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. 2025 Apr 25;53(7):1565–1573. doi: 10.1002/jcu.24015

Application of Two‐Dimensional Shear Wave Elastography in Evaluating Liver Reserve Function in Patients With Liver Cancer

Bijuan Hu 1, Zhiping Huang 1, Liyin Huang 1,✉
PMCID: PMC12413556  PMID: 40277022

ABSTRACT

Background

Shear wave elastography (SWE) is an emerging noninvasive imaging technique for assessing liver fibrosis. This meta‐analysis aimed to evaluate the diagnostic accuracy of SWE compared to conventional imaging techniques.

Methods

A systematic search was conducted in PubMed, Embase, Cochrane Library, and Web of Science databases. Nine studies were included, and diagnostic performance metrics, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC), were pooled using meta‐analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test.

Results

The pooled sensitivity and specificity of SWE were 0.88 (95% CI: 0.83–0.92) and 0.88 (95% CI: 0.83–0.91), respectively. The positive likelihood ratio (PLR) was 7.07 (95% CI: 5.26–9.50), and the negative likelihood ratio (NLR) was 0.14 (95% CI: 0.09–0.20). The DOR was 51.85 (95% CI: 29.80–90.19), and the area under the SROC curve (AUC) was 0.94 (95% CI: 0.91–0.96), indicating excellent diagnostic accuracy. Fagan nomogram analysis further confirmed SWE's clinical utility by significantly improving post‐test probability. No significant publication bias was detected (p = 0.26).

Conclusion

SWE is a highly accurate and reliable tool for diagnosing liver fibrosis, demonstrating superior diagnostic performance compared to conventional imaging techniques. SWE has significant potential to serve as a noninvasive alternative to liver biopsy in the clinical assessment of liver fibrosis.

Keywords: chronic liver disease, diagnostic accuracy, liver fibrosis, meta‐analysis, noninvasive imaging, sensitivity, shear wave elastography, specificity

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1. Introduction

Liver fibrosis is a progressive pathological condition characterized by excessive accumulation of extracellular matrix proteins, often resulting from chronic liver injury (Hu et al. 2023; Wang et al. 2024; Chen et al. 2015). Its progression can lead to cirrhosis, liver failure, and hepatocellular carcinoma, making it a critical indicator of liver disease severity (Sotoudeheian 2024). The clinical significance of liver fibrosis lies in its ability to reflect the extent of liver damage and its potential to predict adverse outcomes in patients with chronic liver diseases, such as chronic viral hepatitis, alcoholic liver disease, and nonalcoholic fatty liver disease (Shabangu et al. 2020; Xu et al. 2022). Early diagnosis and accurate staging of liver fibrosis are crucial for preventing progression to cirrhosis and liver cancer (Karmacharya et al. 2020). Timely intervention can significantly improve prognosis by guiding therapeutic decisions and reducing the risk of complications (Guy and Guy 2024). Therefore, the assessment of liver fibrosis has become a cornerstone in the management of chronic liver diseases.

Traditional imaging techniques, such as ultrasound and computed tomography (CT), have limitations in their ability to qualitatively and quantitatively assess liver fibrosis (Petitclerc et al. 2017; Zheng et al. 2023). Although ultrasound is widely used because of its accessibility and noninvasive nature, it offers limited sensitivity in detecting early‐stage fibrosis (Lee 2021). Similarly, CT scans can provide information about liver structure but lack the resolution needed to accurately stage fibrosis (Yin et al. 2021). These limitations highlight the need for more reliable and precise diagnostic tools. In addition to imaging techniques, noninvasive scoring systems have been developed to assess liver fibrosis. The FIB‐4 index, NAFLD fibrosis score (NFS), and AST to platelet ratio index (APRI) are widely used blood‐based tests that can help predict the presence and stage of fibrosis in liver diseases such as chronic hepatitis B and C, NAFLD, and alcohol‐related liver disease. These scores rely on easily measurable clinical parameters, including age, liver enzymes, platelet count, and AST/ALT ratios, and have shown promise in identifying patients at risk for advanced fibrosis (Huang et al. 2021; Patel et al. 2018). However, these scores may have limitations in certain patient populations or in distinguishing between early stages of fibrosis, necessitating the development of more accurate noninvasive methods such as shear wave elastography (SWE). SWE represents a significant advancement in the noninvasive assessment of liver fibrosis (Almutawakel et al. 2023; Furlan et al. 2020). By measuring liver tissue stiffness, SWE provides a quantitative evaluation of liver elasticity, which correlates closely with the degree of fibrosis. Unlike traditional imaging methods, SWE offers both high accuracy and reproducibility, making it a promising tool for liver fibrosis assessment. In addition, transient elastography (TE) and magnetic resonance imaging (MRI) are two other noninvasive techniques that are gaining increasing recognition in clinical practice due to their respective advantages and applications in fibrosis assessment.

This study aims to compare the diagnostic accuracy of SWE with conventional imaging techniques in the evaluation of liver fibrosis. Through a meta‐analysis, we aim to synthesize existing evidence and provide a comprehensive understanding of the relative performance of SWE and traditional imaging methods, thereby offering insights into their clinical utility.

2. Methods

2.1. Search Strategy

This study conducted a search of PubMed, Embase, Cochrane Library, and Web of Science databases to identify potentially relevant studies. The search period covered the inception of each database up to December 2024, with the included literature limited to English and Chinese languages. The search strategy used core keywords such as “shear wave elastography” or “SWE,” “liver fibrosis” or “hepatic fibrosis,” and “conventional imaging” or “ultrasound” or “CT” or “MRI.” Boolean operators (AND, OR, NOT) were employed to combine keywords, for example: “(‘shear wave elastography’ OR ‘SWE’) AND (‘liver fibrosis’ OR ‘hepatic fibrosis’) AND (‘conventional imaging’ OR ‘ultrasound’ OR ‘CT’ OR ‘MRI’).” This protocol has been registered in PROSPERO under the registration number CRD42024615444.

2.2. Inclusion and Exclusion Criteria

2.2.1. Inclusion Criteria

This study included research that met the following criteria: (1) Studies that assessed the diagnostic accuracy of SWE in comparison with conventional imaging techniques (e.g., ultrasound, CT, or MRI) for the evaluation of liver fibrosis; (2) studies that provided diagnostic performance metrics such as sensitivity, specificity, or the area under the receiver operating characteristic curve (AUC); (3) studies with clearly described methodologies, particularly those using liver biopsy as the diagnostic reference standard; and (4) full‐text studies published in English or Chinese.

2.2.2. Exclusion Criteria

Studies were excluded if they met any of the following conditions: (1) Studies that did not provide specific diagnostic data necessary for calculating sensitivity, specificity, or other performance metrics; (2) studies with insufficient sample sizes to ensure statistical power; (3) duplicate publications or studies containing overlapping data with previously included research; (4) case reports, review articles, or conference abstracts without full‐text availability, as well as studies conducted on animal models rather than human subjects; and (5) studies with unclear methodologies or those that did not utilize liver biopsy as the diagnostic reference standard.

2.3. Data Extraction

Data extraction was conducted systematically to ensure consistency and accuracy. The following variables were extracted from each included study: (1) Study design, including prospective or retrospective methodologies; (2) patient characteristics, such as sample size, age, gender distribution, and the etiology of liver fibrosis (e.g., viral hepatitis, nonalcoholic fatty liver disease); (3) the type of conventional imaging technique used as a comparator (e.g., ultrasound, CT, or MRI); (4) the diagnostic reference standard, with emphasis on liver biopsy as the gold standard; and (5) primary diagnostic performance metrics, including sensitivity, specificity, and the AUC. All extracted data were cross‐checked by two independent reviewers to ensure reliability, and any discrepancies were resolved through discussion or consultation with a third reviewer.

2.4. Quality Assessment

The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS), focusing on three domains: selection (study population representativeness), comparability (control of confounding factors), and outcome/exposure (assessment methods and completeness of follow‐up). Each study was scored out of 9 points, with scores of 7–9 indicating high quality, 4–6 moderate quality, and below 4 low quality. Two independent reviewers conducted the assessment, resolving discrepancies through discussion or consultation with a third reviewer.

2.5. Statistical Methods

A comprehensive meta‐analysis was performed using STATA 13 to evaluate the diagnostic accuracy of SWE and conventional imaging techniques for liver fibrosis assessment. The primary diagnostic performance metrics, including sensitivity, specificity, diagnostic odds ratio (DOR), and the area under the receiver AUC, were pooled to provide a comprehensive evaluation of SWE's diagnostic ability in comparison to conventional imaging. Statistical heterogeneity across studies was assessed using the I 2 statistic, with values above 50% indicating significant heterogeneity. Potential publication bias was evaluated through Egger's test and funnel plot analysis, with asymmetry in the funnel plot suggesting possible bias.

3. Results

3.1. Literature Search and Characteristics of Included Studies

A comprehensive literature search identified a total of 1584 records from PubMed, Cochrane Library, Embase, and Web of Science databases. After removing 233 duplicate records, 1351 studies remained for screening. Following the review of titles and abstracts, 1135 studies were excluded for not meeting the inclusion criteria. The remaining 216 studies were sought for full‐text retrieval, with all full texts available. Among the 216 full‐text studies assessed for eligibility, 207 were excluded for the following reasons: missing key data (n = 120), studies not involving SWE interventions (n = 19), and animal experiments (n = 68). Ultimately, 9 studies met the inclusion criteria and were included in this meta‐analysis (Figure 1). Details are provided in Table 1.

FIGURE 1.

FIGURE 1

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Schematic diagram of the literature screening process.

TABLE 1.

General information of the included literature.

Author Sample size Age Body mass index (kg/m2) Sex ratio Hyperlipidemia Diabetes SWE type Control Diagnostic standard tp fp fn tn
Almutawakel et al. (2023) 15 48 (13) 23.3 (4.5) 8/7 / / SWE MRE Biopsy 8 1 2 4
Furlan et al. (2020) 57 50 (13) 34.8 (7.2) 36/24 23 (37%) 22 (35%) 2D‐SWE MRE Biopsy 24 6 6 24
Lefebvre et al. (2019) 100 55 (12) 30.1 (5.9) 53/47 37 (37%) 30 (30%) Point‐SWE MRE Biopsy 45 5 5 45
Matos et al. (2019) 77 55.87 (8.79) 25.31 (4.04) 67/10 / / 2D‐SWE MRE MRI 36 2 2 37
Reiter et al. (2018) 15 56 (10) 24.07 (3.67) 7/8 / / 2D‐SWE MRE MRI 9 0 1 5
Song et al. (2016) 46 / / 22/24 / / 2D‐SWE MRE Biopsy 20 3 3 20
Zhang et al. (2022) 100 51.8 (12.9) 31.6 (4.7) 46/56 33 (33%) 24 (24%) US‐SWE MRE Biopsy 4 14 1 81
Zhao et al. (2014) 49 19–81 / 28/22 / / SWE MRE Biopsy 21 3 3 22
Zheng et al. (2015) 100 36.6 (9.7) 21.6 (3.4) 71/29 / / 2D‐SWE MRE Biopsy 18 10 2 70
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3.2. Quality Assessment of Included Studies

The quality of the included studies was evaluated using NOS. Of the 9 included studies, 6 were rated as high quality (Scores 7–9), and 3 were rated as moderate quality (Scores 4–6). No studies were classified as low quality. The results, detailed in Table 2, indicate overall good methodological quality, ensuring the reliability of the evidence for subsequent analysis.

TABLE 2.

Quality assessment of included studies (Newcastle–Ottawa Scale).

Author Selection (0–4) Comparability (0–2) Outcome/exposure (0–3) Total score (0–9) Quality rating
Almutawakel et al. (2023) 4 2 3 9 High quality
Furlan et al. (2020) 4 1 3 8 High quality
Lefebvre et al. (2019) 3 2 3 8 High quality
Matos et al. (2019) 4 1 2 7 High quality
Reiter et al. (2018) 3 1 2 6 Moderate quality
Song et al. (2016) 4 1 3 8 High quality
Zhang et al. (2022) 3 1 2 6 Moderate quality
Zhao et al. (2014) 3 1 2 6 Moderate quality
Zheng et al. (2015) 4 2 3 9 High quality
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3.3. Meta‐Analysis Results

3.3.1. Diagnostic Performance Analysis of SWE

A total of 9 studies were included in the meta‐analysis to evaluate the diagnostic accuracy of SWE for liver fibrosis. The pooled sensitivity was 0.88 (95% CI: 0.83–0.92; I 2 = 0.00%), and the pooled specificity was 0.88 (95% CI: 0.83–0.91; I 2 = 0.00%). The combined positive likelihood ratio (PLR) was 7.07 (95% CI: 5.26–9.50; I 2 = 0.00%), whereas the negative likelihood ratio (NLR) was 0.14 (95% CI: 0.09–0.20; I 2 = 0.00%). The pooled DOR was 51.85 (95% CI: 29.80–90.19; I 2 = 85.85%). The diagnostic score was 3.95 (95% CI: 3.39–4.50; I 2 = 0.00%). The summary receiver operating characteristic (SROC) curve demonstrated the overall diagnostic performance of SWE, with an area under the curve (AUC) of 0.94 (95% CI: 0.91–0.96), indicating excellent diagnostic accuracy. These results are visualized in Figures 2, 3, 4, 5, showing SWE's robust ability to detect liver fibrosis with high sensitivity and specificity.

FIGURE 2.

FIGURE 2

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Sensitivity and specificity of SWE diagnosis.

FIGURE 3.

FIGURE 3

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PLR and NLR of SWE diagnosis.

FIGURE 4.

FIGURE 4

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Diagnostic odds ratios and diagnostic score.

FIGURE 5.

FIGURE 5

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SROC of SWE diagnosis.

3.3.2. Pre‐ and Post‐Test Probability

The diagnostic utility of SWE was further evaluated using the Fagan nomogram to assess the impact of test results on post‐test probability under a given pre‐test probability. With a pre‐test probability of 50%, the analysis demonstrated that a PLR (LR+) of 7 increased the post‐test probability to 88%, whereas a NLR (LR−) of 0.14 reduced the post‐test probability to 12%. These results, as illustrated in Figure 6, highlight the strong clinical application value of SWE. Even under conditions of moderate pre‐test probability, SWE can significantly improve the likelihood of accurately diagnosing liver fibrosis.

FIGURE 6.

FIGURE 6

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Fagan diagram.

3.3.3. Publication Bias

Publication bias was assessed using Deeks' funnel plot asymmetry test. The results showed that the funnel plot slope was close to zero, with a p value of 0.26 (p > 0.05), indicating no significant publication bias among the included studies. These findings are presented in Figure 7.

FIGURE 7.

FIGURE 7

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Funnel plot.

4. Discussion

This meta‐analysis comprehensively evaluated the diagnostic performance of SWE in assessing liver fibrosis compared to conventional imaging techniques. The pooled sensitivity (0.88, 95% CI: 0.83–0.92) and specificity (0.88, 95% CI: 0.83–0.91) highlight the high diagnostic accuracy of SWE. Additionally, the PLR (7.07) and NLR (0.14) indicate SWE's strong ability to confirm liver fibrosis in positive cases and exclude it in negative cases. The DOR of 51.85 further reflects SWE's overall effectiveness as a noninvasive diagnostic tool. The area under the SROC curve (AUC = 0.94) provides robust evidence of SWE's excellent diagnostic performance, emphasizing its clinical utility.

4.1. Comparison With Conventional Imaging Techniques

Compared with traditional imaging methods such as ultrasound, CT, and MRI, SWE offers significant advantages in evaluating liver fibrosis (Bauer et al. 2024; Chimoriya et al. 2020). Conventional imaging primarily provides qualitative or semi‐quantitative information and lacks sensitivity, especially for early‐stage fibrosis (Lefebvre et al. 2019). SWE, by contrast, quantitatively measures liver tissue stiffness, providing an objective and reproducible assessment (Matos et al. 2019; Yan et al. 2024). This advantage is reflected in the pooled diagnostic metrics, which consistently show SWE's higher accuracy across studies. In the included studies, variations in clinical indications were observed. For instance, Almutawakel et al. (2023) focused on HCV‐infected kidney transplant recipients, whereas Furlan et al. (2020) primarily investigated NAFLD patients. These differences in patient populations may contribute to variations in diagnostic performance. Additionally, some studies compared SWE with noninvasive fibrosis scores such as FIB‐4 and APRI. Zhang et al. (2022) demonstrated that SWE had a higher AUC (0.94) than FIB‐4 (0.79) and APRI (0.76), reinforcing its superior diagnostic accuracy in NAFLD patients. These findings suggest that the effectiveness of SWE may be influenced by the underlying etiology of liver disease, which should be considered when interpreting results.

4.2. Clinical Implications

The findings of this meta‐analysis underscore SWE's potential as a reliable, noninvasive alternative to liver biopsy, which remains the gold standard for diagnosing liver fibrosis but is invasive, costly, and prone to sampling errors. SWE can be effectively integrated into clinical practice for screening, staging, and monitoring liver fibrosis, particularly in patients with chronic liver diseases such as viral hepatitis, NAFLD, and alcoholic liver disease. Furthermore, the results from the Fagan nomogram analysis demonstrate that SWE significantly improves the post‐test probability of diagnosing liver fibrosis, even with moderate pre‐test probabilities.

4.3. Strengths and Limitations

This study has several strengths, including the use of a rigorous meta‐analytic approach, comprehensive assessment of diagnostic accuracy metrics, and robust quality evaluation of included studies. The low heterogeneity observed in sensitivity and specificity results (I 2 = 0.00%) enhances the reliability of the pooled estimates. Additionally, the Deeks' funnel plot analysis revealed no significant publication bias (p = 0.26), further supporting the robustness of our findings. However, some limitations must be acknowledged. First, the small number of included studies (n = 9) may limit the generalizability of the results. Second, variability in patient populations (e.g., different liver disease etiologies) and SWE measurement protocols could influence the diagnostic performance. Finally, although SWE demonstrated high accuracy, its performance in specific subgroups, such as early‐stage fibrosis, requires further investigation through large‐scale, well‐designed prospective studies.

4.4. Future Directions

Future research should focus on evaluating SWE's diagnostic performance across diverse populations and liver disease subtypes. Standardization of SWE protocols and thresholds for liver stiffness measurements is essential to minimize inter‐study variability and facilitate widespread clinical adoption. Additionally, studies combining SWE with other imaging modalities or biomarkers could be explored to further enhance diagnostic accuracy.

4.5. Conclusion

In conclusion, this meta‐analysis demonstrates that SWE is a highly accurate and reliable noninvasive tool for diagnosing liver fibrosis. With excellent sensitivity, specificity, and DOR, SWE has significant clinical potential to replace or complement conventional imaging techniques and liver biopsy, particularly for the noninvasive evaluation and management of chronic liver diseases.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

The authors have nothing to report.

Hu, B. , Huang Z., and Huang L.. 2025. “Application of Two‐Dimensional Shear Wave Elastography in Evaluating Liver Reserve Function in Patients With Liver Cancer.” Journal of Clinical Ultrasound 53, no. 7: 1565–1573. 10.1002/jcu.24015.

Funding: This study was supported by Ganzhou City Guided Science and Technology Plan Project, Project Number: S2024‐NSLY‐0528.

Bijuan Hu contributed as a lead author for this work.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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