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SYSTEMATIC REVIEW article

Front. Oncol., 05 March 2019
Sec. Cancer Imaging and Image-directed Interventions

Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis

\r\nRongzhong HuangRongzhong Huang1Lihong JiangLihong Jiang1Yu XuYu Xu2Yuping GongYuping Gong3Haitao RanHaitao Ran3Zhigang WangZhigang Wang3Yang Sun*Yang Sun3*
  • 1The First People's Hospital of Yunnan Province, Kunming, China
  • 2Chuangxu Institute of Life Science, Chongqing, China
  • 3Second Affiliated Hospital of Chongqing Medical University, Chongqing, China

Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites.

Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3).

Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = −6.67, 95%CI [−15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [−3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89.

Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.

Introduction

Ultrasound (US) has been used for decades in differentiating benign and malignant lesions because of its low cost, ease of access, and non-invasiveness. For example, it belongs to the triad (physical examination, mammography and US), commonly used to assess the risk of breast cancer (1). Moreover, it can detect thyroid nodules as small as 2 mm in size and predicts malignancy based on features like irregular border, hypo-echogenicity, and calcification (2, 3). However, none of these features can individually predict malignancy and conventional US alone has shown moderate accuracy in detecting malignant lesions (4). Therefore, improvements to US technique have been sought.

The introduction of contrast agents (contrast-enhanced US/CEUS) allows for visibility of blood flow within the lesion, which improves its characterization (5). The current in-use contrast media are second-generation agents as SonoVue. These agents remain within the intravascular space, which increases their safety and allows for continuous imaging over the enhancement period (6). Several studies have reported high sensitivity and specificity for CEUS in differentiating malignant lesions with the breast, thyroid, liver and kidneys (5, 79). A recent meta-analysis showed no significant difference between CEUS and contrast-enhanced computed tomography (CECT) and magnetic resonance imaging (CEMRI) in terms of the diagnostic accuracy in characterizing focal liver lesions (FLLs) (8).

Shear wave elastography (SWE) relies on the degree of lesion stiffness when subjected to external pressure. Malignant nodules have harder consistency (less elasticity) than benign ones due to the uncontrolled proliferation of cancer cells (10). Therefore, SWE has been investigated for differentiating benign and malignant nodules. Compared to conventional US, SWE is more quantitative and is less operator-dependent, allowing more effective detection of malignant tumors (11). Recent diagnostic test accuracy (DTA) studies and meta-analyses showed high sensitivity and specificity for SWE in detecting malignant lesions within the breast and hepatic tissues (1113).

According to our knowledge, data are lacking on the direct comparison between CEUS and SWE; therefore, we performed a meta-analysis to evaluate the diagnostic accuracy of CEUS and SWE in differentiating malignant tumors in the breast, liver, thyroid, kidneys, and prostate tissues in comparison to histopathology as a reference test. Moreover, we used network meta-analysis (NMA) to compare the diagnostic accuracy of both tests in malignant tumor differentiation.

Materials and Methods

This meta-analysis has been conducted and reported in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (The PRISMA-DTA Statement) (14); Supplementary File I.

Literature Search

We searched Medline (via PubMed), Embase, SCOPUS and Web of Science for diagnostic accuracy studies that evaluated the use of CEUS and SWE in the differentiation of malignant tumors in different body organs. The following search terms were used with different combinations in different databases: Contrast-enhanced Ultrasound OR CEUS OR Ultrasound OR SonoVue OR Shear Wave Elastography OR SWE OR Sonoelastography OR Elastosonography AND Malignant OR Cancer OR Tumor OR Benign OR Adenoma OR Adenocarcinoma OR Carcinoma OR Nodule. No search filters of any sort were used during the search. All retrieved search results from database search (including bibliographic data and abstracts) were imported into EndNote (X7) for duplicate removal and then were transferred to a Microsoft Excel Sheet for screening.

Study Screening

For a study to be eligible for inclusion, it must have matched all the following criteria: (1) Population: Patients, suspected or diagnosed with malignancy in any body organ, (2) Intervention: CEUS or SWE [no specifications by US system or probe type], (3) Comparator: Histopathology, (4) Outcomes: Sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV], and (5) Study type: Diagnostic accuracy study. Two independent authors reviewed the title and abstract of retrieved records against our eligibility criteria and classified them into: eligible, non-eligible, or requires further screening (seems to fit the inclusion criteria, but further confirmation is required). The full-text articles of the latter type were retrieved and underwent a second wave of screening. Any discrepancy between the two reviewers' decisions was solved by a senior reviewer (with a 15-year experience in secondary analysis and evidence synthesis methods) after reviewing the debated studies in reference to the pre-specified PICO criteria.

Data Extraction and Quality Assessment

An extraction sheet (in Microsoft Excel) was formatted and pilot-tested before final extraction. The sheet was customized to extract the baseline data of the imaging device, enrolled patients, as well as the raw diagnostic data of each included study. For pilot testing, two reviewers extracted these data from 5 included studies and the datasets were matched and compared with the original studies by a third reviewer. Each set of data was extracted by two reviewers and discordant decisions were resolved by discussion. These discussions included re-examining the studies, inspecting their available additional data sources and re-evaluating the former decisions. When the discrepancies remained, a senior reviewer examined the studies and settled the differences. The extracted data included (I) baseline characteristics of enrolled participants, (II) study design, (III) diagnostic test parameters: Parameters, cutoff value and US system for SWE and contrast agent, US technique, probe and mechanical index for CEUS, and (IV) Outcome data: true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values. When these values were not directly given, they were calculated from the processed data as sensitivity, specificity, PPV, and NPV, using the statistical calculator on RevMan software (Version 5.3 for Windows). We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) score to assess the quality of included studies. It consists of 14 (yes/no/unclear) questions to assess different forms of bias within DTA studies (15).

Data Analysis

Pairwise meta-analyses were done under the random-effects model when two or more studies investigated the same predefined research question with the same laboratory test. We extracted the sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) values for CEUS and SWE compared to histopathology as a reference standard. The DOR is calculated as (TP X TN)/ (FP X FN) and defined as the odds of having a positive test result in a patient with disease compared with the odds of a positive test result in a patient without disease. Moreover, summary receiver operating characteristic (SROC) curves were constructed to examine diagnostic accuracy. All statistics were reported as absolute values with their 95% confidence interval (95% CI). A p-value < 0.05 was considered statistically significant. The Chi-square and I-square statistics were calculated in order to assess heterogeneity. Significant heterogeneity was considered to be present if the chi-square p-value was < 0.1 (as per the Cochrane Handbook for Systematic Reviews of Intervention). Data were presented into five subgroups according to cancer site: breast, liver, thyroid, kidneys, and prostate. Network meta-analyses were conducted to compare the diagnostic accuracy of CEUS vs. SWE in malignancy detection. Heterogeneity and inconsistency were checked by the I2 and the corresponding p-value. All statistical analyses were conducted on MetaDiSc (version 1.4) and R software (version 3.4.3).

Results

Literature Search and Study Characteristics

Database search retrieved 5896 unique citations. Following title and abstract screening, 422 full-text articles were retrieved for further scrutiny. Finally, 114 diagnostic accuracy studies (65 on SWE and 50 on CEUS; one study by 4 assessed both modalities), reporting data from 15926 patients (5680 for CEUS and 10392 for SWE) were included in our network meta-analysis (Figure 1, Bibliographic details in Supplementary File II). According to the QUADAS score, 25 (21.5%), 30 (25.8%), 22 (18.9%), 23 (19.8%), and 16 (13.8%) studies scored 10, 11, 12, 13, and 14, respectively. The baseline data of enrolled participants, as well as the characteristics of the used US systems for SWE and CEUS tests are illustrated in Tables 1, 2, respectively.

FIGURE 1
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Figure 1. PRISMA flow diagram of literature search and study selection.

TABLE 1
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Table 1. Baseline characteristics of enrolled patients and criteria of the used SWE system.

TABLE 2
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Table 2. Baseline characteristics of enrolled patients and criteria of the used CEUS system.

Outcomes of Pair-Wise Meta-Analysis

Breast Cancer

Detailed figures for pairwise meta-analysis in all five organs are illustrated in Supplementary File III. The pooled sensitivity, specificity, positive LR, and negative LR for CEUS in detection of breast malignant lesions were 0.89 (95% CI, 0.85, 0.92), 0.85 (95% CI, 0.81, 0.89), 6.13 (95% CI, 4.70, 8.01), and 0.12 (95% CI, 0.07, 0.21), respectively. The pooled DOR was 49.66 (95% CI, 29.42, 83.82) and the area under the receiving-operating characteristic (AUROC) curve was 0.92, Figure 2A. No heterogeneity was observed for sensitivity (p = 0.15) or specificity (p = 0.95).

FIGURE 2
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Figure 2. Summary receiver operating characteristic curve of (A) Contrast Enhanced Ultrasound, and (B) Shear Weight Elastography in breast cancer diagnosis.

For SWE, the pooled sensitivity, specificity, positive LR, and negative LR were 0.84 (95% CI, 0.83, 0.86), 0.86 (95% CI, 0.85, 0.87), 7.12 (95% CI, 5.54, 9.15), and 0.18 (95% CI, 0.15, 0.22), respectively. The pooled DOR was 46.22 (95% CI, 31.33, 68.18) with an AUROC of 0.93, Figure 2B. Significant heterogeneity was observed for sensitivity (p < 0.0001) and specificity (p < 0.0001).

Hepatic Cancer

The pooled sensitivity, specificity, positive LR, and negative LR for CEUS in differentiating malignant hepatic lesions were 0.78 (95% CI, 0.76, 0.81), 0.89 (95% CI, 0.87, 0.91), 6.51 (95% CI, 3.90, 10.85), and 0.13 (95% CI, 0.06, 0.25), respectively. The overall DOR was 57.94 (95% CI, 24.78, 135.45) with an AUROC of 0.95, Figure 3A. The included studies were heterogeneous in the estimates of sensitivity (p < 0.0001) and specificity (p < 0.0001).

FIGURE 3
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Figure 3. receiver operating characteristic curve of (A) Contrast Enhanced Ultrasound, and (B) Shear Weight Elastography in hepatic cancer diagnosis.

For SWE, the pooled sensitivity, specificity, positive LR, and negative LR were 0.82 (95% CI, 0.77, 0.87), 0.83 (95% CI, 0.76, 0.89), 4.30 (95% CI, 2.85, 6.48), and 0.29 (95% CI, 0.12, 0.71), respectively. The overall DOR was 14.46 (95% CI, 4.09, 51.04) with an AUROC of 0.90, Figure 3B. The included studies were heterogeneous in the estimates of sensitivity (p < 0.0009) and specificity (p < 0.0001).

Thyroid Cancer

The pooled sensitivity, specificity, positive LR, and negative LR for CEUS in detecting malignant thyroid nodules were 0.81 (95% CI, 0.78, 0.84), 0.88 (95% CI, 0.86, 0.90), 6.01 (95% CI, 3.88, 9.31), and 0.23 (95% CI, 0.17, 0.31), respectively. The overall DOR was 28.54 (95% CI, 16.79, 48.51) with an AUROC of 0.91, Figure 4A. Significant heterogeneity was observed for sensitivity (p = 0.001) and for specificity (p < 0.0001).

FIGURE 4
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Figure 4. Summary receiver operating characteristic curve of (A) Contrast Enhanced Ultrasound, and (B) Shear Weight Elastography in thyroid cancer diagnosis.

For SWE, the pooled sensitivity, specificity, positive LR, and negative LR were 0.67 (95% CI, 0.64, 0.69), 0.77 (95% CI, 0.76, 0.79), 3.50 (95% CI, 2.93, 4.18), and 0.33 (95% CI, 0.25, 0.45), respectively. The overall DOR was 11.17 (95% CI, 8.04, 15.51) with an AUROC of 0.84, Figure 4B. Significant heterogeneity was observed for sensitivity (p < 0.0001) and specificity (p < 0.0001).

Renal Cancer

The sensitivity of CEUS ranged from 0.71 to 0.98 with a pooled sensitivity of 0.87 (95% CI, 0.85, 0.88). Specificity ranged from 0.50 to 0.97 with a pooled specificity of 0.84 (95% CI, 0.82, 0.87). The pooled positive and negative LRs were 5.55 (95% CI, 3.74, 8.22) and 0.12 (95% CI, 0.07, 0.19), respectively. The overall DOR was 53.44 (95% CI, 29.89, 95.56) with an AUROC of 0.95, Figure 5A. Significant heterogeneity was observed for sensitivity (p < 0.0001) and specificity (p < 0.0001).

FIGURE 5
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Figure 5. Summary receiver operating characteristic curve of (A) Contrast Enhanced Ultrasound in renal cancer diagnosis, and (B) Shear Weight Elastography in prostate cancer diagnosis.

Prostate Cancer

The sensitivity of SWE ranged from 0.42 to 0.96 with a pooled sensitivity of 84% (95% CI, 0.80, 0.87). Specificity ranged from 0.70 to 0.95 with a pooled specificity of 0.84 (95% CI, 0.82, 0.86). The pooled positive and negative LRs were 4.59 (95% CI, 2.68, 7.87) and 0.18 (95% CI, 0.07, 0.44), respectively. The overall DOR was 25.35 (95% CI, 7.15, 89.89) with an AUROC of 0.89 (Figure 5A). Significant heterogeneity was observed for sensitivity (p < 0.0001) and specificity (p < 0.0001) (Figure 5B). Table 3 summarizes the diagnostic results for both tests in different cancer sites.

TABLE 3
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Table 3. Summary of the results of pooled sensitivity, specificity, positive, and negative likelihood ratios for SWE and CEUS in different cancers.

Outcomes of Network Meta-Analysis

Corresponding network plots and forest plots of network meta-analysis between CEUS and SWE are shown in Figure 6. In breast cancer, NMA showed that CEUS was associated with significantly higher DOR than SWE (DOR = 27.14, 95% CI [2.30, 51.97], p = 0.011). While NMA showed no significant difference between CEUS and SWE in detecting hepatic (DOR = −6.67, 95% CI [-15.08, 1.74, p = 0.61]) and thyroid malignant lesions (DOR = 3.79, 95% CI [−3.10, 10.68], p = 0.58). No significant heterogeneity or inconsistency were observed between the pooled studies for breast (I2 = 10%, p = 0.30) and hepatic cancer (I2 = 20%, p = 0.21). While a p-value of 0.05 indicated significant heterogeneity among the studies of thyroid cancer; therefore, the random-effects model was employed.

FIGURE 6
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Figure 6. Network plots showing direct evidence between Contrast Enhanced Ultrasound and Shear Weight Elastography in (A) breast cancer, (B) hepatic caner, and (C) thyroid cancer. Also, forest plots of network meta-analysis between Contrast Enhanced Ultrasound and Shear Weight Elastography vs. histopathology in (A) breast cancer, (B) hepatic caner, and (C) thyroid cancer. (D) Forest plot CEUS vs. SWE of breast cancer. (E) Forest plot CEUS vs. SWE of hepatic cancer. (F) Forest plot CEUS vs. SWE of thyroid cancer.

Ranking Diagnostic Tests

According to Glas et al. (116), the DOR is considered as an indicator of ranking of competing diagnostic tests. According to our results, CEUS achieved the highest DOR in detecting breast and thyroid malignant lesions, while SWE achieved the highest DOR in detecting hepatic malignant lesions.

Discussion

This meta-analysis of DTA studies provides a comprehensive assessment and comparison of the diagnostic accuracy of two US modalities in differentiating malignant tumors in different body organs. It showed relatively high sensitivity (between 78 and 89%) and specificity (between 84 and 89%) for CEUS in identifying malignant lesions in the breast, liver, thyroid and kidneys. Moreover, it demonstrated relatively high sensitivity (between 82 and 84%) and specificity (between 83 and 86%) for SWE in differentiating malignant tumors within the breast, liver and prostate. However, it had relatively lower sensitivity (67%) and specificity (77%) in identifying malignant nodules within the thyroid gland.

Our results support some recent practice guidelines that endorse the use of CEUS and SWE in differentiating malignant lesions within the liver and the breast (117, 118). Moreover, it provides new data on a comparison that can impact the clinical practice. Through NMA, we compared the diagnostic accuracy of CEUS and SWE in three organs (where data on both tests were available in the literature). Our network and ranking analysis showed that CEUS was more accurate than SWE in differentiating breast and thyroid lesions (although the difference was not significant in thyroid malignancy according to NMA). On the other hand, SWE ranked higher in terms of diagnostic accuracy in differentiating hepatic malignant lesions (although the difference was not significant according to NMA).

Our results are in agreement with a former meta-analysis by Sadigh et al. that showed high sensitivity and specificity for SWE in differentiating breast malignant lesions [88 and 83% in comparison to 84 and 86% in our analysis; (11)]. However, our sensitivity and specificity results are quite lower than those obtained by Liu et al. in a meta-analysis on SWE accuracy in differentiating thyroid malignancy [sensitivity 81% and specificity 84%; (12)]. Likewise, another meta-analysis reported high sensitivity and specificity (93 and 90%, respectively) for CEUS in identifying hepatic malignant lesions (119). The observed discrepancy between our findings and those of the aforementioned meta-analyses may be attributed to the different sample size (being larger in our analysis) or the lesional characteristics of enrolled patients (being easier to identify in the studies included in the other meta-analysis i.e., less depth and clear contrast from the surrounding tissue).

Interestingly, a meta-analysis by Guang et al. showed comparable diagnostic accuracy for SonoVue-enhanced US with contrast-enhanced computed tomography and magnetic resonance imaging (8). Moreover, CEUS has other advantages over these modalities as ease of access, lack of radiation exposure or nephrotoxic materials; limitations that affect the use of CT and MRI in several diagnostic applications (120, 121). It is also fair to recognize that both tests have limitations as well. For example, SWE suffers from operator-dependency and manual compression, while the adverse effects of the contrast agent is a concern with CEUS use. Further technical improvements with both modalities would further enhance their clinical potential.

Strength Points

This NMA directly compares the diagnostic accuracies of CEUS and SWE in different cancer sites and using different analytic approaches as pairwise, network and ranking pooled analyses. Therefore, it provides a holistic evaluation of the comparison of both techniques in different body organs. We performed a thorough literature search and retrieved a large number of studies (relatively large sample size), which adds to the validity and generalizability of our findings. Unlike former reviews that retrieved a small number of studies and focused on one test in one organ, we aimed to provide a comprehensive assessment of both tests in different organs and a high quality comparison whenever suitable data were provided.

Limitations and Future Research Implications

Our meta-analysis has some limitations. First, the observed heterogeneity in the majority of our outcomes may be due to differences in study design and patient characteristics. Second, we could not examine the effects of lesion characteristics, such as size and depth on the diagnostic accuracy of both tests due to lack of data. Third, many of the included studies did not mention whether the results of CEUS or SWE were interpreted with blinding to the findings of histopathology or not. Future studies should report diagnostic accuracy data based on the size and depth of the lesions to allow more detailed analysis. Moreover, they should adhere to the Standards for Reporting of Diagnostic Accuracy “STRAD” checklist in reporting their methods and findings to allow a more thorough critical appraisal.

Conclusion

Both diagnostic tests (CEUS and SWE) showed relatively high sensitivity and specificity in detecting malignant tumors in different organs; CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer (the differences in the latter two cancer types were not statistically significant). These results endorse the use of both tests for malignancy detection and rank their accuracy in different organs. Future studies should provide more data to allow characterization of both tests in lesions of different size or depth.

Author Contributions

YS developed the concept, designed the study, and prepared the manuscript. RH acquired the data, controlled quality of the work, analyzed the data, and prepared the manuscript. LJ acquired the data. YX analyzed the data. YG acquired the data. HR acquired the data and conducted the analysis. ZW analyzed the data and prepared the manuscript.

Funding

This work was supported by funding from National Natural Science Foundation of China. Award Number 31300137 received by RH.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are extremely thankful to authors of all the included papers for proving suitable data for analysis.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2019.00102/full#supplementary-material

Supplementary File I PRISMA checklist for systematic reviews/meta-analysis.

Supplementary File II Bibliographic Information of Included Studies.

Supplementary File III Additional Pairwise Meta-analysis Figures.

References

1. Hatzung G, Grunwald S, Zygmunt M, Geaid AA, Behrndt PO, Isermann R, et al. Sonoelastography in the diagnosis of malignant and benign breast lesions: initial clinical experiences. Ultraschall Med. (2010) 31:596–603. doi: 10.1055/s-0029-1245526

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, et al. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology. (2011) 260:892–9. doi: 10.1148/radiol.11110206

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Zhan J, Diao X-H, Chen L, Jin J-M, Chen Y. Role of contrast-enhanced ultrasound in diagnosis of thyroid nodules in acoustic radiation force impulse “Gray Zone.” Ultras Med Biol. (2017) 43:1179–86. doi: 10.1016/j.ultrasmedbio.2017.02.006

CrossRef Full Text | Google Scholar

4. Cooper P. What can we learn from old wetlands? Lessons that have been learned and some that may have been forgotten over the past 20 years. Desalination. (2009) 246:11–26. doi: 10.1016/j.desal.2008.03.040

CrossRef Full Text | Google Scholar

5. Bertolotto M, Cicero C, Perrone R, Degrassi F, Cacciato F, Cova MA. Renal masses with equivocal enhancement at CT: characterization with contrast-enhanced ultrasound. Am J Roentgenol. (2015) 204:W557–65. doi: 10.2214/AJR.14.13375

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Lencioni R, Crocetti L. Radiofrequency ablation of liver cancer. Tech Vasc Interv Radiol. (2007) 10:38–46. doi: 10.1053/j.tvir.2007.08.006

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Zhao H, Xu R, Ouyang Q, Chen L, Dong B, Huihua Y. Contrast-enhanced ultrasound is helpful in the differentiation of malignant and benign breast lesions. Eur J Radiol. (2010) 73:288–93. doi: 10.1016/j.ejrad.2009.05.043

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Guang Y, Xie L, Ding H, Cai A, Huang Y. Diagnosis value of focal liver lesions with SonoVue®-enhanced ultrasound compared with contrast-enhanced computed tomography and contrast-enhanced MRI: a meta-analysis. J Cancer Res Clin Oncol. (2011) 137:1595. doi: 10.1007/s00432-011-1035-8

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Cantisani V, Consorti F, Guerrisi A, Guerrisi I, Ricci P, Di Segni M, et al. Prospective comparative evaluation of quantitative-elastosonography (Q-elastography) and contrast-enhanced ultrasound for the evaluation of thyroid nodules: preliminary experience. Eur J Radiol. (2013) 82:1892–8. doi: 10.1016/j.ejrad.2013.07.005

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Tranquart F, Bleuzen A, Pierre-Renoult P, Chabrolle C, Sam Giao M, Lecomte P. Elastosonography of thyroid lesions. J Radiol. (2008) 89:35–9. doi: 10.1016/S0221-0363(08)70367-6

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Sadigh G, Carlos RC, Neal CH, Dwamena BA. Accuracy of quantitative ultrasound elastography for differentiation of malignant and benign breast abnormalities: a meta-analysis. Breast Cancer Res Treat. (2012) 134:923–31. doi: 10.1007/s10549-012-2020-x

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Liu H, Zhao L-X, Xu G, Yao M-H, Zhang A-H, Xu HX, et al. Diagnostic value of virtual touch tissue imaging quantification for benign and malignant breast lesions with different sizes. Int J Clin Exp Med. (2015) 8:13118–26.

PubMed Abstract | Google Scholar

13. Elmoneam GA, Almolla RM, Ahmed AF, Al Ekrashy MA. Supersonic shear waves quantitative elastography and kinetic magnetic resonance dynamic curve in discriminating BI-RADS 4 breast masses: a comparative study. Egypt J Radiol Nuclear Med. (2016) 47:1773–82. doi: 10.1016/j.ejrnm.2016.08.004

CrossRef Full Text | Google Scholar

14. McInnes MD, Moher D, Thombs BD, McGrath TA, Bossuyt PM, Clifford T, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA. (2018) 319:388–96. doi: 10.1001/jama.2017.19163

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol. (2003) 3:25. doi: 10.1186/1471-2288-3-25

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Li D-D, Xu H-X, Guo L-H, Bo X-W, Li X-L, Wu R, et al. Combination of two-dimensional shear wave elastography with ultrasound breast imaging reporting and data system in the diagnosis of breast lesions: a new method to increase the diagnostic performance. Eur Radiol. (2016) 26:3290–300. doi: 10.1007/s00330-015-4163-8

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Yang Y-P, Xu X-H, Guo L-H, He Y-P, Wang D, Liu B-J, et al. Qualitative and quantitative analysis with a novel shear wave speed imaging for differential diagnosis of breast lesions. Sci Rep. (2017) 7:40964. doi: 10.1038/srep40964

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Yang Y-P, Xu X-H, Bo X-W, Liu B-J, Guo L-H, Xu JM, et al. Comparison of virtual touch tissue imaging & quantification (VTIQ) and Virtual touch tissue quantification (VTQ) for diagnosis of thyroid nodules. Clin Hemorheol Microcirc. (2017) 65:137–49. doi: 10.3233/CH-16142

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Kim SJ, Ko KH, Jung HK, Kim H. Shear wave elastography: is it a valuable additive method to conventional ultrasound for the diagnosis of small (≤ 2 cm) breast cancer? Medicine. (2015) 94:e1540. doi: 10.1097/MD.0000000000001540

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Youk JH, Son EJ, Park AY, Kim JA. Shear-wave elastography for breast masses: local shear wave speed (m/sec) versus Young modulus (kPa). Ultrasonography. (2014) 33:34–9. doi: 10.14366/usg.13005

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Tang L, Xu H-X, Bo X-W, Liu B-J, Li X-L, Wu R, et al. A novel two-dimensional quantitative shear wave elastography for differentiating malignant from benign breast lesions. Int J Clin Exp Med. (2015) 8:10920–8.

PubMed Abstract | Google Scholar

22. Choi H, Sohn Y-M, Seo M. Comparison of 3D and 2D shear-wave elastography for differentiating benign and malignant breast masses: focus on the diagnostic performance. Clin Radiol. (2017) 72:878–86. doi: 10.1016/j.crad.2017.04.009

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Golatta M, Schweitzer-Martin M, Harcos A, Schott S, Gomez C, Stieber A, et al. Evaluation of virtual touch tissue imaging quantification, a new shear wave velocity imaging method, for breast lesion assessment by ultrasound. Biomed Res Int. (2014) 2014:960262. doi: 10.1155/2014/960262

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Youk JH, Gweon HM, Son EJ, Han KH, Kim JA. Diagnostic value of commercially available shear-wave elastography for breast cancers: integration into BI-RADS classification with subcategories of category 4. Eur Radiol. (2013) 23:2695–704. doi: 10.1007/s00330-013-2873-3

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Ko KH, Jung HK, Kim SJ, Kim H, Yoon JH. Potential role of shear-wave ultrasound elastography for the differential diagnosis of breast non-mass lesions: preliminary report. Eur Radiol. (2014) 24:305–11. doi: 10.1007/s00330-013-3034-4

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Lee EJ, Jung HK, Ko KH, Lee JT, Yoon JH. Diagnostic performances of shear wave elastography: which parameter to use in differential diagnosis of solid breast masses? Eur Radiol. (2013) 23:1803–11. doi: 10.1007/s00330-013-2782-5

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Ng WL, Rahmat K, Fadzli F, Rozalli FI, Mohd-Shah MN, Chandran PA, et al. Shearwave elastography increases diagnostic accuracy in characterization of breast lesions. Medicine. (2016) 95:e3146. doi: 10.1097/MD.0000000000003146

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Tian J, Liu Q, Wang X, Xing P, Yang Z, Wu C. Application of 3D and 2D quantitative shear wave elastography (SWE) to differentiate between benign and malignant breast masses. Sci Rep. (2017) 7:41216. doi: 10.1038/srep41216

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Olgun DÇ, Korkmazer B, Kiliç F, Dikici AS, Velidedeoglu M, Aydogan F, et al. Use of shear wave elastography to differentiate benign and malignant breast lesions. Diagn Intervent Radiol. (2014) 20:239. doi: 10.5152/dir.2014.13306

CrossRef Full Text | Google Scholar

30. Chang JY, Moon JH, Koh SH, Park SY, Lee KS. Clinical application of shear wave elastography in breast masses. Iran J Radiol. (2017) 14:e39585. doi: 10.5812/iranjradiol.39585

CrossRef Full Text | Google Scholar

31. Yao M, Wu J, Zou L, Xu G, Xie J, Wu R, et al. Diagnostic value of virtual touch tissue quantification for breast lesions with different size. Biomed Res. Int. (2014) 2014:142504. doi: 10.1155/2014/142504

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Seo M, Ahn HS, Park SH, Lee JB, Choi BI, Sohn YM, et al. Comparison and combination of strain and shear wave elastography of breast masses for differentiation of benign and malignant lesions by quantitative assessment: preliminary study. J Ultras Med. (2018) 37:99–109. doi: 10.1002/jum.14309

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Au FWF, Ghai S, Moshonov H, Kahn H, Brennan C, Dua H, et al. Diagnostic performance of quantitative shear wave elastography in the evaluation of solid breast masses: determination of the most discriminatory parameter. Am J Roentgenol. (2014) 203:W328–36. doi: 10.2214/AJR.13.11693

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Chang JM, Won J-K, Lee K-B, Park IA, Yi A, Moon WK. Comparison of shear-wave and strain ultrasound elastography in the differentiation of benign and malignant breast lesions. Am J Roentgenol. (2013) 201:W347–56. doi: 10.2214/AJR.12.10416

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Choi JS, Han B-K, Ko EY, Ko ES, Shin JH, Kim GR. Additional diagnostic value of shear-wave elastography and color Doppler US for evaluation of breast non-mass lesions detected at B-mode US. Eur Radiol. (2016) 26:3542–9. doi: 10.1007/s00330-015-4201-6

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Chung J, Lee WK, Cha E-S, Lee JE, Kim JH, Ryu YH. Shear-wave elastography for the differential diagnosis of breast papillary lesions. PLoS ONE. (2016) 11:e0167118. doi: 10.1371/journal.pone.0167118

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Dobruch-Sobczak K, Nowicki A. Role of shear wave sonoelastography in differentiation between focal breast lesions. Ultras Med Biol. (2015) 41:366–74. doi: 10.1016/j.ultrasmedbio.2014.08.024

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Guo X, Liu Y, Li W. Diagnostic accuracy of shear wave elastography for prediction of breast malignancy in patients with pathological nipple discharge. BMJ Open. (2016) 6:e008848. doi: 10.1136/bmjopen-2015-008848

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Hong S, Woo OH, Shin HS, Hwang S-Y, Cho KR, Seo BK. Reproducibility and diagnostic performance of shear wave elastography in evaluating breast solid mass. Clin Imaging. (2017) 44:42–5. doi: 10.1016/j.clinimag.2017.03.022

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Kim GR, Choi JS, Han B-K, Ko EY, Ko ES, Hahn SY. Combination of shear-wave elastography and color Doppler: feasible method to avoid unnecessary breast excision of fibroepithelial lesions diagnosed by core needle biopsy. PLoS ONE. (2017) 12:e0175380. doi: 10.1371/journal.pone.0175380

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Klotz T, Boussion V, Kwiatkowski F, Dieu-de Fraissinette V, Bailly-Glatre A, Lemery S, et al. Shear wave elastography contribution in ultrasound diagnosis management of breast lesions. Diagn Intervent Imag. (2014) 95:813–24. doi: 10.1016/j.diii.2014.04.015

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Lee BE, Chung J, Cha ES, Lee JE, Kim JH. Role of shear-wave elastography (SWE) in complex cystic and solid breast lesions in comparison with conventional ultrasound. Eur J Radiol. (2015) 84:1236–41. doi: 10.1016/j.ejrad.2015.04.005

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Shi XQ, Li JL, Wan WB, Huang Y. A set of shear wave elastography quantitative parameters combined with ultrasound BI-RADS to assess benign and malignant breast lesions. Ultras Med Biol. (2015) 41:960–6. doi: 10.1016/j.ultrasmedbio.2014.11.014

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Sim Y, Vinnicombe S, Whelehan P, Thomson K, Evans A. Value of shear-wave elastography in the diagnosis of symptomatic invasive lobular breast cancer. Clin Radiol. (2015) 70:604–9. doi: 10.1016/j.crad.2015.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Wu S, Cui X, Huang L, Bai X. Combining virtual touch tissue imaging and BI-RADS may improve solid breast lesion evaluation. Breast Care. (2017) 12:97–100. doi: 10.1159/000456026

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Zhang S-P, Zeng Z, Liu H, Yao M-H, Xu G, Wu R. Combination of conventional ultrasonography and virtual touch tissue imaging quantification for differential diagnosis of breast lesions smaller than 10 mm. Clin Hemorheol Microcirc. (2017) 67:59–68. doi: 10.3233/CH-170249

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Cong R, Li J, Wang X. Comparing performance of combinations of shear wave elastography and B-mode ultrasound in diagnosing breast masses: is it influenced by mass size? Ultras Med Biol. (2017) 43:2133–43. doi: 10.1016/j.ultrasmedbio.2017.04.015

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Park J, Woo OH, Shin HS, Cho KR, Seo BK, Kang EY. Diagnostic performance and color overlay pattern in shear wave elastography (SWE) for palpable breast mass. Eur J Radiol. (2015) 84:1943–8. doi: 10.1016/j.ejrad.2015.06.020

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Park AY, Son EJ, Han K, Youk JH, Kim J-A, Park CS. Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study. Eur J Radiol. (2015) 84:407–12. doi: 10.1016/j.ejrad.2014.11.019

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Wang S, Zhong Z, Wan J, Tan W, Wu G, Chen M, et al. Oridonin induces apoptosis, inhibits migration and invasion on highly-metastatic human breast cancer cells. Am J Chin Med. (2013) 41:177–96. doi: 10.1142/S0192415X13500134

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Kasai Y, Moriyasu F, Saito K, Hara T, Kobayashi Y, Nakamura I, et al. Value of shear wave elastography for predicting hepatocellular carcinoma and esophagogastric varices in patients with chronic liver disease. J Med Ultras. (2015) 42:349–55. doi: 10.1007/s10396-014-0603-3

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Gerber L, Fitting D, Srikantharajah K, Weiler N, Kyriakidou G, Bojunga J, et al. Evaluation of 2D-shear wave elastography for characterisation of focal liver lesions. J Gastrointest Liver Dis. (2017) 26:283–90. doi: 10.15403/jgld.2014.1121.263.dsh

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Özmen E, Adaletli I, Kayadibi Y, Emre S, Kiliç F, Dervişoglu S, et al. The impact of share wave elastography in differentiation of hepatic hemangioma from malignant liver tumors in pediatric population. Eur J Radiol. (2014) 83:1691–97. doi: 10.1016/j.ejrad.2014.06.002

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Tian W-S, Lin M-X, Zhou L-Y, Pan F-S, Huang G-L, Wang W, et al. Maximum value measured by 2-D shear wave elastography helps in differentiating malignancy from benign focal liver lesions. Ultras Med Biol. (2016) 42:2156–66. doi: 10.1016/j.ultrasmedbio.2016.05.002

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Ahmad S, Cao R, Varghese T, Bidaut L, Nabi G. Transrectal quantitative shear wave elastography in the detection and characterisation of prostate cancer. Surg Endosc. (2013) 27:3280–7. doi: 10.1007/s00464-013-2906-7

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Boehm K, Salomon G, Beyer B, Schiffmann J, Simonis K, Graefen M, et al. Shear wave elastography for localization of prostate cancer lesions and assessment of elasticity thresholds: implications for targeted biopsies and active surveillance protocols. J Urol. (2015) 193:794–800. doi: 10.1016/j.juro.2014.09.100

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Porsch M, Wendler JJ, Liehr U-B, Lux A, Schostak M, Pech M. New aspects in shear-wave elastography of prostate cancer. J Ultrasonogr. (2015) 15:5–14. doi: 10.15557/JoU.2015.0001

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Woo S, Kim SY, Cho JY, Kim SH. Shear wave elastography for detection of prostate cancer: a preliminary study. Korean J Radiol. (2014) 15:346–55. doi: 10.3348/kjr.2014.15.3.346

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Correas JM, Tissier AM, Khairoune A, Vassiliu V, Mejean A, Helenon O, et al. Prostate cancer: diagnostic performance of real-time shear-wave elastography. Radiology. (2015) 275:280–89. doi: 10.1148/radiol.14140567

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Glybochko P, Alyaev Y, Amosov A, Krupinov G, Ganzha T, Vorobev A, et al. Prostate cancer detection by assessing stiffness of different tissues using shear wave ultrasound elastog-raphy. Urologiia. (2016) 2016:56–61.

Google Scholar

61. Zhang H, Shi Q, Gu J, Jiang L, Bai M, Liu L, et al. Combined value of Virtual Touch tissue quantification and conventional sonographic features for differentiating benign and malignant thyroid nodules smaller than 10 mm. J Ultras Med. (2014) 33:257–64. doi: 10.7863/ultra.33.2.257

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Zhang P, Zhou P, Tian S-M, Qian Y, Li J-L, Li RZ. Diagnostic performance of contrast-enhanced sonography and acoustic radiation force impulse imaging in solid liver lesions. J Ultras Med. (2014) 33:205–14. doi: 10.7863/ultra.33.2.205

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Azizi G, Keller JM, Mayo ML, Piper K, Puett D, Earp KM, et al. Thyroid nodules and shear wave elastography: a new tool in thyroid cancer detection. Ultras Med Biol. (2015) 41:2855–65. doi: 10.1016/j.ultrasmedbio.2015.06.021

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Wang W-P, Dong Y, Cao J, Mao F, Xu Y, Si Q, et al. Detection and characterization of small superficially located focal liver lesions by contrast-enhanced ultrasound with high frequency transducers. Med Ultrason. (2017) 19:349–56. doi: 10.11152/mu-1276

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Duan S-B, Yu J, Li X, Han Z-Y, Zhai H-Y, Liang P. Diagnostic value of two-dimensional shear wave elastography in papillary thyroid microcarcinoma. Onco Targets Ther. (2016) 9:1311–7. doi: 10.2147/OTT.S98583

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Liu R, Xing M. TERT promoter mutations in thyroid cancer. Endocr Relat Cancer. (2016) 23:R143–55. doi: 10.1530/ERC-15-0533

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Liu B-J, Zhao C-K, Xu H-X, Zhang Y-F, Xu J-M, Li DD, et al. Quality measurement on shear wave speed imaging: diagnostic value in differentiation of thyroid malignancy and the associated factors. Oncotarget. (2017) 8:4948–59. doi: 10.18632/oncotarget.13996

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Kim H, Kim J-A, Son EJ, Youk JH. Quantitative assessment of shear-wave ultrasound elastography in thyroid nodules: diagnostic performance for predicting malignancy. Eur Radiol. (2013) 23:2532–7. doi: 10.1007/s00330-013-2847-5

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Deng J, Zhou P, Tian SM, Zhang L, Qian Y. Comparison of diagnostic efficacy of contrast-enhanced ultrasound, acoustic radiation force impulse imaging, and their combined use in differentiating focal solid thyroid nodules. PLoS ONE. (2014) 9:e90674. doi: 10.1371/journal.pone.0090674

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Baig FN, Liu SY, Lam H-C, Yip S-P, Law HK, Ying M. Shear wave elastography combining with conventional grey scale ultrasound improves the diagnostic accuracy in differentiating benign and malignant thyroid nodules. Applied Sciences. 7:1103. doi: 10.3390/app7111103

CrossRef Full Text

71. Dobruch-Sobczak K, Zalewska EB, Guminska A, Słapa RZ, Mlosek K, Wareluk P, et al. Diagnostic performance of shear wave elastography parameters alone and in combination with conventional B-mode ultrasound parameters for the characterization of thyroid nodules: a prospective, dual-center study. Ultras Med Biol. (2016) 42:2803–11. doi: 10.1016/j.ultrasmedbio.2016.07.010

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Liu B-X, Xie X-Y, Liang J-Y, Zheng Y-L, Huang G-L, Zhou LY, et al. Shear wave elastography versus real-time elastography on evaluation thyroid nodules: a preliminary study. Eur J Radiol. (2014) 83:1135–43. doi: 10.1016/j.ejrad.2014.02.024

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Park SY, Seo AN, Jung HY, Gwak JM, Jung N, Cho NY, et al. Alu and LINE-1 hypomethylation is associated with HER2 enriched subtype of breast cancer. PLoS ONE. (2014) 9:e100429. doi: 10.1371/journal.pone.0100429

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Samir AE, Dhyani M, Anvari A, Prescott J, Halpern EF, Faquin WC, et al. Shear-wave elastography for the preoperative risk stratification of follicular-patterned lesions of the thyroid: diagnostic accuracy and optimal measurement plane. Radiology. (2015) 277:565–73. doi: 10.1148/radiol.2015141627

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Yang GC, Fried KO, Scognamiglio T. Sonographic and cytologic differences of NIFTP from infiltrative or invasive encapsulated follicular variant of papillary thyroid carcinoma: a review of 179 cases. Diagn Cytopathol. (2017) 45:533–41. doi: 10.1002/dc.23709

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Zhou H, Zhou XL, Xu HX, Li DD, Liu BJ, Zhang YF, et al. Virtual Touch tissue imaging and quantification in the evaluation of thyroid nodules. J Ultras Med. (2017) 36:251–60. doi: 10.7863/ultra.15.12070

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Cai Y, Du L, Li F, Gu J, Bai M. Quantification of enhancement of renal parenchymal masses with contrast-enhanced ultrasound. Ultras Med Biol. (2014) 40:1387–93. doi: 10.1016/j.ultrasmedbio.2014.02.003

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Chen L, Wang L, Diao X, Qian W, Fang L, Pang Y, et al. The diagnostic value of contrast-enhanced ultrasound in differentiating small renal carcinoma and angiomyolipoma. Biosci Trends. (2015) 9:252–8. doi: 10.5582/bst.2015.01080

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Chen Y, Wu N, Xue T, Hao Y, Dai J. Comparison of contrast-enhanced sonography with MRI in the diagnosis of complex cystic renal masses. J Clin Ultras. (2015) 43:203–9. doi: 10.1002/jcu.22232

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Chen D, Ma Y, Wang X, Yu S, Li L, Dai B, et al. Clinical characteristics and disease predictors of a large Chinese cohort of patients with autosomal dominant polycystic kidney disease. PLoS ONE. (2014) 9:e92232. doi: 10.1371/journal.pone.0092232

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Defortescu G, Cornu JN, Béjar S, Giwerc A, Gobet F, Werquin C, et al. Diagnostic performance of contrast-enhanced ultrasonography and magnetic resonance imaging for the assessment of complex renal cysts: a prospective study. Int J Urol. (2017) 24:184–9. doi: 10.1111/iju.13289

PubMed Abstract | CrossRef Full Text | Google Scholar

82. Li X, Liang P, Guo M, Yu J, Yu X, Cheng Z, et al. Real-time contrast-enhanced ultrasound in diagnosis of solid renal lesions. Discov Med. (2013) 16:15–25. Available online at: http://www.discoverymedicine.com/Xin-Li/2013/07/26/real-time-contrast-enhanced-ultrasound-in-diagnosis-of-solid-renal-lesions/

PubMed Abstract | Google Scholar

83. Lu Q, Xue LY, Huang BJ, Wang WP, Li CX. Histotype differentiation of hypo-echoic renal tumors on CEUS: usefulness of enhancement homogeneity and intensity. Abdom Imaging. (2015) 40:1675–83. doi: 10.1007/s00261-014-0340-5

PubMed Abstract | CrossRef Full Text | Google Scholar

84. Nicolau C, Buñesch L, Paño B, Salvador R, Ribal MJ, Mallofré C, et al. Prospective evaluation of CT indeterminate renal masses using US and contrast-enhanced ultrasound. Abdom Imaging. (2015) 40:542–51. doi: 10.1007/s00261-014-0237-3

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Oh TH, Lee YH, Seo IY. Diagnostic efficacy of contrast-enhanced ultrasound for small renal masses. Korean J Urol. (2014) 55:587–92. doi: 10.4111/kju.2014.55.9.587

PubMed Abstract | CrossRef Full Text | Google Scholar

86. Sanz E, Hevia V, Gómez V, Álvarez S, Fabuel JJ, Martínez L, et al. Renal complex cystic masses: usefulness of contrast-enhanced ultrasound (CEUS) in their assessment and its agreement with computed tomography. Curr Urol Rep. (2016) 17:89. doi: 10.1007/s11934-016-0646-7

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Tamas-Szora A, Socaciu M, Crisan N, Dobrota F, Prundus P, Bungardean C, et al. Investigation of renal cell carcinoma by contrast-enhanced ultrasound-predictive value of time intensity curve analysis in establishing local tumor invasion and stage: a pilot study. Urol J. (2015) 12:2173–81. doi: 10.22037/uj.v12i3.2851

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Wei S-P, Xu C-L, Zhang Q, Zhang Q-R, Zhao Y-E, Huang PF, et al. Contrast-enhanced ultrasound for differentiating benign from malignant solid small renal masses: comparison with contrast-enhanced CT. Abdom Radiol. (2017) 42:2135–45. doi: 10.1007/s00261-017-1111-x

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Yong C, Teo Y-M, Kapur J. Diagnostic performance of contrast-enhanced ultrasound in the evaluation of renal masses in patients with renal impairment. Med J Malaysia. (2016) 71:193–8. Available online at: http://www.e-mjm.org/2016/v71n4/contrast-enhanced-ultrasound.pdf

PubMed Abstract | Google Scholar

90. Zhang Y, Luo YK, Zhang MB, Li J, Li J, Tang J. Diagnostic accuracy of contrast-enhanced ultrasound enhancement patterns for thyroid nodules. Med Sci Monit. (2016) 22: 4755–64. doi: 10.12659/MSM.899834

PubMed Abstract | CrossRef Full Text | Google Scholar

91. Miyamoto Y, Ito T, Takada E, Omoto K, Hirai T, Moriyasu F. Efficacy of sonazoid (perflubutane) for contrast-enhanced ultrasound in the differentiation of focal breast lesions: phase 3 multicenter clinical trial. Am J Roentgenol. (2014) 202:W400–7. doi: 10.2214/AJR.12.10518

PubMed Abstract | CrossRef Full Text | Google Scholar

92. Xia H-S, Wang X, Ding H, Wen J-X, Fan P-L, Wang WP. Papillary breast lesions on contrast-enhanced ultrasound: morphological enhancement patterns and diagnostic strategy. Eur Radiol. (2014) 24:3178–90. doi: 10.1007/s00330-014-3375-7

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Xiao X, Jiang Q, Wu H, Guan X, Qin W, Luo B. Diagnosis of sub-centimetre breast lesions: combining BI-RADS-US with strain elastography and contrast-enhanced ultrasound—a preliminary study in China. Eur Radiol. (2017) 27:2443–50. doi: 10.1007/s00330-016-4628-4

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Yuan Z, Quan J, Yunxiao Z, Jian C, Zhu H, Liping G. Diagnostic value of contrast-enhanced ultrasound parametric imaging in breast tumors. J Breast Cancer. (2013) 16:208–13. doi: 10.4048/jbc.2013.16.2.208

PubMed Abstract | CrossRef Full Text | Google Scholar

95. Aubé C, Oberti F, Lonjon J, Pageaux G, Seror O, N'kontchou G, et al. EASL and AASLD recommendations for the diagnosis of HCC to the test of daily practice. Liver Int. (2017) 37:1515–25. doi: 10.1111/liv.13429

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Beyer LP, Wassermann F, Pregler B, Michalik K, Rennert J, Wiesinger I, et al. Characterization of focal liver lesions using CEUS and MRI with liver-specific contrast media: experience of a single radiologic center. Ultr Med Eur J Ultras. (2017) 38:619–25. doi: 10.1055/s-0043-105264

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Corvino A, Catalano O, Corvino F, Sandomenico F, Petrillo A. Diagnostic performance and confidence of contrast-enhanced ultrasound in the differential diagnosis of cystic and cysticlike liver lesions. Am J Roentgenol. (2017) 209:W119–27. doi: 10.2214/AJR.16.17062

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Feng Y, Qin X-C, Luo Y, Li Y-Z, Zhou X. Efficacy of contrast-enhanced ultrasound washout rate in predicting hepatocellular carcinoma differentiation. Ultras Med Biol. (2015) 41:1553–60. doi: 10.1016/j.ultrasmedbio.2015.01.026

PubMed Abstract | CrossRef Full Text | Google Scholar

99. Iwamoto T, Imai Y, Kogita S, Igura T, Sawai Y, Fukuda K, et al. Comparison of contrast-enhanced ultrasound and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced MRI for the diagnosis of macroscopic type of hepatocellular carcinoma. Dig Dis. (2016) 34:679–86. doi: 10.1159/000448855

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Kobayashi T, Aikata H, Hatooka M, Morio K, Morio R, Kan H, et al. Usefulness of combining gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging and contrast-enhanced ultrasound for diagnosing the macroscopic classification of small hepatocellular carcinoma. Eur Radiol. (2015) 25:3272–81. doi: 10.1007/s00330-015-3725-0

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Kobayashi K, Maruyama H, Kiyono S, Yokosuka O, Ohtsuka M, Miyazaki M, et al. Histology-based assessment of sonazoid-enhanced ultrasonography for the diagnosis of liver metastasis. Ultras Med Biol. (2017) 43:2151–8. doi: 10.1016/j.ultrasmedbio.2017.06.014

PubMed Abstract | CrossRef Full Text | Google Scholar

102. Quaia E, De Paoli L, Angileri R, Cabibbo B, Cova MA. Indeterminate solid hepatic lesions identified on non-diagnostic contrast-enhanced computed tomography: assessment of the additional diagnostic value of contrast-enhanced ultrasound in the non-cirrhotic liver. Eur J Radiol. (2014) 83:456–62. doi: 10.1016/j.ejrad.2013.12.012

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Sandrose S, Karstrup S, Gerke O, Rafaelsen S. Contrast enhanced ultrasound in CT-undetermined focal liver lesions. Ultras Int Open. (2016) 2:E129. doi: 10.1055/s-0042-120272.

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Schellhaas B, Görtz RS, Pfeifer L, Kielisch C, Neurath MF, Strobel D. Diagnostic accuracy of contrast-enhanced ultrasound for the differential diagnosis of hepatocellular carcinoma: ESCULAP versus CEUS-LI-RADS. Eur J Gastroenterol Hepatol. (2017) 29:1036–44. doi: 10.1097/MEG.0000000000000916

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Tada T, Kumada T, Toyoda H, Ito T, Sone Y, Kaneoka Y, et al. Utility of contrast-enhanced ultrasound with perflubutane for diagnosing the macroscopic type of small nodular hepatocellular carcinomas. Eur Radiol. (2014) 24:2157–66. doi: 10.1007/s00330-014-3254-2

PubMed Abstract | CrossRef Full Text | Google Scholar

106. Thakur S, Jhobta A, Dhiman D, Sood R, Chauhan A, Thakur CS. Role of contrast enhanced ultrasound in characterization of focal liver lesions. Egypt J Radiol Nuclear Med. (2014) 45:7–17. doi: 10.1016/j.ejrnm.2013.11.001

CrossRef Full Text | Google Scholar

107. Wu JP, Shu R, Zhao YZ, Ma GL, Xue W, He QJ, et al. Comparison of contrast-enhanced ultrasonography with virtual touch tissue quantification in the evaluation of focal liver lesions. J Clin Ultras. (2016) 44:347–53. doi: 10.1002/jcu.22335

PubMed Abstract | CrossRef Full Text | Google Scholar

108. Yin S, Cui Q, Yan K, Yang W, Wu W, Bao L, et al. Effect of contrast-enhanced ultrasound on differential diagnosis of intrahepatic cholangiocarcinoma and arterial phase enhanced hepatic inflammatory lesions. Chin J Cancer Res. (2017) 29:272. doi: 10.21147/j.issn.1000-9604.2017.03.13

PubMed Abstract | CrossRef Full Text | Google Scholar

109. Zhang F, Zhu H, Wu Y, Dou Z, Zhang Y, Kleinman N, et al. HIV, hepatitis B virus, and hepatitis C virus co-infection in patients in the China National Free Antiretroviral Treatment Program, 2010–12: a retrospective observational cohort study. Lancet Infect Dis. (2014) 14:1065–72. doi: 10.1016/S1473-3099(14)70946-6

PubMed Abstract | CrossRef Full Text | Google Scholar

110. Takahashi M, Maruyama H, Shimada T, Kamezaki H, Sekimoto T, Kanai F, et al. Characterization of hepatic lesions (≤ 30 mm) with liver-specific contrast agents: a comparison between ultrasound and magnetic resonance imaging. Eur J Radiol. (2013) 82:75–84. doi: 10.1016/j.ejrad.2012.05.035

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Taimr P, Jongerius VL, Pek CJ, Krak NC, Hansen BE, Janssen HL, et al. Liver contrast-enhanced ultrasound improves detection of liver metastases in patients with pancreatic or periampullary cancer. Ultras Med Biol. (2015) 41:3063–9. doi: 10.1016/j.ultrasmedbio.2015.06.019

CrossRef Full Text | Google Scholar

112. Diao X, Zhan J, Chen L, Chen Y, Liu Y. Quantification of solid hypo-echoic thyroid nodule enhancement with contrast-enhanced ultrasound. Transl Cancer Res. (2017) 6:1078–87. doi: 10.21037/tcr.2017.09.15

CrossRef Full Text | Google Scholar

113. Giusti M, Orlandi D, Melle G, Massa B, Silvestri E, Minuto F, et al. Is there a real diagnostic impact of elastosonography and contrast-enhanced ultrasonography in the management of thyroid nodules? J Zhejiang Univ Sci B. (2013) 14:195–206. doi: 10.1631/jzus.B1200106

PubMed Abstract | CrossRef Full Text | Google Scholar

114. Jiang J, Shang X, Wang H, Xu Y-B, Gao Y, Zhou Q. Diagnostic value of contrast-enhanced ultrasound in thyroid nodules with calcification. Kaohsiung J Med Sci. (2015) 31:138–44. doi: 10.1016/j.kjms.2014.12.001

PubMed Abstract | CrossRef Full Text | Google Scholar

115. Zhou X, Zhou P, Hu Z, Tian SM, Zhao Y, Liu W, et al. Diagnostic efficiency of quantitative contrast-enhanced ultrasound indicators for discriminating benign from malignant solid thyroid nodules. J Ultras Med. (2018) 37:425–37. doi: 10.1002/jum.14347

PubMed Abstract | CrossRef Full Text | Google Scholar

116. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. (2003) 56:1129–35. doi: 10.1016/S0895-4356(03)00177-X

PubMed Abstract | CrossRef Full Text | Google Scholar

117. Claudon M, Dietrich CF, Choi BI, Cosgrove DO, Kudo M, Nolsøe CP, et al. Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) in the liver-update 2012. Ultraschall Med. (2013) 34:11–29. doi: 10.1016/j.ultrasmedbio.2012.09.002

PubMed Abstract | CrossRef Full Text | Google Scholar

118. Lee T, Mendhiratta N, Sperling D, Lepor H. Focal laser ablation for localized prostate cancer: principles, clinical trials, and our initial experience. Rev Urol. (2014) 16:55.

PubMed Abstract | Google Scholar

119. Friedrich-Rust M, Buggisch P, De Knegt R, Dries V, Shi Y, Matschenz K, et al. Acoustic radiation force impulse imaging for non-invasive assessment of liver fibrosis in chronic hepatitis B. J Viral Hepatitis. (2013) 20:240–7. doi: 10.1111/j.1365-2893.2012.01646.x

PubMed Abstract | CrossRef Full Text | Google Scholar

120. Morris MJ, Akhurst T, Osman I, Nunez R, Macapinlac H, Siedlecki K, et al. Fluorinated deoxyglucose positron emission tomography imaging in progressive metastatic prostate cancer. Urology. (2002) 59:913–8. doi: 10.1016/S0090-4295(02)01509-1

PubMed Abstract | CrossRef Full Text | Google Scholar

121. Porter TR, Mulvagh SL, Abdelmoneim SS, Becher H, Belcik JT, Bierig M, et al. Clinical applications of ultrasonic enhancing agents in echocardiography: 2018 American Society of Echocardiography guidelines update. J Am Soc Echocardiogr. (2018) 31:241–74. doi: 10.1016/j.echo.2017.11.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: contract enhanced ultrasonography, malignant lesions benign lesions, network meta analysis, shear wave elastography, lesions

Citation: Huang R, Jiang L, Xu Y, Gong Y, Ran H, Wang Z and Sun Y (2019) Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis. Front. Oncol. 9:102. doi: 10.3389/fonc.2019.00102

Received: 24 August 2018; Accepted: 04 February 2019;
Published: 05 March 2019.

Edited by:

Giuseppe Esposito, MedStar Georgetown University Hospital, United States

Reviewed by:

Meiyappan Solaiyappan, Johns Hopkins University, United States
Vishwa S. Parekh, Johns Hopkins University, United States

Copyright © 2019 Huang, Jiang, Xu, Gong, Ran, Wang and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yang Sun, sy19850905@126.com

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