Diagnostic accuracy of superb microvascular imaging for detecting intraplaque neovascularization: a systematic review and meta-analysis
Original Article

Diagnostic accuracy of superb microvascular imaging for detecting intraplaque neovascularization: a systematic review and meta-analysis

Lina Zhao1, Yubo Han1, Yanling Che1, Xuesong Zhang1, Min Luo1, Jinzhu Li2

1Department of Ultrasound Medicine, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China; 2Department of Ultrasound Medicine, The Second Hospital of Heilongjiang Province, Harbin, China

Contributions: (I) Conception and design: L Zhao, J Li; (II) Administrative support: L Zhao, Y Han; (III) Provision of study materials or patients: Y Che, X Zhang; (IV) Collection and assembly of data: Y Han, Y Che; (V) Data analysis and interpretation: Y Han, Y Che; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Jinzhu Li, MD. Department of Ultrasound Medicine, The Second Hospital of Heilongjiang Province, No. 209 Jiangdu Street, Songbei District, Harbin 150028, China. Email: xingzhuyu@sina.com.

Background: Atherosclerotic plaques can cause carotid artery stenosis, and “vulnerable plaques” can even lead to ischemic stroke. The objective of this study was to assess the accuracy of superb microvascular imaging (SMI) for the detection of carotid intraplaque neovascularization (IPN) in patients with atherosclerotic plaques.

Methods: We searched the Cochrane Library, Embase, Medline, and Wanfang databases until January 17, 2023. We included original studies with information on diagnostic accuracy of SMI for the evaluation of carotid IPN. The primary outcome was the accuracy of SMI for detecting carotid IPN. A meta-analysis was performed to estimate the accuracy of each parameter. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the risk of bias for each included article. Meta-regression was performed to determine items that may have contributed to heterogeneity in the sensitivity or specificity of the test.

Results: This meta-analysis included 20 studies with 1,589 carotid plaques in 1,225 patients. The analysis showed a sensitivity and specificity of SMI for detecting IPN of 93% [95% confidence interval (CI): 87–96%] and 80% (95% CI: 71–87%), respectively. The risk of bias across the QUADAS-2 domains was low. Only the proportion of dyslipidemia influenced the estimates of sensitivity and specificity.

Conclusions: This review suggests that SMI has a good diagnostic performance for detecting carotid IPN. The very high sensitivity with excellent post-test probability indicated that SMI can be recommended to screen for carotid IPN among patients with carotid plaques.

Keywords: Superb microvascular imaging (SMI); contrast-enhanced carotid ultrasonography (CEUS); carotid plaque; diagnostic accuracy; systematic review

Submitted May 08, 2023. Accepted for publication Sep 22, 2023. Published online Oct 24, 2023.

doi: 10.21037/cdt-23-202

Highlight box

Key findings

• This meta-analysis shows that superb microvascular imaging (SMI) has a sensitivity of 93% [95% confidence interval (CI): 87–96%] and a specificity of 80% (95% CI: 71–87%) for detecting intraplaque neovascularization (IPN), making it a good tool for diagnosis.

What is known and what is new?

• IPN is a known risk factor for atherosclerotic plaque vulnerability.

• Contrast-enhanced ultrasonography (CEUS) and SMI are both tools for detecting IPN, but SMI is less invasive.

• This meta-analysis synthesized data to assess the accuracy of SMI for detecting IPN.

What is the implication, and what should change now?

• SMI is a less invasive alternative to CEUS for the purposes of diagnosing IPN. Additional large-scale studies should be performed to confirm our findings using pathological results as the reference test.

Introduction

Background

Atherosclerotic disease (myocardial infarction and stroke) is one of the leading causes of death worldwide (1-3). Atherosclerotic plaques can cause carotid artery stenosis, and “vulnerable plaques” can even lead to ischemic stroke due to plaque rupture and thrombi formation (4). Therefore, to optimize the management of atherosclerotic disease, evaluation of atherosclerotic plaque vulnerability is vital.

Rationale and knowledge gap

Intraplaque neovascularization (IPN) is a known risk factor for atherosclerotic plaque vulnerability (5). Ultrasound imaging techniques, including contrast-enhanced carotid ultrasonography (CEUS) and non-invasive superb microvascular imaging (SMI), can effectively indicate IPN features in patients with carotid stenosis. SMI does not use contrast agents while CEUS uses adaptive principles to display low-velocity blood flow signals (6). Two recently published meta-analysis demonstrated that SMI and CEUS display excellent diagnostic value for detecting carotid IPN (7,8). However, the analysis could not draw conclusions regarding the accuracy of SMI since the sample sizes in the relevant studies were small (7,8).

Objective

To the best of our knowledge, there are no reports of synthesized data on the accuracy of SMI for detecting IPN. The objective of this study was to assess the accuracy of SMI in the detection of carotid IPN in patients with atherosclerotic plaques via conducting a meta-analysis of the relevant literature. We present this article in accordance with the PRISMA-DTA reporting checklist (9) (available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-202/rc).

Methods

This systematic review and meta-analysis did not require the approval of a Research Ethics Board because we used published evidence in our data analysis. The study was registered at PROSPERO (No. CRD42023399416). A protocol was not prepared.

Eligibility criteria

The inclusion criteria for this meta-analysis were as follows: (I) original studies [randomized controlled trials (RCTs), cohort studies, cross-sectional studies, and case-control studies]; (II) patients with carotid plaques; (III) having information on the diagnostic accuracy of SMI for the evaluation of carotid IPN; and (IV) having information on pathologic evaluations or CEUS as the reference test.

The exclusion criteria were as follows: (I) duplicate publications; (II) study design was a systematic review, meta-analysis, editorial, protocol, letters, or case reports; (III) full text was not available; and (IV) studies without sufficient data to perform SMI accuracy assessment.

Literature search

We searched the Cochrane Library, Embase, Medline, and Wanfang databases until January 17, 2023. The full search strategy is presented in Appendix 1. English was applied as the language restriction when searching the Cochrane Library, Embase and Medline. The key search terms included “carotid”, “plaque”, “fatty streak”, “fibroatheroma”, “neovascularization”, and “superb microvascular imaging”. The bibliographies of the related papers (reviews, meta-analysis, and potential eligible studies) were checked to find other potential articles. We also checked published conference proceedings for eligible data or references.

Study selection

Two independent reviewers (Zhao L and Han Y) performed title and abstract screening to generate a potentially relevant study list according to the eligibility criteria. The full texts of these papers were then reviewed to further confirm the eligibility of the studies. Any inconsistency between the two reviewers was resolved by discussion or referred to a third reviewer (Li J). The study selection workflow is shown in Figure 1.

Figure 1 PRISMA flow diagram for article selection for meta-analysis.

Data collection

Two study team members (Zhao L and Han Y) independently performed the data extraction using a pre-test data collection form. Discrepancies were resolved by discussion or by referral to a third reviewer. The data collection included information for authors, publication year, study country, study design, patient demographics, SMI-related data, and index test information.

Outcomes

The primary outcome of the study was the accuracy of SMI for the detection of carotid IPN in patients with atherosclerotic plaques, which was measured using sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR) analyses. The definitions and equations of each parameter are described in Appendix 2.

In this study, we applied both histology results and the CEUS results as reference tests to define the target condition of carotid IPN. For the different categories of carotid IPN for SMI, CEUS, and pathologic evaluation results, we used light (spot IPN), moderate (linear IPN), and severe (multiple linear IPNs) diagnoses as the positive results, and no IPN as a negative result.

Study risk of bias assessment

Two study team members (Zhao L and Han Y) independently performed the risk of bias analysis using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria (10). The QUADAS-2 tool includes four primary parameters: patient selection, index test, reference standards, and methodological quality. Quality was graded as “no” for low quality, “yes” for high quality, or “unclear” if the information was not available.

Statistical analysis and synthesis methods

We performed the statistical analysis of SMI accuracy according to the Cochrane guidelines for diagnostic test accuracy reviews (11). All data analyses were performed using the STATA software, version 15.0 (Stata Corp., College Station, USA) (12). The STATA commands METANDI and MIDAS were used to perform the meta-analysis. We applied forest plots and receiver operating characteristic (ROC) plots to visualize the variation between sensitivity and specificity of SMI and their 95% confidence intervals (CIs) for detecting IPN. The bivariate random effects model was used to summarize sensitivity and specificity. The sensitivity, specificity, LR+, LR−, and DOR with their 95% CIs were achieved. We applied Cochran’s Q-statistic and I2 tests to evaluate potential heterogeneity between studies. The random effects model was applied if significant heterogeneity (P<0.1 for Q test or I2 test exceeded 50%) was detected; otherwise, we used the fixed effects model.

Subgroup analyses were performed to explore variation in test performance according to different reference indexes (CEUS and pathologic evaluation). These estimates were used to indirectly compare each parameter of accuracy by checking the overlap of the 95% CIs.

Fagan’s nomogram was used to estimate the clinical value of SMI for detecting IPN. We used this analysis to evaluate how much the result of SMI changed the probability that a plaque has IPN. Deeks’ funnel plot was used to assess publication bias (9). Meta-regression analysis was applied to investigate potential heterogeneity by including variables of age, sex, thickness of the plaques, percentage of having hypertension (HTN), percentage of having diabetes mellitus, dyslipidemia, and probe velocity scale.

Results

Study selection

A total of 103 articles were identified after searching the databases, and five more articles were found after checking the bibliographies of the potential related papers. After omitting duplicated studies, 74 articles were further screened for title and abstract. Of these, 28 articles were selected for full-text review. Finally, 20 articles were included in the data quality assessment and data analysis (6,13-31). The study selection process is shown in Figure 1 (32).

Study characteristics

A total of 1,225 patients with 1,589 carotid plaques were studied in all included papers. The mean age of the included patients ranged from 59.3 to 71.0 years. The study patients had >50% carotid stenosis and maximum carotid plaque thickness >1.5 mm. Most studies used the Toshiba Aplio500 and linear probes with slightly different MHz (range, 3–12 MHz) to detect plaques. The study characteristics of the included studies are summarized in Table 1.

Table 1

The characteristics of the included studies

First author [year], country Study design Patient type Sample size, n Age (years), mean or specified Male, n (%) DM/HTN/smoking/dyslipidemia, n Plaques, n Reference test Machine/probe of SMI
Zamani et al. [2019], Norway (6) One-arm CS Carotid stenosis ≥50% 31 70.0 20 (64.5) 3/21/17/13 31 Plaque histology Toshiba Aplio500/L 7.5 MHz
Chen et al. [2016], China (13) One-arm CS Carotid plaque thickness >1.5 mm 56 NA NA NA 80 CEUS Toshiba Aplio500/L 4–11 MHz
Chen et al. [2020], China (14) One-arm CS Carotid stenosis ≥50% 28 63.4 22 (78.6) 12/23/18/18 28 Plaque histology Toshiba Aplio500/L 4–9 MHz
Cheng et al. [2015], China (15) One-arm CS Carotid plaque thickness 2.6–5.7 mm 57 61.8 44 (77.2) 33/42/NA/31 33 Enhanced CEUS GE Logid E9/L 7–10 MHz
13 Plaque histology
Ding et al. [2020], China (16) One-arm CS Carotid plaque thickness >1.5 mm 89 68.5 45 (50.6) NA 89 CEUS Toshiba Aplio500/L 7.5 MHz
Forsberg et al. [2019], USA (17) One-arm CS Carotid plaque thickness >2 mm 30 71.0 12 (40.0) NA 28 Plaque histology Toshiba Aplio500/L 7.5 MHz
Guo et al. [2023], China (18) One-arm CS Carotid plaque thickness >2 mm 45 64.5 24 (53.3) 21/38/26/32 76 CEUS Toshiba Aplio500/L 4–11 MHz
He et al. [2018], China (19) One-arm CS Carotid plaque thickness ≥1.5 mm 40 64.2 25 (62.5) NA 72 CEUS Toshiba Aplio500/L 7–12 MHz
Huang et al. [2015], China (20) One-arm CS Carotid plaque thickness >2 mm 62 61.6 50 (80.6) NA 103 CEUS Toshiba Aplio500/L 5–10 MHz
Jin et al. [2017], China (21) One-arm CS Carotid plaque thickness >2 mm 146 67.6 76 (52.1) NA 146 CEUS Toshiba Aplio500/L 4–9 MHz
Liu et al. [2016], China (22) One-arm CS Carotid plaque thickness >3 mm 52 65.4 40 (76.9) NA 67 CEUS Toshiba Aplio500/NA
Ma et al. [2018], China (23) One-arm CS Carotid plaque thickness ≥1.5 mm 46 61.7 34 (73.9) NA 55 CEUS Toshiba Aplio500/L 4–9 MHz
Meng et al. [2021], China (24) One-arm CS Carotid plaque thickness >2.5 mm 78 67.3 63 (80.8) 28/59/39/46 104 CEUS Toshiba Aplio500/L 3–9 MHz
Oura et al. [2018], Japan (25) One-arm CS Carotid plaque thickness ≥2 mm 27 71.0 (median) 25 (92.6) 14/18/NA/17 27 CEUS Toshiba Aplio500/L 7.5 MHz
Tang et al. [2019], China (26) One-arm CS Carotid plaque thickness >2 mm 92 66.4 52 (56.5) NA 92 CEUS Toshiba Aplio500/L 7.5 MHz
Wang et al. [2021], China (27) One-arm CS Carotid plaque thickness ≥1.5 mm 100 62.3 63 (63.0) 32/88/43/NA 134 CEUS Toshiba Aplio500/NA
Xie et al. [2018], China (28) One-arm CS Carotid plaque thickness ≥2.5 mm 69 68.1 53 (76.8) 21/51/31/47 108 CEUS Toshiba Aplio500/L 4–9 MHz
Yi et al. [2018], China (29) One-arm CS Carotid plaque thickness ≥1.5 mm 95 59.3 56 (58.9) NA 157 CEUS Toshiba Aplio500/L 4–9 MHz
Zhang et al. [2017], China (30) One-arm CS Carotid plaque thickness ≥2 mm 39 66.8 31 (79.5) NA 64 CEUS Toshiba Aplio500/L 4–11 MHz
Zhu et al. [2019], China (31) One-arm CS Carotid plaque thickness >1.5 mm 43 66.0 26 (60.5) 23/23/20/28 82 CEUS Toshiba Aplio500/L 7.5 MHz

DM, diabetes mellitus; HTN, hypertension; SMI, superb microvascular imaging; One-arm CS, one-arm comparative study; NA, not available; CEUS, contrast-enhanced carotid ultrasonography.

Risk of bias and applicability

The methodological quality for each included study and the overall summary of the analysis are shown in Figure 2. The risk of bias of the flow and timing domain in the final analysis was reported in three studies (14,15,17). Another risk of bias came from the reference test. Although the CEUS can diagnose IPN with high accuracy, it was not used for final pathological results. Therefore, we defined it as an unclear risk of bias in 17 included studies (Figure 2).

Figure 2 Risk of bias and applicability concerns summary. (A) Review of authors’ judgements for each domain of each included study. (B) Review of authors’ judgements for each domain presented as percentages across included studies.

Accuracy of SMI for detecting IPN

Overall, the sensitivity and specificity of SMI for detecting IPN were 93% (95% CI: 87–96%) and 80% (95% CI: 71–87%), respectively. We applied the random effects model due to the high heterogeneity in the results of the included studies, which is shown in a forest plot (Figure 3). In addition, the LR+, LR−, and DOR were 4.75 (95% CI: 3.16–7.15), 0.09 (95% CI: 0.05–0.16), and 52.4 (95% CI: 26.6–103.0), respectively. The area under the summary ROC curve (AUC) was 0.93 (95% CI: 0.91–0.95), and the graphs of the hierarchical summary ROC (HSROC) curves of the individual studies for the diagnostic accuracy of the test analyzed are shown in Figure 4.

Figure 3 Forest plot showing the pooled accuracy of SMI for detecting IPN. CI, confidence interval; SMI, superb microvascular imaging; IPN, intraplaque neovascularization.
Figure 4 HSROC plot displaying SMI diagnostic accuracy for detecting IPN of the included studies. Summary sensitivity and specificity are marked by red squares. HSROC, hierarchical summary receiver operating characteristic; SMI, superb microvascular imaging; IPN, intraplaque neovascularization.

Subgroup analysis

We performed subgroup analyses according to the different reference tests (pathological results versus CEUS). Details of the accuracy data are listed in Table 2. According to the current value of each diagnostic accuracy parameter, the accuracy of the CEUS group was better than the pathological group. However, according to the indirect comparison, there was no statistical difference between the two groups. Meta-regression analysis showed that age, male (proportion), smoking (proportion), HTN (proportion), different reference tests, different machine types, and probe velocity scale did not contribute to heterogeneity among studies; only dyslipidemia (proportion) contributed to study heterogeneity (Figure 5).

Table 2

Subgroup analysis of the diagnostic accuracy of SMI for detecting IPN according to different reference tests

Accuracy Reference tests
CEUS Pathological results
Sensitivity, % (95% CI) 93.6 (88.1–96.7) 86.5 (58.9–96.6)
Specificity, % (95% CI) 80.9 (69.8–88.6) 78.8 (45.4–94.3)
LR+, ratio (95% CI) 4.90 (3.06–7.82) 4.07 (1.11–4.89)
LR−, ratio (95% CI) 0.08 (0.04–0.14) 0.17 (0.04–0.76)
DOR, ratio (95% CI) 62.3 (32.0–121.2) 23.8 (1.73–327.80)

SMI, superb microvascular imaging; IPN, intraplaque neovascularization; CEUS, contrast-enhanced carotid ultrasonography; CI, confidence interval; LR+, positive likelihood ratio; LR−, negative likelihood ratio; DOR, diagnostic odds ratio.

Figure 5 Univariate meta-regression analyses of the sensitivity and specificity. *, P<0.05. HTN, hypertension; CI, confidence interval.

Post-test probability

A pretest probability of 20% of SMI for detecting IPN was fixed, which was defined from the number of IPN cases in the included studies. SMI for detecting IPN had a post-test probability of 54%. Thus, with a prevalence of 20% for IPN, the post-test probability that a patient truly has IPN would be 54% if this patient tests positive. In contrast, the post-test probability that a patient truly has IPN would be 2% if the patient tests negative (Figure 6).

Figure 6 Fagan’s nomogram for the calculation of post-test probabilities. LR, likelihood ratio; Prob, probability; Pos, positive; Neg, negative.

Publication bias

Although significant heterogeneity was detected for the test (80.2% for sensitivity and 68.2% for specificity), the funnel plots showed that there was no potential publication bias (P=0.14) (Figure 7).

Figure 7 Funnel plot asymmetry test for evaluation of potential publication bias. ESS, excess statistical significance.

Discussion

Key findings

This systematic review showed that SMI can be a highly accurate technique for detecting IPN according to the meta-analysis results for each key accuracy parameter. This meta-analysis of 20 studies with 1,589 carotid plaques in 1,225 patients reported a summary sensitivity and specificity of SMI for detecting IPN of 93% (95% CI: 87–96%) and 80% (95% CI: 71–87%), respectively. Summary estimates of each diagnostic accuracy parameter of the CEUS group were better than the pathological group. However, according to the indirect comparison, there was no statistical difference between the two groups. The risk of bias across QUADAS domains was low; only the proportion of dyslipidemia influenced the estimates of sensitivity and specificity.

Explanation of findings

The present study demonstrated that the sensitivity of SMI for detecting IPN was 93% (87–96%), which means the test is suitable as a screening tool for IPN in patients with carotid plaques (33). The high specificity of the test [80% (95% CI: 71–87%)] indicates a good capacity for diagnosing IPN in the clinic (33). Both the DOR [52.4 (95% CI: 26.6–103.0)] and the AUC [0.93 (95% CI: 0.91–0.95)] confirmed that SMI can effectively detect IPN (34,35).

Comparison with similar research

Previous studies have confirmed the validity of CEUS for detecting IPN (36,37). However, CEUS has the limitations of high cost and invasive characteristics of the contrast agent. Furthermore, compared to SMI, CEUS does not define the direction or velocity of blood flow (38,39). According to a recent meta-analysis, there is high consistency between SMI and CEUS for diagnosing IPN (7,8). However, no studies have reported the accuracy of SMI for detecting IPN. In this study, we applied CEUS as the reference test, in addition to using pathological evaluation standards, because CEUS has a high diagnosis validity for detecting IPN. However, considering the potential discrepancy between the real diagnosis and the results of the CEUS detection, we also performed subgroup analysis. According to our analysis, the accuracy estimates of SMI based on the CEUS results are better than those based on the pathological results. However, there was no statistical difference between the two groups, as there was overlap in the 95% CIs for each parameter. According to the meta-regression analysis (Figure 5), the difference in the reference tests did not contribute to the heterogeneity of the sensitivity and the specificity through the included studies. However, this may be due to the small sample sizes of the included studies for the pathological results group. Future studies with large sample sizes for pathological results as the reference test are needed to confirm our findings.

An interesting finding of this study was that among the variables that could influence SMI’s ability to detect IPN, only the proportion of dyslipidemia contributed significantly to the heterogeneity for the sensitivity and specificity analyses (Figure 4). It has been reported that patients with dyslipidemia have a higher prevalence of carotid plaques (40-43). Thus, the apparent increased IPN prevalence due to the increased prevalence of carotid plaques can influence the sensitivity and specificity (44).

Implications of results

The results of the present study provide new insight for the clinical management of IPN. If patients have a positive SMI result for detecting IPN, the post-test likelihood that the patient has IPN will be 54% (given that the population prevalence in this study was 20%). These patients may need further tests to confirm the diagnosis. On the other hand, if a patient has a negative SMI result, the probability of having the condition of IPN will be 2%. This analysis indicates that SMI is an excellent tool for detecting IPN. Further research and high-quality studies on this subject are needed to confirm our findings.

In clinical practice, plaques are categorized as homogeneous and heterogeneous using ultrasonography. It is generally believed that homogeneous plaques are mostly stable with uniform internal echoes and intensity. Stable plaques are characterized by a smooth surface, regular shape, uniform and strong echoes, as well as a stable internal tissue structure. Heterogeneous plaques are mostly unstable. Unstable plaques are characterized by irregular morphology, non-smooth surface, low or predominantly low echo, and some plaques have a detectable juxtaluminal black area, which is mostly due to hemorrhage and ulceration. The surface of the plaque will have at least two sections with detectable depressions; the depressions will be bordered, the surface of the plaque will be markedly defective, and color Doppler can be used to visualize the formation of filling defects.

To distinguish homogeneous plaques from heterogeneous plaques, clinicians can only rely on the appearance of the plaque and the homogeneity of the echoes to determine whether the plaque is stable or not. Furthermore, this determination is highly subjective and uncertain and lacks a prospective approach for evaluating clinical events, which can result in the occurrence of acute events, such as strokes, that cannot be predicted. In clinical practice, most hypoechoic or isoechoic plaques with smooth borders and intact fibrous caps are treated conservatively as stable plaques. However, not all of these plaques are actually stable, and some may have undergone neovascularization, which cannot be accurately detected by conventional ultrasound and Doppler ultrasound techniques. Moreover, neither of these techniques can accurately obtain clinically meaningful blood flow information, making it difficult to judge plaque stability.

Strengths and limitations

The present review is, to the best of our knowledge, the first meta-analysis to summarize the accuracy of SMI for detecting IPN. We applied rigorous methodology in this systematic review. However, there are still some limitations to our analysis. First, only a few of the included studies provided pathological results to confirm the test results, which may lead to an overestimation of the accuracy of SMI. In addition, the prevalence of IPN varied across the included studies, which may have significantly influenced the overall estimates of sensitivity and specificity. The protocol which was uploaded to PROSPERO contains some inaccuracies, which we were regrettably not able to properly update. A version of the protocol in line with the review’s methods can be found in the supplementary file (available at https://cdn.amegroups.cn/static/public/cdt-23-202-1.pdf). Finally, most of the studies were from China, which may have limited the external validity of the findings.

Conclusions

In summary, the present review suggests that SMI is a non-invasive ultrasound method that has a good diagnostic performance for detecting IPN. The high sensitivity and excellent post-test probability indicated that SMI can be recommended to screen for IPN among patients with carotid plaques. Additional large-scale studies should be performed to confirm our findings using pathological results as the reference test.

Acknowledgments

The authors would also like to thank Medjaden Bioscience Limited for their assistance in polishing the language of the manuscript.

Funding: This work was supported by the National Natural Science Foundation of China (Youth Science Foundation Program) (Batch No. 82205060). The funding bodies had no role in the design of the study, the collection, analysis, or interpretation of the data, or writing of the manuscript.

Footnote

Reporting Checklist: The authors have completed the PRISMA-DTA reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-202/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-202/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-202/coif). The authors have no conflicts of interest to declare.

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.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Zhao L, Han Y, Che Y, Zhang X, Luo M, Li J. Diagnostic accuracy of superb microvascular imaging for detecting intraplaque neovascularization: a systematic review and meta-analysis. Cardiovasc Diagn Ther 2023;13(5):893-905. doi: 10.21037/cdt-23-202

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