based cohort study, systematic review, and meta-analysis. Lancet Neurol 2021;20:193-202. Erratum in: Lancet Neurol. 2021 May;20(5):e4.. 10.1016/S1474-4422(20)30484-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Lackland DT, Roccella EJ, Deutsch AF, Fornage M, George MG, Howard G, Kissela BM, Kittner SJ, Lichtman JH, Lisabeth LD, Schwamm LH, Smith EE, Towfighi A, American Heart Association Stroke Council. Council on Cardiovascular and Stroke Nursing, Council on Quality of Care and Outcomes Research, Council on Functional Genomics and Translational Biology. Factors influencing the decline in stroke mortality: a statement from the American Heart Association/American Stroke Association. Stroke 2014;45:315-53.. 10.1161/01.str.0000437068.30550.cf [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Qureshi AI, Alexandrov AV, Tegeler CH, Hobson RW, 2nd, Dennis Baker J, Hopkins LN, American Society of Neuroimaging. Society of Vascular and Interventional Neurology . Guidelines for screening of extracranial carotid artery disease: a statement for healthcare professionals from the multidisciplinary practice guidelines committee of the American Society of Neuroimaging; cosponsored by the Society of Vascular and Interventional Neurology. J Neuroimaging 2007;17:19-47.. 10.1111/j.1552-6569.2006.00085.x [DOI] [PubMed] [Google Scholar]
- 6. Barnett HJ, Taylor DW, Eliasziw M, Fox AJ, Ferguson GG, Haynes RB, Rankin RN, Clagett GP, Hachinski VC, Sackett DL, Thorpe KE, Meldrum HE, Spence JD. Benefit of carotid endarterectomy in patients with symptomatic moderate or severe stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators. N Engl J Med 1998;339:1415-25. [DOI] [PubMed] [Google Scholar]
- 7. Rothwell PM, Warlow CP. Prediction of benefit from carotid endarterectomy in individual patients: a risk-modelling study. European Carotid Surgery Trialists' Collaborative Group. Lancet 1999;353:2105-10. [DOI] [PubMed] [Google Scholar]
- 8. Endarterectomy for asymptomatic carotid artery stenosis. Executive Committee for the Asymptomatic Carotid Atherosclerosis Study. JAMA 1995;273:1421-8. [PubMed] [Google Scholar]
- 9. North American Symptomatic Carotid Endarterectomy Trial. Methods, patient characteristics, and progress. Stroke 1991;22:711-20. [DOI] [PubMed] [Google Scholar]
- 10. Boules TN, Proctor MC, Aref A, Upchurch GR, Jr, Stanley JC, Henke PK. Carotid endarterectomy remains the standard of care, even in high-risk surgical patients. Ann Surg 2005;241:356-63.. 10.1097/01.sla.0000150270.86267.29 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. van Dam-Nolen DHK, Truijman MTB, van der Kolk AG, Liem MI, Schreuder FHBM, Boersma E, Daemen MJAP, Mess WH, van Oostenbrugge RJ, van der Steen AFW, Bos D, Koudstaal PJ, Nederkoorn PJ, Hendrikse J, van der Lugt A, Kooi ME, PARISK Study Group . Carotid Plaque Characteristics Predict Recurrent Ischemic Stroke and TIA: The PARISK (Plaque At RISK) Study. JACC Cardiovasc Imaging 2022;15:1715-26.. 10.1016/j.jcmg.2022.04.003 [DOI] [PubMed] [Google Scholar]
- 12. Paraskevas KI, Saba L, Perler BA, Aburahma A, Sultan S, Meschia JF, et al. High-risk carotid plaque features may be more accurate predictors of ipsilateral ischemic stroke risk than the degree of carotid artery stenosis. Int Angiol 2025;44:224-34.. 10.23736/S0392-9590.25.05431-8 [DOI] [PubMed] [Google Scholar]
- 13. Baradaran H, Eisenmenger LB, Hinckley PJ, de Havenon AH, Stoddard GJ, Treiman LS, Treiman GS, Parker DL, Scott McNally J. Optimal Carotid Plaque Features on Computed Tomography Angiography Associated With Ischemic Stroke. J Am Heart Assoc 2021;10:e019462.. 10.1161/JAHA.120.019462 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Saba L, Saam T, Jäger HR, Yuan C, Hatsukami TS, Saloner D, Wasserman BA, Bonati LH, Wintermark M. Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol 2019;18:559-72.. 10.1016/S1474-4422(19)30035-3 [DOI] [PubMed] [Google Scholar]
- 15. Brinjikji W, Rabinstein AA, Lanzino G, Murad MH, Williamson EE, DeMarco JK, Huston J, 3rd. Ultrasound Characteristics of Symptomatic Carotid Plaques: A Systematic Review and Meta-Analysis. Cerebrovasc Dis 2015;40:165-74.. 10.1159/000437339 [DOI] [PubMed] [Google Scholar]
- 16. Naghavi M, Libby P, Falk E, Casscells SW, Litovsky S, Rumberger J, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I. Circulation 2003;108:1664-72.. 10.1161/01.CIR.0000087480.94275.97 [DOI] [PubMed] [Google Scholar]
- 17. Demeure F, Bouzin C, Roelants V, Bol A, Verhelst R, Astarci P, Gerber BL, Pouleur AC, Pasquet A, Meester C, Vanoverschelde JJ, Vancraeynest D. Response by Demeure et al to Letter Regarding Article, "Head-to-Head Comparison of Inflammation and Neovascularization in Human Carotid Plaques: Implications for the Imaging of Vulnerable Plaques". Circ Cardiovasc Imaging 2017;10:e007012.. 10.1161/CIRCIMAGING.117.007012 [DOI] [PubMed] [Google Scholar]
- 18. Kassem M, Nies KPH, Boswijk E, van der Pol J, Aizaz M, Gijbels MJJ, Li D, Bucerius J, Mess WH, Wildberger JE, van Oostenbrugge RJ, Moonen RPM, Fan Z, Kooi ME. Quantification of carotid plaque composition with a multi-contrast atherosclerosis characterization (MATCH) MRI sequence. Front Cardiovasc Med 2023;10:1227495.. 10.3389/fcvm.2023.1227495 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Saba L, Cau R, Murgia A, Nicolaides AN, Wintermark M, Castillo M, et al. Carotid Plaque-RADS: A Novel Stroke Risk Classification System. JACC Cardiovasc Imaging 2024;17:62-75.. 10.1016/j.jcmg.2023.09.005 [DOI] [PubMed] [Google Scholar]
- 20. Huang Z, Cheng XQ, Lu RR, Bi XJ, Liu YN, Deng YB. Incremental Prognostic Value of Carotid Plaque-RADS Over Stenosis Degree in Relation to Stroke Risk. JACC Cardiovasc Imaging 2025;18:77-89.. 10.1016/j.jcmg.2024.07.004 [DOI] [PubMed] [Google Scholar]
- 21. Song JW, Phi HQ, Koneru M, Cao Q, Rubin J, Sakai Y, Ibrahim L, Zhou SE, Woo JH, Kasner SE, Saba L, Cucchiara BL. Prevalence of High-Risk CTA-Based Carotid Plaque-RADS Subtypes in Patients With Embolic Stroke of Undetermined Source. Stroke 2025;56:737-40.. 10.1161/STROKEAHA.124.048305 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Deng L, Zeng W, Yang J, Zhu W, Lai J, Huang Y, Lv D, He Z, Lv Y, Qin G, Chen W. CTA-Based Carotid Plaque Reporting and Data System Classification Improves Risk Stratification for Ischemic Cerebrovascular Events. AJNR Am J Neuroradiol 2026;47:1483-91.. 10.3174/ajnr.A9139 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Qin R, Zheng C, Zhang Y, Feng M, Zhang S, Gai Q, Liu Z, Li T, Wang X, Lu J. Carotid Plaque-RADS improves preoperative coronary risk stratification in candidates for carotid revascularization. Insights Imaging 2026;17:18.. 10.1186/s13244-025-02188-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Bonati LH, Kakkos S, Berkefeld J, de Borst GJ, Bulbulia R, Halliday A, van Herzeele I, Koncar I, McCabe DJ, Lal A, Ricco JB, Ringleb P, Taylor-Rowan M, Eckstein HH. European Stroke Organisation guideline on endarterectomy and stenting for carotid artery stenosis. Eur Stroke J 2021;6:I-XLVII.. 10.1177/23969873211012121 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Grant EG, Benson CB, Moneta GL, Alexandrov AV, Baker JD, Bluth EI, Carroll BA, Eliasziw M, Gocke J, Hertzberg BS, Katanick S, Needleman L, Pellerito J, Polak JF, Rholl KS, Wooster DL, Zierler RE. Carotid artery stenosis: gray-scale and Doppler US diagnosis--Society of Radiologists in Ultrasound Consensus Conference. Radiology 2003;229:340-6.. 10.1148/radiol.2292030516 [DOI] [PubMed] [Google Scholar]
- 26. Sztajzel R. Ultrasonographic assessment of the morphological characteristics of the carotid plaque. Swiss Med Wkly 2005;135:635-43.. 10.4414/smw.2005.11038 [DOI] [PubMed] [Google Scholar]
- 27. Geroulakos G, Ramaswami G, Nicolaides A, James K, Labropoulos N, Belcaro G, Holloway M. Characterization of symptomatic and asymptomatic carotid plaques using high-resolution real-time ultrasonography. Br J Surg 1993;80:1274-7.. 10.1002/bjs.1800801016 [DOI] [PubMed] [Google Scholar]
- 28. Coli S, Magnoni M, Sangiorgi G, Marrocco-Trischitta MM, Melisurgo G, Mauriello A, Spagnoli L, Chiesa R, Cianflone D, Maseri A. Contrast-enhanced ultrasound imaging of intraplaque neovascularization in carotid arteries: correlation with histology and plaque echogenicity. J Am Coll Cardiol 2008;52:223-30.. 10.1016/j.jacc.2008.02.082 [DOI] [PubMed] [Google Scholar]
- 29. Kakkos SK, Griffin MB, Nicolaides AN, Kyriacou E, Sabetai MM, Tegos T, Makris GC, Thomas DJ, Geroulakos G, Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) Study Group . The size of juxtaluminal hypoechoic area in ultrasound images of asymptomatic carotid plaques predicts the occurrence of stroke. J Vasc Surg 2013;57:609-618.e1; discussion 617-8.. 10.1016/j.jvs.2012.09.045 [DOI] [PubMed] [Google Scholar]
- 30. Fedak A, Ciuk K, Urbanik A. Ultrasonography of vulnerable atherosclerotic plaque in the carotid arteries: B-mode imaging. J Ultrason 2020;20:e135-45.. 10.15557/JoU.2020.0022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Gupta A, Baradaran H, Schweitzer AD, Kamel H, Pandya A, Delgado D, Dunning A, Mushlin AI, Sanelli PC. Carotid plaque MRI and stroke risk: a systematic review and meta-analysis. Stroke 2013;44:3071-7.. 10.1161/STROKEAHA.113.002551 [DOI] [PubMed] [Google Scholar]
- 32. Pakizer D, Kozel J, Elmers J, Feber J, Michel P, Školoudík D, Sirimarco G. Histologically Verified Carotid Plaque Characteristics by Ultrasound: A Diagnostic Accuracy Systematic Review. Ultrasound Med Biol 2025;51:1646-59.. 10.1016/j.ultrasmedbio.2025.07.001 [DOI] [PubMed] [Google Scholar]
- 33. Alexandratou M, Papachristodoulou A, Li X, Partovi S, Davidhi A, Rafailidis V, Prassopoulos P, Kamperidis V, Koutroulou I, Tsivgoulis G, Grigoriadis N, Krogias C, Karapanayiotides T. Advances in Noninvasive Carotid Wall Imaging with Ultrasound: A Narrative Review. J Clin Med 2022;11:6196.. 10.3390/jcm11206196 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Grønholdt ML, Nordestgaard BG, Schroeder TV, Vorstrup S, Sillesen H. Ultrasonic echolucent carotid plaques predict future strokes. Circulation 2001;104:68-73.. 10.1161/hc2601.091704 [DOI] [PubMed] [Google Scholar]
- 35. Cheng L, Zheng S, Zhang J, Wang F, Liu X, Zhang L, Chen Z, Cheng Y, Zhang W, Li Y, He W. Multimodal ultrasound-based carotid plaque risk biomarkers predict poor functional outcome in patients with ischemic stroke or TIA. BMC Neurol 2023;23:13.. 10.1186/s12883-023-03052-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Sun J, Zhao XQ, Balu N, Neradilek MB, Isquith DA, Yamada K, Cantón G, Crouse JR, 3rd, Anderson TJ, Huston J, 3rd, O'Brien K, Hippe DS, Polissar NL, Yuan C, Hatsukami TS. Carotid Plaque Lipid Content and Fibrous Cap Status Predict Systemic CV Outcomes: The MRI Substudy in AIM-HIGH. JACC Cardiovasc Imaging 2017;10:241-9.. 10.1016/j.jcmg.2016.06.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Cui L, Liu R, Zhou F, Tian B, Chen Y, Xing Y. Incremental clinical value of intraplaque neovascularization in predicting recurrent ischemic stroke. Ann Clin Transl Neurol 2025;12:291-9.. 10.1002/acn3.52255 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Xia J, Yu C, Li L, Pan J. Arterial transit artifacts and carotid Plaque-RADS may predict symptoms in patients with carotid stenosis. Magn Reson Imaging 2024;111:131-7.. 10.1016/j.mri.2024.05.001 [DOI] [PubMed] [Google Scholar]
- 39. Rafailidis V, Chryssogonidis I, Xerras C, Grisan E, Cheimariotis GA, Tegos T, Rafailidis D, Sidhu PS, Charitanti-Kouridou A. An Ultrasonographic Multiparametric Carotid Plaque Risk Index Associated with Cerebrovascular Symptomatology: A Study Comparing Color Doppler Imaging and Contrast-Enhanced Ultrasonography. AJNR Am J Neuroradiol 2019;40:1022-8.. 10.3174/ajnr.A6056 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Ihle-Hansen H, Vigen T, Berge T, Walle-Hansen MM, Hagberg G, Ihle-Hansen H, Thommessen B, Ariansen I, Røsjø H, Rønning OM, Tveit A, Lyngbakken M. Carotid Plaque Score for Stroke and Cardiovascular Risk Prediction in a Middle-Aged Cohort From the General Population. J Am Heart Assoc 2023;12:e030739.. 10.1161/JAHA.123.030739 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Hatsukami TS, Ross R, Polissar NL, Yuan C. Visualization of fibrous cap thickness and rupture in human atherosclerotic carotid plaque in vivo with high-resolution magnetic resonance imaging. Circulation 2000;102:959-64.. 10.1161/01.cir.102.9.959 [DOI] [PubMed] [Google Scholar]
- 42. Takaya N, Yuan C, Chu B, Saam T, Underhill H, Cai J, Tran N, Polissar NL, Isaac C, Ferguson MS, Garden GA, Cramer SC, Maravilla KR, Hashimoto B, Hatsukami TS. Association between carotid plaque characteristics and subsequent ischemic cerebrovascular events: a prospective assessment with MRI--initial results. Stroke 2006;37:818-23.. 10.1161/01.STR.0000204638.91099.91 [DOI] [PubMed] [Google Scholar]
- 43. Ouyang Y, Lyu G, Huang S, Lin Y, Tong X. The predictive value of Plaque-RADS classification in two-dimensional carotid ultrasound for stroke risk stratification. Quant Imaging Med Surg 2026;16:156.. 10.21037/qims-2025-1152 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Zhu G, Hom J, Li Y, Jiang B, Rodriguez F, Fleischmann D, Saloner D, Porcu M, Zhang Y, Saba L, Wintermark M. Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease. Cardiovasc Diagn Ther 2020;10:1048-67.. 10.21037/cdt.2020.03.10 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Wagner B, Mukhija S, Kassem M, Wiencierz A, Müller MD, Gensicke H, Staub D, Wolff T, Mujagic E, Tsogkas I, Psychogios M, Kooi ME, Engelter ST, Lyrer P, Bonati L, BIOPLAQUE Collaborative Group. BIOPLAQUE Investigators. Diagnostic accuracy of gray-scale analysis on B-mode ultrasound for identifying intraplaque hemorrhage and lipid-rich necrotic core in carotid plaques. Vasc Med 2026;31:175-84.. 10.1177/1358863X251410527 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Chen CY, Weng WC, Wu CL, Huang WY. Association between gender and stoke recurrence in ischemic stroke patients with high-grade carotid artery stenosis. J Clin Neurosci 2019;67:62-7.. 10.1016/j.jocn.2019.06.021 [DOI] [PubMed] [Google Scholar]
- 47. Haynes WG. Hyperhomocysteinemia, vascular function and atherosclerosis: effects of vitamins. Cardiovasc Drugs Ther 2002;16:391-9.. 10.1023/a:1022130217463 [DOI] [PubMed] [Google Scholar]
- 48. Watanabe K, Sugiyama S, Kugiyama K, Honda O, Fukushima H, Koga H, Horibata Y, Hirai T, Sakamoto T, Yoshimura M, Yamashita Y, Ogawa H. Stabilization of carotid atheroma assessed by quantitative ultrasound analysis in nonhypercholesterolemic patients with coronary artery disease. J Am Coll Cardiol 2005;46:2022-30.. 10.1016/j.jacc.2005.04.070 [DOI] [PubMed] [Google Scholar]
- 49. Kwee RM, van Oostenbrugge RJ, Prins MH, Ter Berg JW, Franke CL, Korten AG, Meems BJ, van Engelshoven JM, Wildberger JE, Mess WH, Kooi ME. Symptomatic patients with mild and moderate carotid stenosis: plaque features at MRI and association with cardiovascular risk factors and statin use. Stroke 2010;41:1389-93.. 10.1161/STROKEAHA.109.575670 [DOI] [PubMed] [Google Scholar]
- 50. Dai N, Tang X, Hu Y, Lu H, Chen Z, Duan S, Guo W, Edavi PP, Yu Y, Huang D, Qian J, Ge J. SARS-CoV-2 Infection Association with Atherosclerotic Plaque Progression at Coronary CT Angiography and Adverse Cardiovascular Events. Radiology 2025;314:e240876.. 10.1148/radiol.240876 [DOI] [PubMed] [Google Scholar]
- 51. Varga Z, Flammer AJ, Steiger P, Haberecker M, Andermatt R, Zinkernagel AS, Mehra MR, Schuepbach RA, Ruschitzka F, Moch H. Endothelial cell infection and endotheliitis in COVID-19. Lancet 2020;395:1417-8.. 10.1016/S0140-6736(20)30937-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
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