Characteristics of contrast-enhanced ultrasonography and strain elastography of locally advanced breast cancer

Clinical Diagnostic Value of Shear-Wave Elastography in Detecting Malignant Nipple Retraction

OBJECTIVES

In recent years, the use of shear-wave elastography (SWE) as a diagnostic tool for detecting malignant breast lesions has shown promising results. This study aims to determine the clinical diagnostic value of SWE in detecting malignant nipple retraction.

METHODS

Both US and SWE (Philips EPIQ7 system) were performed for 41 consecutive patients with nipple retraction (56 nipples). The mean, median, and maximum tissue elasticity values (in kilopascals) were determined for each nipple by using SWE. The sensitivity, specificity, and overall accuracy of each measurement was determined by using the surgical pathology results or clinical diagnosis as the gold standard.

RESULTS

Of the 56 retracted nipples, 32 were due to benign lesions, and 24 were due to malignant lesions. No significant differences in dimensions or echo features were found between the benign and malignant groups. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the color Doppler flow imaging (CDFI) pattern were 63.89% (23/36), 95% (19/20), 95.83 (23/24), 59.38 (19/32), and 75% (42/56), respectively; the corresponding values for median elasticity on SWE were 88.46% (23/26), 96.67% (29/30), 95.83 (23/24), 90.63 (29/32), and 92.85 (52/56), respectively.

CONCLUSIONS

The addition of SWE to conventional US could help differentiate benign from malignant lesions associated with nipple retraction.

Advantages of contrast-enhanced ultrasound in the localization and diagnostics of sentinel lymph nodes in breast cancer

Sentinel lymph nodes (SLNs) are the first station of lymph nodes that extend from the breast tumor to the axillary lymphatic drainage. The pathological status of these LNs can predict that of the entire axillary lymph node. Therefore, the accurate identification of SLNs is necessary for sentinel lymph node biopsy (SLNB) to replace axillary lymph node dissection (ALND). The quality of life and prognosis of breast cancer patients are related to proper surgical treatment after the precise identification of SLNs. Some of the SLN tracers that have been identified include radioisotope, nano-carbon, indocyanine green (ICG), and methylene blue (MB). However, these tracers have certain limitations, such as pigmentation, radiation dangers, and the requirement for costly detection equipment. Ultrasound contrast agents (UCAs) have good specificity and sensitivity, and thus can compensate for some shortcomings of the mentioned tracers. This technique is also being applied to SLNB in patients with breast cancer, and can even provide an initial judgment on SLN status. Contrast-enhanced ultrasound (CEUS) has the advantages of high distinguishability, simple operation, no radiation harm, low cost, and accurate localization; therefore, it is expected to replace the traditional biopsy methods. In addition, it can significantly enhance the accuracy of SLN localization and shorten the operation time.

Predictive value of acoustic radiation force impulse imaging in breast cancer after neoadjuvant chemotherapy

To investigate the predictive value of acoustic radiation force impulse imaging for neoadjuvant chemotherapy in breast cancer. Seventy-eight breast cancer patients treated in our hospital from March 2019 to March 2022 were recruited. They received neoadjuvant chemotherapy and were examined by conventional ultrasound and acoustic radiation force impulse imaging before chemotherapy and after two cycles of chemotherapy. The lesion diameter, intralesional blood flow pulsatility index (PI), resistance index (RI), shear wave velocity (SWV), and change rate (Δlesion diameter, ΔPI, ΔRI, ΔSWV) were compared between the two groups before and after chemotherapy. The receiver operating characteristic curve was drawn to evaluate the predictive power of related parameters on the efficacy of neoadjuvant chemotherapy in breast cancer. After two cycles of neoadjuvant chemotherapy, according to the Miller-Payne grading, 57 cases (73.08%) with significant neoadjuvant chemotherapy response were classified as the response group, and 21 cases (26.92%) with non-significant response were classified as the non-response group. Before and after chemotherapy, the difference in lesion diameter, PI, RI, SWV, and change rate (Δlesion diameter, ΔPI, ΔRI, and ΔSWV) was statistically significant between the two groups (P < 0.05). The area under the curve of ΔSWV in predicting the efficacy of neoadjuvant chemotherapy 0.876 (95%CI 0.781 ~ 0.939) was significantly higher than that of Δlesion diameter 0.652 (95%CI 0.535 ~ 0.756), that of ΔPI 0.712 (95%CI 0.599 ~ 0.809), and that of ΔRI 0.678 (95%CI 0.563 ~ 0.780) (P < 0.05). The change rate of tissue stiffness has a relatively high predictive value for the effect of neoadjuvant chemotherapy in breast cancer.

The diagnostic value of contrast-enhanced ultrasonography in breast ductal abnormalities

BACKGROUND

Ductal lesions are an important, often overlooked, and poorly understood issue in breast imaging, which have a risk of underlying malignancy ranging from 5 to 23%. Ultrasonography (US), which has largely replaced galactography or ductography, has become an important imaging method to assess patients with ductal lesions. However, it is difficult to distinguish benign from malignant ductal abnormalities only by ultrasonography, most of which are recommended to be at least in subcategory 4A; these require biopsy according to the ACR BI-RADS®atlas 5th Edition-breast ultrasound. Contrast-enhanced ultrasound (CEUS) has been shown to be valuable for differentiating benign from malignant tumors, but its value is unclear in breast ductal lesions. Therefore, the purposes of this study were to explore the characteristics of malignant ductal abnormalities on US and CEUS imaging and the diagnostic value of CEUS in breast ductal abnormalities.

METHODS

Overall, 82 patients with 82 suspicious ductal lesions were recruited for this prospective study. They were divided into benign and malignant groups according to the pathological results. Morphologic features and quantitative parameters of US and CEUS were analyzed by comparison and multivariate logistic regression to determine the independent risk factors. The diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

Shape, margin, inner echo, size, microcalcification and blood flow classification on US, wash-in time, enhancement intensity, enhancement mode, enhancement scope, blood perfusion defects, peripheral high enhancement and boundary on CEUS were identified as features correlated with malignant ductal lesions. However, multivariate logistic regression showed that only microcalcification (OR = 8.96, P = 0.047) and enhancement scope (enlarged, OR = 27.42, P = 0.018) were independent risk factors for predicting malignant ductal lesions. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy and area under the ROC curve of microcalcifications combined with an enlarged enhancement scope were 0.895, 0.886, 0.872, 0.907, 0.890, and 0.92, respectively.

CONCLUSIONS

Microcalcification and enlarged enhancement scope are independent factors for predicting malignant ductal lesions. The combined diagnosis can greatly improve the diagnostic performance, indicating that CEUS can be useful in the differentiation of benign and malignant lesions to formulate more appropriate management for ductal lesions.

Contrast-enhanced ultrasonography for early prediction of response of neoadjuvant chemotherapy in breast cancer

Neoadjuvant chemotherapy (NAC) is widely accepted as a primary treatment for inoperable or locally advanced breast cancer before definitive surgery. However, not all advanced breast cancers are sensitive to NAC. Contrast-enhanced ultrasonography (CEUS) has been considered to assess tumor response to NAC as it can effectively reflect the condition of blood perfusion and lesion size. Therefore, this study aimed to evaluate the diagnostic performance of CEUS to predict early response in different regions of interest in breast tumors under NAC treatment. This prospective study included 82 patients with advanced breast cancer. Parameters of TIC (time-intensive curve) between baseline and after the first cycle of NAC were calculated for the rate of relative change (Δ), including Δpeak, ΔTTP (time to peak), ΔRBV (regional blood volume), ΔRBF (regional blood flow) and ΔMTT (mean transit time). The responders and non-responders were distinguished by the Miller-Payne Grading (MPG) system and parameters from different regions of tumors were compared in these two groups. For ROI 1(the greatest enhancement area in the central region of the tumor), there were significant differences in Δpeak1, ΔRBV1 and ΔRBF1 between responders and non-responders. For ROI 2 (the greatest enhancement area on edge of the tumor), there were significant differences in Δpeak2 and ΔRBF2 between the groups. The Δpeak1 and ΔRBF2 showed good prediction (AUC 0.798-0.820, p ≤ 0.02) after the first cycle of NAC. When the cut-off value was 0.115, the ΔRBF2 had the highest diagnostic accuracy and the maximum NPV. Quantitative TIC parameters could be effectively used to evaluate early response to NAC in advanced breast cancer.

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

OBJECTIVES

To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS).

MATERIALS AND METHODS

This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model.

RESULTS

Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively).

CONCLUSION

The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.

Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics

AIMS

We evaluated the performance of contrast-enhanced ultrasound (CEUS) based on radiomics analysis to distinguish benign from malignant breast masses.

METHODS

131 women with suspicious breast masses (BI-RADS 4a, 4b, or 4c) who underwent CEUS examinations (using intravenous injection of perflutren lipid microsphere or sulfur hexafluoride lipid-type A microspheres) prior to ultrasound-guided biopsies were retrospectively identified. Post biopsy pathology showed 115 benign and 16 malignant masses. From the cine clip of the CEUS exams obtained using the built-in GE scanner software, breast masses and adjacent normal tissue were then manually segmented using the ImageJ software. One frame representing each of the four phases: precontrast, early, peak, and delay enhancement were selected post segmentation from each CEUS clip. 112 radiomic metrics were extracted from each segmented tissue normalized breast mass using custom Matlab® code. Linear and nonlinear machine learning (ML) methods were used to build the prediction model to distinguish benign from malignant masses. tenfold cross-validation evaluated model performance. Area under the curve (AUC) was used to quantify prediction accuracy.

RESULTS

Univariate analysis found 35 (38.5%) radiomic variables with p < 0.05 in differentiating between benign from malignant masses. No feature selection was performed. Predictive models based on AdaBoost reported an AUC = 0.72 95% CI (0.56, 0.89), followed by Random Forest with an AUC = 0.71 95% CI (0.56, 0.87).

CONCLUSIONS

CEUS based texture metrics can distinguish between benign and malignant breast masses, which can, in turn, lead to reduced unnecessary breast biopsies.

Contrast-enhanced ultrasound of breast tumors: an initial experience

Background

The increase of neoadjuvant treatment for breast cancer creates a capacity challenge as response evaluation by magnetic resonance imaging (MRI) is a limited resource. Contrast-enhanced ultrasound (CEUS) has been proposed as an alternative imaging strategy.

Purpose

To get experience with examination of malignant breast tumors with CEUS and evaluate the potential for future use in response evaluation of neoadjuvant treatment.

Material and methods

In this pilot study, the dynamic contrast-enhancement of ultrasound and MRI examinations were analyzed in 14 women with histologically verified breast cancer.

Results

Analysis of the time intensity curve of CEUS demonstrated the difference between tumor and normal tissue. The peak intensity was five times higher in tumor tissue (mean increase 397%, 95% CI 250–545). The curve was steeper for tumor tissue (mean 1.76, 95% CI 1.26–2.26) than for normal tissue (mean 0.43, 95% CI 0.24–0.62).

Conclusion

CEUS is a feasible method of examining blood flow in malignant breast tumors.

A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer

The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.

A new search pattern for emergency breast exams: the clinical picture

Distinct breast diseases are readily diagnosed by clinical and ultrasound appearance that radiologists and sonographers may encounter in emergency room and urgent care patient presentations. While it may be impractical or impossible for the on-call emergency radiologist to examine a patient with breast complaints, radiologists can and should adopt the practice pattern to routinely seek out the clinical photographs in the patient's medical record while interpreting breast examinations. Imaging should be interpreted in the context of both the history and the physical findings. Sonographers play important roles in the documentation of visual inspection findings, in addition to performing high quality targeted ultrasound where applicable. This pictorial offers resources to emergency radiologists and sonographers that facilitate rapid accurate diagnosis of ten distinct breast diseases.

Early prediction of response to neoadjuvant chemotherapy using contrast-enhanced ultrasound in breast cancer

Early prediction of non-response is essential in order to avoid inefficient treatments. The objective of this study was to determine the contrast-enhanced ultrasound (CEUS) for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients.Between March 2018 and October 2019, 93 consecutive patients with histologically proven breast cancer scheduled for NAC were enrolled. Conventional ultrasound and CEUS imaging were performed before NAC and after two cycles of NAC. CEUS parameters were compared with pathologic response. Multiple logistic regression analyses were utilized to explore CEUS parameters to predict pCR, and receiver operating characteristic analysis was used to evaluate the predictive ability.Therapeutic response was obtained from 25 (27%) patients with pCR and 68 (73%) with non-pCR. Compared to non-pCR, pCR cases have a significantly higher proportion of homogeneous enhancement feature (56% vs 14%, P < .001) and centripetal enhancement (52% vs 23%, P = .012). A significant decrease in peak intensity (PI) was observed after two cycles of NAC. Compared with non-pCR patients, the kinetic parameters PI change (PI%) was higher in pCR patients (P < .001). Multiple logistic regression demonstrated two independent predictors of pCR: internal homogeneity (odds ratio, 4.85; 95% confidence interval: 1.20-19.65; P = .027) and PI% (odds ratio, 1.08; 95% confidence interval: 1.02-1.15; P = .007). In receiver operating characteristic curve analysis, internal homogeneity and PI%, with area under curve of 0.71 and 0.84, predicted pCR with sensitivity (56%, 95%) and specificity (85%, 70%), respectively.Internal homogeneity and PI% of CEUS may be useful in the noninvasive early prediction of pCR in patients with breast cancer.