Diagnostic value of greyscale ultrasound combined with superb microvascular imaging in thyroid nodules: a systematic review and meta-analysis

Introduction

The prevalence of thyroid nodules has increased in recent years and can reach 68% in adults detected by high-resolution ultrasound (US) (1). However, most thyroid nodules are benign with only 10–15% of nodules being malignant and requiring clinical intervention (2). Preoperatively determining whether a thyroid nodule is benign or malignant has a direct impact on clinical decision-making.

US is the preferred imaging method for thyroid nodules. Several guidelines for US, such as the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) (3), the American Thyroid Association (ATA) Risk Stratification System (1), the Korean Society of Thyroid Radiology (KSThR) TI-RADS (4), and the Chinese TI-RADS (5), have been developed and are commonly used to help evaluate the malignancy risk of thyroid nodules. Although the details of these classifications vary, US can be used to evaluate the thyroid nodules in terms of composition, echogenicity, shape, margin, and calcification. Features such as solid composition, hypoechogenicity, taller-than-wide shape, irregular margin, and microcalcification are commonly associated with malignant thyroid nodules (1,3-5). However, studies have shown that the greyscale features of benign and malignant thyroid nodules often overlap (6,7). Vascular information is considered valuable in diagnosing thyroid cancer since the development of tumors depends heavily on the angiogenesis (8). However, color Doppler flow imaging (CDFI) is limited in its ability to detect microvessels smaller than 0.1 mm in diameter or microflows with velocities less than 1 mm/s (9). Some scholars have explored the role of CDFI in differentiating between benign and malignant thyroid nodules but have drawn different conclusions (10-13). The low sensitivity and high interobserver variability of CDFI have hindered its inclusion in the aforementioned US classification system.

Superb microvascular imaging (SMI) is the new generation of Doppler flow imaging with Toshiba’s/Canon’s Aplio series US scanners (Toshiba/Canon Medical Systems Corporation, Tochigi, Japan). SMI is employed for depicting tiny vessels and microflows at low velocities (Figure 1), enabling true microflow to be distinguished from the tissue movement and motion artifacts to be removed effectively with a new algorithm. The developed wall filter benefits clutter suppression while retaining low-flow signals that CDFI may miss. Moreover, SMI is convenient and safe because it does not require contrast agents. Thus, SMI can facilitate the application of vascular characteristics as indicators for differentiating between benign and malignant thyroid nodules. Some scholars have also discovered that SMI is helpful in predicting central compartment lymph node metastasis in thyroid cancer (14).

Figure 1 Greyscale US, CDFI, and SMI images of a papillary thyroid carcinoma of classic variant in a 28-year-old female patient. (A) Greyscale US showed a solid hypoechoic nodule with ill-defined margin. “+” means calipers; “A” means the vertical diameter of the nodule; “B” means the anteroposterior diameter of the nodule. (B) CDFI showed several dot-like and rod-like flows at the periphery of the nodule. (C) Color SMI showed predominantly perinodular vascularity. (D) Monochrome SMI showed more peri- and intranodular microflows. US, ultrasound; CDFI, color Doppler flow imaging; SMI, superb microvascular imaging.

Several meta-analyses that focused on the diagnostic validity of SMI in thyroid cancer have been published previously (15-18). However, there are several limitations, such as the lack of subgroup analysis according to different SMI diagnostic criteria, and the analysis of the diagnostic validity of SMI alone, which restrict the clinical application of SMI. Therefore, we used a meta-analysis to assess the diagnostic performance of greyscale US combined with SMI for differentiation between benign and malignant thyroid nodules from multiple aspects of SMI in this study. We present this article in accordance with the PRISMA-DTA reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1195/rc).

Methods

This meta-analysis was registered under PROSPERO with the registration number CRD42023495143.

Systematic literature research

A systematic search of PubMed, Embase, Cochrane Library, Scopus, and Web of Science was conducted to identify relevant studies that differentiated between benign and malignant thyroid nodules with greyscale US combined with SMI. Only papers in English and published up to 25 October 2023 were included. The search terms used were ((Thyroid Neoplasm) OR (Thyroid Carcinoma) OR (Thyroid Cancer) OR (thyroid nodule) OR (thyroid tumor) OR (thyroid lesion)) AND ((superb microvascular imaging) OR (SMI)). The inclusion criteria for the studies were as follows: (I) studies with more than 50 thyroid nodules; (II) all the patients underwent preoperative thyroid US that combined greyscale imaging with SMI; (III) all the nodules had definite pathological results, and patients with only cytopathological benign nodules had at least 6 months of follow-up without significant change over time; (IV) studies were prospective or retrospective; and (V) studies with sufficient data including true positive (TP), false positive (FP), false negative (FN), and true negative (TN) which could be obtained directly or indirectly. The exclusion criteria of the studies were (I) reviews, case reports, conference abstracts, theses, editorials and letters to the editor; (II) studies about no human subjects; (III) articles that were not published in English; (IV) raw data of the studies could not be extracted; (V) patients were not examined by greyscale US in combination with SMI; and (VI) repeated publication of data. Two reviewers independently conducted the literature selection process. When there was a disagreement, a third reviewer evaluated during a consensus meeting with the other two reviewers. Endnote 20.0 (Clarivate Analytics, Philadelphia, PA, USA) was used for literature management.

Evaluation index

Vascular features of thyroid nodules included richness, distribution, and penetrating vessels via SMI. Despite variations in the classifications used to evaluate vascularity, all eligible studies were included considering the novelty of SMI technology and the paucity of the studies. The vascular richness of SMI was measured using factors such as the number of vessels, vascular index (VI), and semiquantitative classification such as Adler’s grade (19). The vascular distribution was divided into three types: mainly peripheral vascularity, mainly central vascularity, and mixed vascularity (the abundance of both central and peripheral vessels in the nodules is similar) (4,5). The penetrating vessel refers to those moving from the outside to the inside of the nodule (20). All the above vascular features were acquired from Toshiba/Canon Aplio US scanners (Toshiba/Canon Medical Systems Corporation) equipped with SMI technology.

Data extraction and quality assessment

Two reviewers extracted the relevant data independently. Data such as the first author, publication year, study design, case number, age of patient, US instrument, the gold standard, number of thyroid nodules, SMI parameters, the malignant and benign sign on SMI, method of greyscale US combined with SMI and TP, FP, FN, TN were obtained from each study. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) (21) was used for quality assessment. Disagreement was solved by consensus. The quality assessment plot was acquired by RevMan5.3 (Cochrane Collaboration, London, UK).

Statistical analysis and data synthesis

Meta disc 1.4 (Universidad Complutense, Madrid, Spain) and STATA 16.0 (Stata Corporation, College Station, TX, USA) were used for threshold effects analysis and other statistical analysis. Subgroup analysis according to different SMI diagnostic criteria for malignant thyroid nodules was made. Per-lesion was the unit of assessment. The heterogeneity caused by threshold effects was defined as P<0.05 of Spearman correlation coefficients. The summary sensitivity, specificity, positive likelihood ratio (LR⁺), negative likelihood ratio (LR⁻), diagnostic odds ratio (DOR), and respective 95% confidence interval (CI) were calculated by TP, FP, FN, and TN of each study. The summary receiver operating characteristic (SROC) curve was drawn and the area under the curve (AUC) was obtained. Q-test or I² index were used to examine the heterogeneity among the studies. If a high heterogeneity was detected (P<0.1 or I² index >50%), a random-effects model was applied and meta-regression analysis was used for the source of heterogeneity. The robustness of the results was assessed by the sensitivity analysis. The publication bias was evaluated by Deeks’ funnel plot asymmetry test.

Results

Search results

The literature search process is shown in Figure 2. Initially, 437 studies were included in the selection after the search of PubMed, Embase, Cochrane Library, Scopus, and Web of Science databases. Eighty-nine duplicated studies were excluded. Three hundred and fifteen studies were removed after reviewing the titles and abstracts. After reviewing the full text of the selected 33 studies, 10 studies were finally included in our meta-analysis.

Figure 2 Flowchart of the study selection process.

Characteristics of included studies

The main characteristics of the 10 included studies are summarized in Table 1. A total of 1,160 thyroid nodules (including 556 malignant nodules and 604 benign nodules) were evaluated. All the studies were based on single-center. Five studies were prospective and the rest were retrospective. Two studies only included TI-RADS 4 nodules (26,28), one of which only evaluated nodules less than 1 cm (26). The rest studies included all the nodules. As for SMI parameters, six studies evaluated vascular richness, eight assessed vascular distribution, and four involved the penetrating vessel. In the way that greyscale US combined with SMI, five studies reassessed the TI-RADS grade according to vascular features [two studies used the ACR TI-RADS (3), one study used the Kwak-TI-RADS (32), one study used the KSThR TI-RADS (4), and one study used the Chinese TI-RADS (5)]. Two studies adjusted the malignancy risk of nodules according to vascular features. One study established a model to evaluate the nodules based on the greyscale and SMI features. The combination method was unclear in the rest two studies. QUADAS-2 (21) tool was used for quality assessment, and results showed a low risk of bias of the included studies (Figure 3).

Figure 3 The assessment of the risk of bias for included studies.

Diagnostic performance of greyscale US combined with SMI

We included 10 studies to evaluate the diagnostic performance of greyscale US combined with SMI in differentiating between benign and malignant thyroid nodules. Threshold effects analysis showed obvious heterogeneity (Spearman coefficient 0.697; P=0.025). So only the SROC curve and AUC were used to assess the diagnostic tests. A random-effects model was applied and the SROC curve was drawn (Figure 4A). The AUC was 0.92 (95% CI: 0.89–0.94). No significant publication bias existed among these studies (Figure 4B; P=0.13).

Figure 4 Diagnostic performance of greyscale US combined with SMI in differentiating benign and malignant thyroid nodules. (A) SROC curve. The data in square brackets represent 95% CI. (B) Deeks’ funnel plot. SROC, summary receiver operating characteristic; SENS, sensitivity; SPEC, specificity; AUC, area under the curve; ESS, effective sample size; US, ultrasound; SMI, superb microvascular imaging; CI, confidence interval.

Diagnostic performance of greyscale US combined with vascular richness on SMI

Six studies evaluated the diagnostic performance of greyscale US combined with vascular richness on SMI in differentiating between benign and malignant thyroid nodules. Threshold effects analysis showed obvious heterogeneity (Spearman coefficient 0.829; P=0.042). So only the SROC curve and AUC were used to assess the diagnostic tests. A random-effects model was applied and the SROC curve was drawn (Figure 5A). The AUC was 0.92 (95% CI: 0.89–0.94). No significant publication bias existed among these studies (Figure 5B; P=0.35).

Figure 5 Diagnostic performance of greyscale US combined with vascular richness on SMI in differentiating benign and malignant thyroid nodules. (A) SROC curve. The data in square brackets represent 95% CI. (B) Deeks’ funnel plot. SROC, summary receiver operating characteristic; SENS, sensitivity; SPEC, specificity; AUC, area under the curve; ESS, effective sample size; US, ultrasound; SMI, superb microvascular imaging; CI, confidence interval.

The diagnostic standards of vascular richness in each study for malignant thyroid nodules were compared and we found the standard applied in the study of Yoon et al. (27) was different from those used in the other five studies (22,24,26,30,31). Excluding the study of Yoon et al. (27), in the other five studies, the diagnostic performance of greyscale US combined with rich vascularity on SMI in differentiating between benign and malignant thyroid nodules was analyzed. Threshold effects analysis showed no obvious heterogeneity (Spearman coefficient 0.700; P=0.188). The summary sensitivity, summary specificity, LR⁺, LR⁻, DOR, and AUC of greyscale US combined with rich vascularity in diagnosing malignant thyroid nodules were 0.90 (95% CI: 0.77–0.96), 0.74 (95% CI: 0.54–0.88), 3.5 (95% CI: 1.9–6.4), 0.13 (95% CI: 0.06–0.28), 25 (95% CI: 12–53), and 0.90 (95% CI: 0.87–0.93). Forest plots of sensitivity, specificity, and DOR with corresponding 95% CI from each study are shown in Figure 6A,6B. Heterogeneity test showed that for sensitivity, Q=38.88 (P<0.001), I²=89.71%; for specificity, Q=24.98 (P<0.001), I²=83.98%; for DOR I²=17.3% (P=0.305), which reflected a high heterogeneity. A random-effects model was applied and the SROC curve was drawn (Figure 6C). Sensitivity analysis was performed by excluding studies one by one and the combined effect changed little (DOR change range, 21–37). Taking the sample size and study design as covariates into the meta-regression analysis (Table 2), the results showed the sample size affected the study heterogeneity, and the sensitivity of studies with samples larger than 100 was higher (P=0.03).

Figure 6 Diagnostic performance of greyscale US combined with rich vascularity on SMI in differentiating benign and malignant thyroid nodules. (A) Forest plots of sensitivity and specificity. (B) Forest plot of DOR. (C) SROC curve. The data in square brackets represent 95% CI. CI, confidence interval; OR, odds ratio; SROC, summary receiver operating characteristic; SENS, sensitivity; SPEC, specificity; AUC, area under the curve; US, ultrasound; SMI, superb microvascular imaging; DOR, diagnostic odds ratio.

Diagnostic performance of greyscale US combined with vascular distribution on SMI

Eight studies evaluated the diagnostic performance of greyscale US combined with vascular distribution in differentiating between benign and malignant thyroid nodules. Threshold effects analysis showed no obvious heterogeneity (Spearman coefficient 0.619; P=0.102). The summary sensitivity, summary specificity, LR⁺, LR⁻, DOR, and AUC of greyscale US combined with vascular distribution in diagnosing malignant thyroid nodules were 0.86 (95% CI: 0.76–0.92), 0.83 (95% CI: 0.71–0.91), 5.2 (95% CI: 3.0–9.0), 0.17 (95% CI: 0.10–0.28), 29 (95% CI: 14–58), and 0.91 (95% CI: 0.89–0.94). Forest plots of sensitivity, specificity, and DOR with corresponding 95% CI from each study are shown in Figure 7A,7B. Heterogeneity test showed that for sensitivity, Q=41.62 (P<0.001), I²=83.18%; for specificity, Q=55.03 (P<0.001), I²=87.28%; for DOR I²=56.2% (P=0.025), which reflected a high heterogeneity. A random-effects model was applied and the SROC curve was drawn (Figure 7C). Sensitivity analysis was performed by excluding studies one by one and the combined effect changed little (DOR change range, 21–34). Taking the sample size and study design as covariates into the meta-regression analysis (Table 3), the results showed the sample size affected the study heterogeneity, and the sensitivity of studies with samples larger than 100 was higher (P<0.01). No significant publication bias existed among these studies (Figure 7D; P=0.16).

Figure 7 Diagnostic performance of greyscale US combined with vascular distribution in differentiating benign and malignant thyroid nodules. (A) Forest plots of sensitivity and specificity. (B) Forest plot of DOR. (C) SROC curve. The data in square brackets represent 95% CI. (D) Deeks’ funnel plot. CI, confidence interval; OR, odds ratio; SROC, summary receiver operating characteristic; SENS, sensitivity; SPEC, specificity; AUC, area under the curve; ESS, effective sample size; US, ultrasound; DOR, diagnostic odds ratio.

Diagnostic performance of greyscale US combined with the penetrating vessel on SMI

Four studies evaluated the diagnostic performance of greyscale US combined with the penetrating vessel in differentiating between benign and malignant thyroid nodules. Threshold effects analysis showed no obvious heterogeneity (Spearman coefficient −0.200; P=0.800). The summary sensitivity, summary specificity, LR⁺, LR⁻, DOR, and AUC of greyscale US combined with the penetrating vessel in diagnosing malignant thyroid nodules were 0.87 (95% CI: 0.80–0.92), 0.88 (95% CI: 0.81–0.93), 7.6 (95% CI: 4.4–13.2), 0.14 (95% CI: 0.09–0.23), 48 (95% CI: 19–123), and 0.94 (95% CI: 0.92–0.96). Forest plots of sensitivity, specificity, and DOR with corresponding 95% CI from each study are shown in Figure 8A,8B. Heterogeneity test showed that for sensitivity, Q=6.08 (P=0.11), I²=50.69%; for specificity, Q=8.17 (P=0.04), I²=63.26%; for DOR I²=58.0% (P=0.068), which reflected a high heterogeneity. A random-effects model was applied and the SROC curve was drawn (Figure 8C). Sensitivity analysis was performed by excluding studies one by one and the combined effect changed little (DOR change range, 30–64). Taking the sample size and study design as covariates into the meta-regression analysis and results showed no significant effect factors. No significant publication bias existed among these studies (Figure 8D; P=0.17).

Figure 8 Diagnostic performance of greyscale US combined with the penetrating vessel in differentiating benign and malignant thyroid nodules. (A) Forest plots of sensitivity and specificity. (B) Forest plot of DOR. (C) SROC curve. The data in square brackets represent 95% CI. (D) Deeks’ funnel plot. CI, confidence interval; OR, odds ratio; SROC, summary receiver operating characteristic; SENS, sensitivity; SPEC, specificity; AUC, area under the curve; ESS, effective sample size; US, ultrasound; DOR, diagnostic odds ratio.

Discussion

In this meta-analysis, we covered 10 original researches to evaluate the diagnostic performance of greyscale US combined with SMI features (including vascular richness, vascular distribution, and the penetrating vessel) in differentiation between benign and malignant thyroid nodules. Results showed that the AUC of greyscale US combined with SMI in diagnosing malignant thyroid nodules was 0.92 (95% CI: 0.89–0.94). The summary sensitivity, summary specificity, and AUC of greyscale US combined with rich vascularity in diagnosing malignant thyroid nodules were 0.90 (95% CI: 0.77–0.96), 0.74 (95% CI: 0.54–0.88), and 0.90 (95% CI: 0.87–0.93). The summary sensitivity, summary specificity, and AUC of greyscale US combined with vascular distribution in diagnosing malignant thyroid nodules were 0.86 (95% CI: 0.76–0.92), 0.83 (95% CI: 0.71–0.91), and 0.91 (95% CI: 0.89–0.94). The summary sensitivity, summary specificity, and AUC of greyscale US combined with the penetrating vessel in diagnosing malignant thyroid nodules were 0.87 (95% CI: 0.80–0.92), 0.88 (95% CI: 0.81–0.93), and 0.94 (95% CI: 0.92–0.96). Sensitivity analysis showed robust results, demonstrating greyscale US combined SMI had good diagnostic performance for differentiation between benign and malignant thyroid nodules.

Compared with that of greyscale US and greyscale US combined with CDFI, greyscale combined with SMI yielded better diagnostic performance for malignant thyroid nodules. At present, the most commonly used TI-RADSs and risk stratification approaches include only grayscale features (1,3-5,32-34). Gao et al. (35) found that the sensitivity, specificity, and AUC of multiple greyscale-based US guidelines for thyroid nodules were 81.6–95.5%, 73.0–79.7%, and 0.81–0.86. Ha et al. (36) reported that the specificity of these guidelines was low (28.2–68.9%) although the sensitivity was pleasing. Chen et al. (23) found that for the ACR TI-RADS category 4 nodules, the AUC of TI-RADS combined with SMI was significantly greater than that of TI-RADS alone (P<0.05), which was consistent with the results of Zhao et al. (29). Zhang et al. (28) also found that the AUC of the Kwak-TI-RADS combined with SMI was higher than that of the Kwak-TI-RADS combined with CDFI (P<0.001). These findings support the superiority of SMI for microvessels and low-speed flow. Cappelli et al. (37) found that SMI could depict the detail of nodular vascularity better than CDFI and power Doppler flow imaging (PDFI). Machado et al. (38) reported that SMI could show microflows of lower speed than CDFI and PDFI of thyroid nodules. In our meta-analysis, greyscale US combined with SMI also showed great performance in diagnosing malignant thyroid nodules.

Our results showed obvious heterogeneity induced by the threshold effect. Although there are no unified diagnostic criteria for SMI in differentiating between benign and malignant thyroid nodules, the richness, distribution, and penetrating vessels are the three main aspects used to evaluate the vascularity. Therefore, we performed a subgroup analysis according to different SMI diagnostic criteria for malignant thyroid nodules. Despite the different standards among the studies, we were able to summarize the malignant features of thyroid nodules. In terms of vascular richness, malignant thyroid nodules present more microflow than benign nodules do (22,24,26,30,31). The genesis and development of tumors are closely related to neovascularization (8). The microflow signals on SMI are associated with the pathological index Microvessel Density (39), thus can reflect the vascular richness. In terms of vascular distribution, mainly central vascularity is more common in malignant thyroid nodules (22,24-26,28,30). Moreover, malignant thyroid nodules exhibit a higher propensity for the presence of penetrating vessels (23,25,29,30). When the tumor volume is larger than 10 mm³ or the number of tumor cells is more than 10⁷, the tumor will recruit the surrounding vessels, leading to neovessels forming through the epithelial cells of the original vessels and growing into the tumor (8,40), which present as penetrating vessels on SMI. Compared with those of the three subgroups, the AUC of the studies based on the penetrating vessel was higher than those of the studies based on the rich vascularity and the vascular distribution, which may suggest a greater clinical value of penetrating vessels in diagnosing malignant thyroid nodules.

Apart from the threshold effect, the meta-regression analysis was also performed for potential sources of heterogeneity in each subgroup. The results suggested that heterogeneity was associated with the sample size. The sensitivity was greater for studies with sample sizes larger than 100 (P<0.01). However, only a few factors were included in the meta-regression analysis, which was restricted by the number of studies. Factors such as the quality of the study and the size of the lesion could not be evaluated.

Compared with previous meta-analyses on similar topics, the strengths of our study are as follows. (I) Subgroup analysis was carried out for the first time according to different diagnostic criteria of SMI for malignant thyroid nodules, which refined the results and increased the practicability. (II) We assessed the diagnostic performance of greyscale US combined with SMI for differentiating between benign and malignant thyroid nodules to obtain more practical results. (III) Detailed inclusion and exclusion criteria and strict screening were applied. However, there are several limitations in this study. (I) The number of studies included was small. We ultimately included 10 studies, and the number of studies in each subgroup was relatively small. The reliability of some results needs to be further evaluated. (II) The heterogeneity was significant. Despite the threshold effect, sample size might be a source of heterogeneity. Owing to the limitations of the included studies, other sources of heterogeneity may have not been assessed. (III) The diagnostic criteria of some studies involved two or more SMI parameters, which could affect the diagnostic performance evaluation for a single subgroup.

Conclusions

Greyscale US combined with SMI demonstrates strong diagnostic performance in distinguishing between benign and malignant thyroid nodules. However, more studies are needed to standardize the SMI diagnostic criteria for thyroid nodules, which will promote the clinical application of SMI for thyroid nodules.

Acknowledgments

The authors would like to thank AJE (https://www.aje.cn/) for English-language editing.

Funding: This work was supported by the National High-Level Hospital Clinical Research Funding (No. 2022-PUMCH-B-064).

Footnote

Reporting Checklist: The authors have completed the PRISMA-DTA reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-1195/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1195/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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