Diagnostic performance of shear wave elastography and contrast-enhanced ultrasound in evaluating the pathological response of breast cancer patients to neoadjuvant chemotherapy: a meta-analysis

Introduction

Breast cancer is the most common type of cancer and the second leading cause of cancer-related death in women worldwide (1). According to data from the Global Cancer Statistics Report, there were approximately 2,309,000 new cases of breast cancer worldwide in 2022, approximately 357,000 of which occurred in China (2). Neoadjuvant chemotherapy (NAC) can not only effectively reduce the size of the tumor, making it operable, it can also improve the success rate of breast conservation surgery and provide information about the sensitivity (Sen) of the tumor to chemotherapeutic agents, which is an important basis for subsequent individualized therapy (3,4).

Patients who achieve a pathological complete response (pCR) after NAC have longer disease-free and overall survival than those who do not achieve pCR, and generally have a more favorable clinical prognosis (5). However, breast cancer is a heterogeneous disease, and there is significant variability in the clinical response of patients to NAC. Some patients benefit significantly from NAC; however, others may experience limited efficacy or even disease progression during treatment. Thus, the accurate assessment of patients’ pathological response to NAC has become an urgent clinical challenge.

Ultrasound, a key screening modality for breast cancer, offers real-time, dynamic, safe, and cost-effective advantages, and has widespread application in the diagnosis and treatment monitoring of breast cancer patients (6). However, its diagnostic performance varies. Conventional ultrasound has an overall Sen of 67.2% (range: 26.9–87.5%) and a specificity (Spe) of 76.8% (range: 18.8–96.9%) in the detection of breast cancer (7). In axillary nodal burden assessment, axillary ultrasound has a Sen of 75.7% and a Spe of 92.9%, but its Sen declines significantly in specific subtypes like nodal metastatic invasive lobular carcinoma (8). These limitations underscore the need for advanced ultrasonographic techniques.

Contrast-enhanced ultrasound (CEUS) has attracted significant attention in the past decade. Microbubble contrast agent injection and the real-time observation of blood perfusion in tissue microcirculation improves the Sen and Spe of CEUS in the diagnosis of breast cancer (9). Several studies (10,11) have shown the high diagnostic performance of CEUS in evaluating the pathological response of breast cancer patients to NAC. For example, Jia et al. (3) conducted a meta-analysis of the pathological response to NAC in breast carcinoma patients, and reported that the combined Sen, Spe, and area under the curve (AUC) were 87% [95% confidence interval (CI), 0.81–0.92], 84% (95% CI, 0.74–0.91), and 0.92 (95% CI, 0.89–0.94), indicating CEUS has strong prognostic value.

In recent years, the new ultrasound technique of shear wave elastography (SWE) has gradually attracted the attention of researchers. SWE reflects the elasticity of tissues in real time and quantitatively measures displacement changes in tissues under acoustic excitation, providing a new perspective for the diagnosis and efficacy assessment of breast cancer. Systematic evaluations and meta-analyses have shown the strong diagnostic capabilities of SWE in predicting the response to NAC, which suggests that it could serve as a non-invasive and reliable method for evaluating the treatment response (12).

However, to date, no study has directly and systematically compared the diagnostic performance of CEUS and SWE in assessing the pathological response of breast cancer patients to NAC. This study aimed to address this gap in the literature by systematically reviewing relevant articles to compare the diagnostic value of CEUS and SWE in evaluating the pathological response of breast cancer patients to NAC. We present this article in accordance with the PRISMA reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2730/rc).

Methods

This meta-analysis was registered on the PROSPERO website (registration number CRD42024613894).

Search strategy

Th PubMed, Embase, and Web of Science databases were comprehensively searched to retrieve relevant articles published from database inception to July 2024. The following search terms were used: “breast cancer”, “elastography or shear wave elastography”, “contrast-enhanced ultrasound”, and “neoadjuvant chemotherapy”. The search was conducted in English. A combination of subject terms and free terms were used in the search method to maximize the retrieval of relevant articles, and the strategy included online database searches and manual searches, and a review of the reference lists from the selected studies.

Study selection

To be eligible for inclusion in the meta-analysis, the studies had to meet the following inclusion criteria: (I) examine breast cancer patients undergoing NAC, in whom the tumor response of the primary breast tumor had been histopathologically confirmed after treatment; (II) assess NAC efficacy using SWE or CEUS; (III) provide adequate diagnostic performance data (e.g., the Sen, Spe, and accuracy); and (IV) have been published in the English language.

Articles were excluded from the meta-analysis if they met any of the following exclusion criteria: (I) related to a review, conference abstract, commentary, case report, or letter; (II) included repeatedly published research; (III) used incomplete or unextractable datasets; and/or (IV) related to animal research.

In this study, a pathological responder was defined as a patient who achieved a pCR or who met all response grades [e.g., the Miller-Payne and Residual Cancer Burden (RCB) classifications].

Literature inclusion

To identify the studies eligible for inclusion, two authors independently reviewed titles and abstracts. Potentially relevant studies were then downloaded, and full texts were carefully evaluated to finalize inclusions. Disagreements were resolved by consulting a third author.

Data extraction

The principal investigator used a pre-designed data extraction form to extract detailed information, including the first author, year of publication, country, study design, number of patients, mean age, pathological response profile, response rate, and the Sen, Spe, and AUC of the test being evaluated.

Statistical analysis

The statistical analyses in this study were performed using Stata 18 (StataCorp LLC) and RevMan 5.4 (Cochrane Collaboration) software. Statistical software was used to plot the summary receiver operating characteristic (SROC) curve, and publication bias funnel plots, and to calculate the Sen, Spe, and AUC of the diagnoses, respectively. In addition, the post-test probabilities were calculated and plotted using Fagan’s diagrams. Based on the results, we assessed between-study heterogeneity quantitatively. If the Q-test results met the criteria of P<0.1 and I²≤50%, a fixed-effects model was used; otherwise, a random-effects model was used. Meta-regression and subgroup analyses were conducted to assess the sources of clinical heterogeneity. P values <0.05 were considered statistically significant.

Literature quality evaluation

The Quality Assessment of Diagnostic Accuracy Study Form (QUADAS-2) was used to assess the methodological quality of each of the included studies, including patient selection, metrics testing, reference standards, processes, and timelines. Each item was assessed as “yes” (indicating a low level of bias or good applicability), “no” (indicating a high level of bias or poor applicability), or “unclear” (indicating a lack of relevant information or uncertainty of bias). The included studies were assessed independently by two authors. Any disagreements arising during the assessment process were resolved by discussion.

Results

Literature search results

A total of 925 articles were initially retrieved from the database search. After the title and abstract review, 205 articles remained, of which a further 66 duplicate articles were excluded. In total, 139 articles underwent full-text review. The articles were then rigorously screened based on the inclusion and exclusion criteria, and ultimately, 23 articles were deemed eligible for inclusion in the meta-analysis. After a careful reading of the full text, it was found that there were two articles published at different times by the same author, in which the data partially overlapped, and one of the articles with a shorter research timeframe was screened out, ultimately leaving 22 articles. The detailed inclusion process is illustrated in Figure 1.

Figure 1 Study flow chart detailing the reasons for the exclusion of studies and the total number (n=22) of included studies. SWE, shear wave elastography.

Characteristics of the eligible studies

The characteristics of the 22 included studies, comprising 1,725 participants, are summarized in Table 1. The sample sizes of the included studies ranged from 28 to 145. Of the 22 included studies, 11 assessed the value of CEUS (13-23) and 11 assessed the value SWE (24-34) in monitoring the pathological response of breast cancer patients to NAC. The studies originated from diverse regions: 14 were conducted in China (15-17,19-25,28,31,33,34), two in India (18,30), and one each in the United States (19), the United Kingdom (26), Germany (27), Korea (14), Japan (13), and Egypt (32). Further, of the 22 included studies, three were retrospective (17,21,31), 16 were prospective (14,16,18,19,22-30,32-34), and three did not report the study type (13,15,20).

Data analysis

The CEUS group included 11 studies, comprising 770 breast cancer patients. The pooled diagnostic performance metrics of CEUS in monitoring the pathological response of breast cancer patients to NAC were determined. Specifically, the Sen, Spe, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of CEUS in monitoring the pathological response of breast cancer patients to NAC were 0.88 (95% CI, 0.80–0.93), 0.80 (95% CI, 0.74–0.84), 4.3 (95% CI, 3.3–5.6), 0.15 (95% CI, 0.09–0.26), and 28 (95% CI, 15–55), respectively. Similarly, the SWE data of 11 studies, comprising 955 breast cancer patients, were examined. The combined Sen, Spe, PLR, NLR, and DOR of SWE in monitoring the pathological response of breast cancer patients to NAC were 0.82 (95% CI, 0.77–0.85), 0.81 (95% CI, 0.74–0.86), 4.2 (95% CI, 3.1–5.7), 0.23 (95% CI, 0.18–0.29), and 18 (95% CI, 11–30), respectively.

CEUS had a higher Sen than SWE. The SROC curves for CEUS and SWE are shown in Figure 2. SWE had a higher AUC than CEUS (0.88 vs. 0.86). Both the CEUS and SWE studies had moderate likelihood ratios and posterior probabilities (Figure 3). When the pre-test was positive, the use of CEUS increased the post-test probability from 50% to 81% with a PLR of 4; when the pre-test was negative, the use of CEUS reduced the post-test probability to 13% with an NLR of 0.15; when the pre-test was positive, the use of SWE increased the post-test probability from 50% to 81% with a PLR of 4; when the pre-test was negative, the use of SWE reduced the post-test probability to 19% with an NLR of 0.23. These findings highlight the utility of CEUS and SWE in improving the diagnostic accuracy of these methods in assessing the pathological response of breast cancer patients to NAC.

Figure 2 SROC curves. (A) The SROC curve for CEUS. 1 represents the study of Amioka et al. (13); 2 represents the study of Lee et al. (14); 3 represents the study of Wang et al. (15); 4 represents the study of Huang et al. (16); 5 represents the study of Peng et al. (17); 6 represents the study of Sharma et al. (18); 7 represents the study of Guo et al. (19); 8 represents the study of Liu et al. (20); 9 represents the study of Han et al. (21); 10 represents the study of Wan et al. (22); 11 represents the study of Xie et al. (23); (B) the SROC curve for SWE. 1 represents the study of Jing et al. (24); 2 represents the study of Ma et al. (25); 3 represents the study of Evans et al. (26); 4 represents the study of Maier et al. (27); 5 represents the study of Zhang et al. (28); 6 represents the study of Gu et al. (29); 7 represents the study of Singh et al. (30); 8 represents the study of Duan et al. (31); 9 represents the study of Elmoghazy et al. (32); 10 represents the study of Qi et al. (33); 11 represents the study of Huang et al. (34). AUC, area under the curve; CEUS, contrast-enhanced ultrasound; Sen, sensitivity; SROC, summary receiver operating characteristic; Spe, specificity; SWE, shear wave elastography.

Figure 3 Fagan plots of pre-test probability at 50% for the evaluation of the pathological response of breast cancer patients to neoadjuvant chemotherapy. (A) Fagan plot for contrast-enhanced ultrasound; (B) Fagan plot for shear wave elastography. Each Fagan plot features a left vertical axis representing the pre-test probability, a middle vertical axis representing the likelihood ratio, and a right vertical axis representing the post-test probability. Prob, probability; LR, likelihood ratio; pos, positive; neg, negative.

Publication bias

The P values of CEUS and SWE using the linear regression test were 0.79 and 0.56, respectively (P>0.10), and no significant asymmetry was observed in the funnel plots. Deeks’ funnel plots (Figure 4) further suggested a minimal likelihood of publication bias in this meta-analysis.

Figure 4 The publication bias of the included studies. No significant publication bias was found in the present meta-analysis. Each circle represents an eligible research study. (A) The publication bias of contrast-enhanced ultrasound. 1 represents the study of Amioka et al. (13); 2 represents the study of Lee et al. (14); 3 represents the study of Wang et al. (15); 4 represents the study of Huang et al. (16); 5 represents the study of Peng et al. (17); 6 represents the study of Sharma et al. (18); 7 represents the study of Guo et al. (19); 8 represents the study of Liu et al. (20); 9 represents the study of Han et al. (21); 10 represents the study of Wan et al. (22); 11 represents the study of Xie et al. (23). (B) The publication bias of shear wave elastography. 1 represents the study of Jing et al. (24); 2 represents the study of Ma et al. (25); 3 represents the study of Evans et al. (26); 4 represents the study of Maier et al. (27); 5 represents the study of Zhang et al. (28); 6 represents the study of Gu et al. (29); 7 represents the study of Singh et al. (30); 8 represents the study of Duan et al. (31); 9 represents the study of Elmoghazy et al. (32); 10 represents the study of Qi et al. (33); 11 represents the study of Huang et al. (34). 1/root (ESS), square root of the reciprocal of ESS; ESS, effective sample size.

Literature quality assessment

The quality of all the included articles was assessed using RevMan 5.4 (Cochrane Collaboration), and the results of the literature quality assessment are shown in Figure 5. Patient selection and the index test were the main sources of bias. In terms of patient selection, 15 studies were assessed as having an unclear risk of bias due to non-randomized or non-sequential selection, and two studies were assessed as being at high risk of bias due to the absence of a specified time limit and unclear study design (randomized or consecutive). None of the studies were assessed as having a high risk of bias in terms of the implementation and interpretation of the reference standard.

Figure 5 Quality analysis of the included studies based on the QUADAS-2 criteria. Judgements of the review authors about each domain for each included study. (A) Quality analysis for contrast-enhanced ultrasound studies; (B) quality analysis of shear wave elastography studies.

Heterogeneity detection

CEUS had a Q-test for Sen of P<0.001 and an I² statistic of 69.81%, and a Q-test for Spe of P=0.15 and an I² statistic of 31.01% (Figure 6A). These results suggested that there was some heterogeneity in the Sen between the included studies and statistical significance. SWE had a Q-test for Sen of P=0.10 and an I² statistic of 36.88%, and a Q-test for Spe of P=0.03 and an I² statistic of 49.72% (Figure 6B). These results suggested mild (but not statistically significant) heterogeneity among the included studies. Therefore, a random-effects model was employed.

Figure 6 Forest plot of the sensitivity and specificity in predicting the pathological response of patients. (A) Forest plot of the sensitivity and specificity of contrast-enhanced ultrasound; (B) forest plot of the sensitivity and specificity of shear wave elastography. The horizontal lines illustrate the 95% CIs of the individual studies. CI, confidence interval; df, degree of freedom; I², I² statistic for heterogeneity.

The meta-regression analysis for the CEUS group revealed that factors such as publication year, patient age, study design, geographic location, and sample size did not significantly contribute to the observed heterogeneity (Table 2).

Sen analysis

The Sen analysis of this study was performed by excluding the articles one by one. The Sen and Spe analysis results are set out in Table 3. The results showed that there was no significant change in the Sen and Spe after removing each article, indicating that the statistical results were robust. However, when the article of Lee et al. (14) was excluded, the AUC decreased to 0.79 (95% CI, 0.76–0.83), warranting further investigation into the reasons for this effect.

Discussion

Angiogenesis and neovascularization are key factors in the growth, infiltration, and metastasis of breast cancer. Chemotherapeutic drugs reduce the rate of tumor cell proliferation, and atrophy the nourishing blood vessels of tumor cells, resulting in necrosis. Tumors that are responsive to NAC show reduced blood supply, tumor mesenchyme softening, and connective tissue proliferative response alteration, which in turn affect the hardness of the tumor (28).

Studies have shown that dynamic contrast-enhanced magnetic resonance imaging (MRI) can quantitatively measure kinetic parameters related to tumor perfusion and vascular permeability (35,36), and thus have potential in therapeutic efficacy evaluation. Conversely, CEUS uses microbubble-based agents to dynamically visualize microcirculatory perfusion in tissues and lesions in real time, providing diagnostic insights via perfusion patterns. Unlike MRI contrast agents that diffuse into the extracellular interstitial space, CEUS microbubbles remain strictly intravascular. This unique property enables CEUS to assess microvessels undetectable by MRI (37), thereby offering a direct and precise reflection of lesion microcirculation perfusion. Amioka et al. (13) found that the Sen of ultrasonography was significantly higher than that of MRI (95.7% vs. 69.6%, P=0.047).

The present meta-analysis provides further evidence of the high diagnostic performance of CEUS, which had a pooled Sen of 0.88 and a Spe of 0.80 in monitoring the NAC response. Our findings are consistent with previous findings on the superiority of CEUS in microvascular assessment (13,37). We performed meta-regression analyses on 11 CEUS studies, and found that CEUS had moderate heterogeneity in terms of its Sen. The grouping variables were as follows: (I) pathological assessment (pCR or responders); (II) study participants (Asian or non-Asian); (III) number of patients enrolled in the study (<100 or ≥100) patients; 4, mean age of the patients (<50 or ≥50 years), 5, number of chemotherapy cycle (<2 or ≥2 cycles); and 6, year of the article’s publication (≤2020 or >2020). None of the above variables were found to be a cause of heterogeneity.

The articles were excluded one by one in the Sen analysis. After excluding the study of Lee et al. (14), the AUC decreased from 0.86 (95% CI, 0.83–0.89) to 0.79 (95% CI, 0.76–0.83). The decrease in the AUC could be due to the inclusion of a study with a very small sample size, resulting in an unstable estimation of diagnostic efficacy, which might be the source of CEUS heterogeneity. When the studies of Liu et al. (20) and Han et al. (21) were excluded, the DOR increased, but the combined Sen and Spe changes were not significant. This might be due to the fact that both studies used qualitative ultrasonography diagnostics to assess the pathological response of breast cancer patients after NAC.

Qualitative ultrasonography assessment is used to assess changes in perfusion in the tumor by observing the distribution and enhancement pattern of the contrast agent in the tumor. Qualitative assessment can visually reflect the tumor’s response to chemotherapy, but it is influenced by the physician’s experience and is highly subjective. The quantitative assessment of ultrasonography uses a time-intensity curve analysis to calculate parameters such as peak intensity (PI), time to peak (TTP), and AUC to quantitatively assess dynamic changes in tumor perfusion (13). These parameters can help assess the response to chemotherapy more accurately by providing more detailed quantitative information. The findings of this research indicate that ultrasound imaging provides a marginally higher level of accuracy than qualitative evaluations when measuring the pathological response of breast cancer patients to NAC.

A careful reading of the literature included in this study revealed similarities and differences in the indicators of the quantitative imaging parameters used to assess the pCR of breast cancer patients after NAC. First, multiple studies (13,15,17,18,23) in this meta-analysis showed that the breast cancer patients who showed a pathological response post-NAC had significantly lower PI than those who showed no response. PI refers to the maximum contrast agent intensity achieved during the contrast procedure. A decrease in PI suggests a decrease in tumor blood flow, potentially resulting from the anti-angiogenic effects of chemotherapy, or a reduction in local vascular growth factors due to the death of tumor cells, thereby reflecting the effect of the chemotherapeutic treatment (38). We concluded that PI is a potential predictor of an early response to NAC in breast cancer.

Second, studies by Huang et al. (16) and Xie et al. (23) showed that TTP was elevated after two NAC cycles, and that changes in TTP differed significantly between the pathological response and no pathological response groups. Sharma et al. (18) showed that TTP differed significantly between good and poor responders after one, two, and three cycles. The findings of Wan et al. (22) were similar to those of Sharma et al. (18), and suggested that change in TTP was associated with the pathological response after both one and two cycles of NAC. TTP refers to the time it takes for the first microbubble to enter the lesion and reach maximum intensity. Responders may have a prolonged ultrasound contrast participation TTP compared to non-responders because the reduction in blood perfusion after NAC results in a slower washout of the contrast agent, which is indicative of the favorable efficacy of NAC. Thus, we reasoned that PI and the TTP could also be useful in assessing early responses to NAC. However, Guo et al. (19), Wang et al. (15), and Amioka et al. (13) concluded that there was no significant difference in the TTP between responders and non-responders, and Guo et al. (19) suggested this might be because the TTP cannot be effectively changed until after two cycles of NAC.

The variability in results may stem from differing regions of interest (ROI) selection protocols across studies. In their respective studies, Xie et al. and Huang et al. (16,23) focused on tumor regions with the highest blood flow density, while Sharma et al. (18) examined the tumor’s most uniform solid sections for cursor placement, and Wan et al. (22) manually outlined the entire lesion as the ROI. The common goal across these approaches was to minimize the disruptive effects of calcifications and necrotic areas on study outcomes. Conversely, Guo et al. (19), Wang et al. (15) and Amioka et al. (13) selected the most extensive portion of the tumor for their analyses without ruling out the presence of calcified or necrotic regions.

Given the diversity of breast cancer, different regions of the same tumor may have different characteristics. Calcified and necrotic areas in the tumor may cause abnormal reflection or attenuation of the ultrasound signal due to their unique acoustic properties, which in turn may affect the accurate assessment of perfusion. Therefore, including the entire lesion area in the ROI may lead to discrepancies in contrast results. Choosing the ROI is therefore critical to quantitative analysis using ultrasound (39,40). Quantitative analysis should prioritize ROIs corresponding to the peak enhancement areas on imaging, while systematically excluding avascular necrotic zones and standardizing measurements to minimize confounding effects related to intratumoral heterogeneity.

SWE uses low-frequency vibrations generated by the ultrasound probe to generate shear waves through the tissue, and detects the propagation speed of the shear waves in the tissue to obtain the Young’s modulus of the tissue, thus assessing the hardness of the tissue. The advantage of SWE is that the stiffness (degree of elasticity) of the lesion and the tissue surrounding the lesion can be quantified repeatedly (26). Based on the results analyzed in this study, it appears that the combined Sen and Spe of SWE in assessing the pathological response of breast cancer patients to NAC was high, and no publication bias was observed in any of the studies using the Deeks’ funnel plot, suggesting that SWE has high diagnostic efficacy. However, currently, there are no clearly defined reference values for the diagnostic criteria for SWE. The commonly used parameters for SWE are elasticity modulus and the variation of shear wave velocity (V).

In this study, a total of nine articles (24-26,28-33) supported the statistical significance of elasticity modulus, and its rate of change before and after chemotherapy for predicting pCR. Young’s modulus is a SWE technique that assesses tissue hardness by determining the absolute value of the tissue Young’s modulus (kPa) (41). It includes the elastic maximum (Emax), elastic mean (Emean), and elastic minimum (Emin). Recent studies (24,25,28-30) have systematically shown that the Emean or its rate of change is statistically significant in predicting the pathological response of breast cancer patients to the second cycle of NAC. Conversely, Qi et al. (33) reported that the rate of change in the Emax after two cycles of NAC was higher in responders than non-responders (P<0.05). Jia et al. (42) showed that the Emax was higher than the Emean, while Singla et al. (43) showed that the Emax was similar to the Emean. Due to the limited sample size, it was not possible to determine which modulus of elasticity was more meaningful at this time.

Reduced elastic V was also shown to be an early predictor of the pathological response in two other studies in this trial (27,34). In the heterogeneity analysis, the heterogeneity of SWE was not high for either Sen or Spe, indicating a high degree of consistency between the included studies and the reliability and stability of the results obtained. However, we also performed subgroup analyses and found that the diagnostic performance of the elastic modulus was higher than that of the elastic V (0.84 vs. 0.72). The elastic modulus is assessed by measuring the change in stiffness of tumor tissue before and after chemotherapy. This measurement reflects that the tumor’s response to the chemotherapy drugs (i.e., its reduction in hardness) is often associated with a reduction in tumor cells and a softening of the tissue (44). Although V assessment is also a parameter in SWE, it reflects the propagation properties of sound waves in tissue rather than tissue stiffness or chemotherapy efficacy directly. There is a higher degree of objectivity, Sen, and repeatability when assessing Young’s modulus. It can more accurately reflect changes in the hardness of the tumor tissue and is not influenced by the operator’s subjective judgment. Therefore, this study concluded that the elastic modulus in SWE could be used to predict complete remission and partial remission, especially after the second cycle of NAC.

This meta-analysis incorporated studies from eight countries across Asia, Europe, North America, and Africa (China, India, Japan, Korea, the United States, the United Kingdom, Germany, and Egypt), representing diverse healthcare settings. Although the total number of included studies was modest (n=22), the multinational composition of the cohort enhances the external validity of our findings for populations with varying breast cancer management protocols. However, it also has some limitations. First, the search was limited to three databases (i.e., PubMed, Web of Science, and Embase) with language restrictions (English only), which might have constrained the sample size and introduced potential selection bias via the exclusion of non-English evidence. Second, no restrictions were imposed on ultrasound device selection to reflect real-world clinical practice, but inter-device variability might have introduced a measurement of heterogeneity. Third, this study retained heterogeneity in study designs (prospective/retrospective/unspecified) and response criteria (Miller-Payne/RCB) to comprehensively evaluate SWE and CEUS for assessing NAC response in breast cancer. While subgroup analyses showed no significant sensitivity differences, this approach may have increased between-study heterogeneity. Fourth, heterogeneity in the contrast agent protocols (types and dosages) across the CEUS studies might have introduced a potential measurement bias and confounded the diagnostic accuracy of the tumor response assessments. Thus, methodological standardization through expanded sample cohorts and harmonized study protocols is imperative to rigorously validate the diagnostic accuracy of SWE and CEUS in evaluating the pathological tumor response of breast cancer patients after NAC.

Conclusions

This was the first meta-analysis to systematically show that both SWE and CEUS have good value in predicting the pathological response of breast cancer patients to NAC. Future studies should further explore the possibility of their combined application and the optimization of diagnostic strategies to improve assessment accuracy. Meanwhile, high-quality studies need to be conducted to develop standardized tools to accurately assess the pathological response of breast cancer patients to NAC, ultimately guiding personalized therapeutic strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2730/rc

Funding: This work was supported by the Tianshan Young Talent Scientific and Technological Innovation Team: Innovative Team for Research on Prevention and Treatment of High-incidence Diseases in Central Asia (No. 2023TSYCTD0020), the National Natural Science Foundation of China (Nos. 82060318, 82460353, and 82260105), and the Corps Science and Technology Key Project (No. 2022CB002-04).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2730/coif). All authors report that this work was supported by the Tianshan Young Talent Scientific and Technological Innovation Team: Innovative Team for Research on Prevention and Treatment of High-incidence Diseases in Central Asia (No. 2023TSYCTD0020), the National Natural Science Foundation of China (Nos. 82060318, 82460353, and 82260105), and the Corps Science and Technology Key Project (No. 2022CB002-04). The authors have no other 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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