Adding contrast-enhanced ultrasound markers to conventional axillary ultrasound improves specificity for predicting axillary lymph node metastasis in patients with breast cancer

Developing and Evaluating a Nomogram Model Predicting Axillary Lymph Node Metastasis of Triple-Negative Breast Cancer Based on Multimodal Imaging Characteristics

RATIONALE AND OBJECTIVES

Breast cancer is the most frequently diagnosed cancer among women worldwide, with axillary lymph nodes being common sites of metastasis, particularly triple-negative breast cancer (TNBC), which is the subtype with the poorest prognosis. This study aimed to develop a nomogram model to predict axillary lymph node metastasis (ALNM) in TNBC patients based on mammography (MG), multimodal ultrasound (US), and clinical pathological characteristics.

PATIENTS AND METHODS

A retrospective study was performed on 291 patients diagnosed with TNBC from two centers. Patients from the Center 1 were randomly divided into a training cohort (n = 159) and a internal test cohort (n = 68) using a 7:3 ratio, while patients from the Center 2 served as an external test cohort. Each group was further divided into an ALNM group and a non-ALNM group based on the presence or absence of ALNM. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic analysis. The predictive performance of the nomogram model was evaluated by the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

Notable predictors included MG_reported_margin, MG_reported_suspicious malignant calcifications, MG_reported_abnormal ALN, elastography score, and US_reported_abnormal ALN. The area under the receiver operating characteristics curve (AUC) value of the nomogram model was 0.931 (95%CI: 0.890-0.973) for the training cohort, AUC=0.929 (95%CI: 0.871-0.986) for the internal test cohort and AUC=0.891 (95%CI: 0.794-0.987) for the external test cohort. Calibration curves and DCA both suggested that the nomogram exhibited favorable calibration and clinical utility.

CONCLUSION

The predictive model combined with multimodal US and MG characteristics developed in this study is highly accurate, serves as a powerful tool for clinical assessment, and shows promise for predicting ALNM in patients with TNBC.

Application of Contrast-Enhanced Ultrasound Parameters of Metastatic Axillary Lymph Nodes in Breast Cancer Patients in Predicting the Efficacy of Neoadjuvant Chemotherapy in Early Stage

BACKGROUND

To investigate the performance of contrast-enhanced ultrasound(CEUS) parameters of metastatic axillary lymph nodes (ALNs) before and after two courses of neoadjuvant chemotherapy (NAC) in breast cancer patients in predicting the efficacy of NAC.

METHODS

A total of 41 postoperative breast cancer patients were selected. All patients underwent NAC, and ALN biopsy was positive before chemotherapy. Metastatic ALN was examined by CEUS before and after two courses of NAC. The CEUS parameters of metastatic ALNs before and after two courses of NAC were analyzed to determine the performance of CEUS parameters in predicting the efficacy of NAC in early stage.

RESULTS

The NAC was effective for 28 cases and ineffective for 13 cases. There were no statistically significant differences in the CEUS parameters between effective NAC and ineffective NAC individuals before and after two courses of NAC. But, there were statistically significant differences in long diameter (LD), short diameter (SD), Peak intensity (Peak%) and area under the curve (AUC) between the effective and ineffective NAC patients after two courses of NAC. Receiver operating characteristic curve (ROC) analysis suggested the drop-out value of LD, SD, Peak% and AUC after two courses of NAC can be used as important indicators to evaluate the efficacy of NAC (p < 0.05).

CONCLUSIONS

CEUS parameters of metastatic axillary lymph nodes (ALNs) before and after two courses of neoadjuvant chemotherapy (NAC) in breast cancer patients can predict the efficacy of NAC in early stage.

Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography

Contrast-enhanced ultrasound (CEUS) plays a pivotal role in the diagnosis of primary breast cancer and in axillary lymph node (ALN) metastasis. However, the imaging features that are clinically crucial for lymph node metastasis have not been fully elucidated. Hence, we developed a bimodal model to predict ALN metastasis in patients with early breast cancer by integrating CEUS images with the annotated imaging features. The model adopted a light-gradient boosting machine to produce feature importance, enabling the extraction of clinically crucial imaging features. In this retrospective study, the diagnostic performance of the model was investigated using 788 CEUS images of ALNs obtained from 788 patients who underwent breast surgery between 2013 and 2021, with the ground truth defined by the pathological diagnosis. The results indicated that the test cohort had an area under the receiver operating characteristic curve (AUC) value of 0.93 (95% confidence interval: 0.88, 0.98). The model had an accuracy of 0.93, which was higher than the radiologist's diagnosis (accuracy of 0.85). The most important imaging features were heterogeneous enhancement, diffuse cortical thickening, and eccentric cortical thickening. Our model has an excellent diagnostic performance, and the extracted imaging features could be crucial for confirming ALN metastasis in clinical settings.

Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study

Background

Axillary lymph node (ALN) is the most common metastasis path for breast cancer, and ALN dissection directly affects the postoperative staging and prognosis of breast cancer patients. Therefore, additional research is needed to accurately predict ALN metastasis before surgery and construct predictive models to assist in surgical decision-making and optimize patient care.

Methods

We retrospectively analyzed the clinical data, radiomics, and pathomics of the patients diagnosed with breast cancer in the Breast Cancer Center of Hubei Cancer Hospital from January 2017 to December 2022. The study participants were randomly assigned to either the training queue (70%) or the validation queue (30%). Logistic regression (ie generalized linear regression model [GLRM]) and random forest model (RFM) were used to construct an ALN prediction model in the training queue, and the discriminant power of the model was evaluated using area under curve (AUC) and decision curve analysis (DCA). Meanwhile, the validation queue was used to evaluate the ALN prediction performance of the constructed model.

Results

Out of the 422 patients encompassed in the study, 18.7% were diagnosed with ALN by postoperative pathology. The logical model included shear wave elastography (SWE) related to maximum, minimum, centre, ratio 1, pathomics (Feature 1, Feature 3, and Feature 5) and a nomogram of the GLRM was drawn. The AUC of GLRM was 0.818 (95% CI: 0.757~0.879), significantly lower than that of RFM's AUC 0.893 (95% CI: 0.836~0.950).

Conclusion

The prediction models based on machine learning (ML) algorithms and multiomics have shown good performance in predicting ALN metastasis, and RFM shows greater advantages compared to traditional GLRM. The findings of this study can help clinicians identify patients with higher risk of ALN metastasis and provide personalized perioperative management to assist preoperative decision-making and improve patient prognosis.

Meta-analysis of contrast-enhanced ultrasound in differential diagnosis of breast adenosis and breast cancer

This systematic review and meta-analysis study aimed to determine the total capacity of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of breast lesions and breast cancer. For collecting papers, four groups of keywords were searched in five databases. The required information was extracted from the selected papers. In addition to the descriptive findings, a meta-analysis was also conducted. Thirty-three of thirty-six studies (91.67%) on the differential diagnosis of various degrees and types of breast lesions showed that CEUS has proper performance. The pooled values related to the sensitivity and specificity of CEUS were computed by 88.00 and 76.17.

Utilizing the Postvascular Phase of Contrast-Enhanced Ultrasound to Predict Breast Cancer Lymph Node Metastasis

Background and Objectives: To evaluate the value of the postvascular phase of contrast-enhanced ultrasound (CEUS) in differentiating between benign and metastatic lymph nodes (LNs) in patients with breast cancer (BC). Materials and Methods: This study retrospectively analyzed 96 suspicious LNs in the lymphatic drainage area of the breast from 90 patients with BC. All LNs were assessed by conventional ultrasound (US) and CEUS following intravenous Sonazoid injection. All LNs underwent puncture biopsy, and pathological results were obtained. The correlations between US and CEUS indicators of LNs and LN metastasis (LNM) were analyzed. Results: Of the 96 LNs, 66 were metastatic. Overall, 80.00% (24/30) of the benign LNs exhibited relative hyper-enhancement in the postvascular phase, whereas 96.97% (64/66) of the metastatic LNs exhibited relative hypo-enhancement (p < 0.001). This CEUS finding was highly predictive of metastasis, with a sensitivity of 96.97%, specificity of 80.00%, positive predictive value of 91.43%, negative predictive value of 92.31%, and accuracy of 91.67%. The mean postvascular phase intensity (MPI) was significantly lower for malignant (median MPI, 12 dB) than for benign (median MPI, 75 dB) LNs. The postvascular phase was more sensitive, specific, and accurate than conventional US or the vascular phase of CEUS for the diagnosis of LNM, with an area under the curve of 0.95 (95% confidence interval: 0.89-0.99). Conclusions: Qualitative and quantitative indicators of the postvascular phase of CEUS provide a reliable diagnostic approach to differentiate benign and metastatic LNs in patients with BC.

Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions

Advances in various imaging modalities for breast lesions have improved diagnostic capabilities not only for tumors but also for non-tumorous lesions. Contrast-enhanced ultrasound (CEUS) plays a crucial role not only in the differential diagnosis of breast lesions, identification of sentinel lymph nodes, and diagnosis of lymph node metastasis but also in assessing the therapeutic effects of neoadjuvant chemotherapy (NAC). In CEUS, two image interpretation approaches, i.e., qualitative analysis and quantitative analysis, are employed and applied in various clinical settings. In this paper, we review CEUS for breast lesions, including its various applications.

Prospects of perfusion contrast-enhanced ultrasound (CE-US) in diagnosing axillary lymph node metastases in breast cancer: a comparison with lymphatic CE-US

Accurate diagnosis of lymph node (LN) metastasis is vital for prognosis and treatment in patients with breast cancer. Imaging 1modalities such as ultrasound (US), MRI, CT, and 18F-FDG PET/CT are used for preoperative assessment. While conventional US is commonly recommended due to its resolution and sensitivity, it has limitations such as operator subjectivity and difficulty detecting small metastases. This review shows the microanatomy of axillary LNs to enhance accurate diagnosis and the characteristics of contrast-enhanced US (CE-US), which utilizes intravascular microbubble contrast agents, making it ideal for vascular imaging. A significant focus of this review is on distinguishing between two types of CE-US techniques for axillary LN evaluation: perfusion CE-US and lymphatic CE-US. Perfusion CE-US is used to assess LN metastasis via transvenous contrast agent administration, while lymphatic CE-US is used to identify sentinel LNs and diagnose LN metastasis through percutaneous contrast agent administration. This review also highlights the need for future research to clarify the distinction between studies involving "apparently enlarged LNs" and "clinical node-negative" cases in perfusion CE-US research. Such research standardization is essential to ensure accurate diagnostic performance in various clinical studies. Future studies should aim to standardize CE-US methods for improved LN metastasis diagnosis, not only in breast cancer but also across various malignancies.

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients.

METHODS

From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance.

RESULTS

Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM.

CONCLUSION

The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Determination of axillary lymph node status by fine-needle aspiration: preoperative axillary staging vs. postoperative surveillance for breast cancers

Background

Despite the common use of ultrasound (US)-guided fine-needle aspiration (FNA) for axillary node (AN) in breast cancer patients, only a limited number of studies are available regarding the diagnostic performance of AN-FNA according to the suspicion level based on US findings. This study compares the outcomes of US-guided AN-FNA in breast cancer patients, differentiating between those undergoing staging and surveillance.

Methods

A cross-sectional retrospective study with retrospective analysis was conducted on 767 consecutive AN-FNA procedures performed in 2017 at Samsung Medical Center in Seoul, with 654 for staging and 113 for surveillance in breast cancer patients. The radiologists performed axillary US and the specific finding was prospectively classified into the AN-reporting and data system (AN-RADS) category 3-5 before FNA. The malignancy rate of each category was evaluated. The chi-square test, with or without Bonferroni correction, or Fisher's exact test was used to compare the malignancy rates between the staging and surveillance groups for each category.

Results

Among the 767 AN-FNAs, 424 (55.3%) were malignant. The malignancy rate was significantly higher in the staging group (59.5%) than in the surveillance group (31.0%, P<0.0001). The distribution of AN-RADS categories differed between the groups (P=0.015), with 4A being the most common. The malignancy rates in categories 3, 4A, 4B, 4C, and 5 were as follows: 5.6%, 36.0%, 77.4%, 87.7%, and 98.4% in the staging group, and 0.0%, 9.7%, 53.3%, 88.9%, and 100% in the surveillance group. The malignancy rate was significantly different between the two groups only in category 4A (P=0.0001).

Conclusions

AN-FNA according to AN-RADS category appears to be an appropriate method for determination of axillary nodal status. Overall malignancy rate of AN-FNA in breast cancer patients was higher in the staging group than in the surveillance group. According to the suspicion level, the difference between two groups was significant only in category 4A.

Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer

Primary breast cancer is the most common malignant tumor in women worldwide, and axillary lymph node metastasis (ALNM) is an important marker of disease progression in patients with breast cancer. The objective of the present study was to analyze the association between contrast-enhanced ultrasound (CEUS) features and ALNM in primary breast cancer and its predictive value. A total of 120 patients with breast cancer were assigned to the non-metastatic group (n=70) and metastatic group (n=50). The factors influencing ALNM were explored by multivariate logistic regression analysis. The consistency of CEUS, ordinary ultrasonography and pathological examination in the diagnosis of the ALNM of breast cancer was evaluated by consistency testing. The sensitivity, specificity and consistency rate of CEUS features and ordinary ultrasonography were analyzed by receiver operating characteristic curve and four-fold table analyses. High enhancement amplitude, centripetal enhancement sequence, increased maximum cortical thickness, high peak intensity and a larger area under the curve of lymph nodes were more commonly found in the metastatic group than in the non-metastatic group. The lymph node aspect ratio and time to peak were lower in the metastatic group than the non-metastatic group. The time to peak was a protective factor for ALNM in patients with breast cancer. The sensitivity, specificity and coincidence rate with pathological examination of CEUS in the diagnosis of ALNM were 92.00, 90.00 and 90.83%, while these of ordinary ultrasonography were 76.00, 80.00 and 78.33%, respectively. The consistency test indicated that CEUS and pathological examination were consistent in the diagnosis of ALNM in patients with breast cancer, with a κ value of 0.816, indicating a good consistency. The κ value of ordinary ultrasonography and pathological examination was 0.763, also indicating a good consistency. However, these results indicate that CEUS is more valuable than ordinary ultrasonography in the diagnosis of ALNM in cases of breast cancer. In conclusion, the present study indicates that CEUS features were influencing factors associated with ALNM in patients with breast cancer and may serve as an important reference for the preoperative prediction of ALNM in breast cancer.

Contrast-enhanced ultrasound-guided biopsy of suspicious breast lesions on contrast-enhanced mammography and contrast-enhanced MRI: a case series

PURPOSE

To assess the effectiveness of contrast-enhanced ultrasound (CEUS) in guiding biopsies of breast lesions that were detected on contrast-enhanced mammography (CEM) or contrast-enhanced breast MRI (CE-MRI) but were not clearly visible on B-mode ultrasound (B-US).

METHODS

In this study, 23 lesions in 16 patients were selected for CEUS-guided biopsy due to poor visualization on B-US despite being detected on CEM (n = 20) or CE-MRI (n = 3). B-US, color Doppler ultrasound (CDUS), and CEUS were used to visualize the suspicious lesions, followed by a CEUS-guided core needle biopsy using Sonazoid as the contrast agent. The accuracy of the biopsy was assessed based on pathology-radiology concordance and 12-month imaging follow-up. The conspicuity scores for lesion visualization were evaluated using a 5-point conspicuity scale agreed upon by two breast radiologists.

RESULTS

The enhancing lesions detected on CEM/CE-MRI had an average size of 1.6 ± 1.3 cm and appeared as mass-enhancing (61%) or non-mass-enhancing (39%). The lesions had mean conspicuity scores of 2.30 on B-US, 2.78 on CDUS, and 4.61 on CEUS, with 96% of the lesions showing contrast enhancement on CEUS. CEUS-guided biopsy showed increased visibility in 96% and 91% of the lesions compared to B-US and CDUS, respectively. The overall accuracy of CEUS-guided biopsy was 100% based on concordance with histology and 12-month follow-up.

CONCLUSIONS

CEUS enhances the visibility of suspicious CEM/CE-MRI lesions that are poorly visible on B-US during biopsy procedures.

Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions

PURPOSE

To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4.

METHODS

A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves.

RESULTS

On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test.

CONCLUSION

CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.

Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study

OBJECTIVE

The aim of the work described here was to develop a non-invasive tool based on the radiomics and ultrasound features of automated breast volume scanning (ABVS), clinicopathological factors and serological indicators to evaluate axillary lymph node metastasis (ALNM) in patients with early invasive breast cancer (EIBC).

METHODS

We retrospectively analyzed 179 ABVS images of patients with EIBC at a single center from January 2016 to April 2022 and divided the patients into training and validation sets (ratio 8:2). Additionally, 97 ABVS images of patients with EIBC from a second center were enrolled as the test set. The radiomics signature was established with the least absolute shrinkage and selection operator. Significant ALNM predictors were screened using univariate logistic regression analysis and further combined to construct a nomogram using the multivariate logistic regression model. The receiver operating characteristic curve assessed the nomogram's predictive performance.

DISCUSSION

The constructed radiomics nomogram model, including ABVS radiomics signature, ultrasound assessment of axillary lymph node (ALN) status, convergence sign and erythrocyte distribution width (standard deviation), achieved moderate predictive performance for risk probability evaluation of ALNs in patients with EIBC. Compared with ultrasound, the nomogram model was able to provide a risk probability evaluation tool not only for the ALNs with positive ultrasound features but also for micrometastatic ALNs (generally without positive ultrasound features), which benefited from the radiomics analysis of multi-sourced data of patients with EIBC.

CONCLUSION

This ABVS-based radiomics nomogram model is a pre-operative, non-invasive and visualized tool that can help clinicians choose rational diagnostic and therapeutic protocols for ALNM.

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

Background

Axillary lymph node dissection (ALND) can be safely avoided in women with T1 or T2 primary invasive breast cancer (BC) and one to two metastatic sentinel lymph nodes (SLNs). However, cancellation of ALND based solely on SLN biopsy (SLNB) may lead to adverse outcomes. Therefore, preoperative assessment of LN tumor burden becomes a new focus for ALN status.

Objective

This study aimed to develop and validate a nomogram incorporating the radiomics score (rad-score) based on automated breast ultrasound system (ABUS) and other clinicopathological features for evaluating the ALN status in patients with early-stage BC preoperatively.

Methods

Totally 354 and 163 patients constituted the training and validation cohorts. They were divided into ALN low burden (<3 metastatic LNs) and high burden (≥3 metastatic LNs) based on the histopathological diagnosis. The radiomics features of the segmented breast tumor in ABUS images were extracted and selected to generate the rad-score of each patient. These rad-scores, along with the ALN burden predictors identified from the clinicopathologic characteristics, were included in the multivariate analysis to establish a nomogram. It was further evaluated in the training and validation cohorts.

Results

High ALN burdens accounted for 11.2% and 10.8% in the training and validation cohorts. The rad-score for each patient was developed based on 7 radiomics features extracted from the ABUS images. The radiomics nomogram was built with the rad-score, tumor size, US-reported LN status, and ABUS retraction phenomenon. It achieved better predictive efficacy than the nomogram without the rad-score and exhibited favorable discrimination, calibration and clinical utility in both cohorts.

Conclusion

We developed an ABUS-based radiomics nomogram for the preoperative prediction of ALN burden in BC patients. It would be utilized for the identification of patients with low ALN burden if further validated, which contributed to appropriate axillary treatment and might avoid unnecessary ALND.

Diagnostic value of multiple ultrasound diagnostic techniques for axillary lymph node metastases in breast cancer: A systematic analysis and network meta-analysis

Background

Early diagnosis of axillary lymph node metastasis is very important for the recurrence and prognosis of breast cancer. Currently, Lymph node biopsy is one of the important methods to detect lymph node metastasis in breast cancer, however, its invasiveness might bring complications to patients. Therefore, this study investigated the diagnostic performance of multiple ultrasound diagnostic methods for axillary lymph node metastasis of breast cancer.

Materials and methods

In this study, we searched PubMed, Web of Science, CNKI and Wan Fang databases, conducted Bayesian network meta-analysis (NMA) on the studies that met the inclusion criteria, and evaluated the consistency of five different ultrasound imaging techniques in axillary lymph node metastasis of breast cancer. Funnel graph was used to evaluate whether it had publication bias. The diagnostic performance of each ultrasound imaging method was ranked using SUCRA.

Results

A total of 22 papers were included, US+CEUS showed the highest SUCRA values in terms of sensitivity (SEN) (0.874), specificity (SPE) (0.911), positive predictive value (PPV) (0.972), negative predictive value (NPV) (0.872) and accuracy (ACC) (0.990).

Conclusion

In axillary lymph node metastasis of breast cancer, the US+CEUS combined diagnostic method showed the highest SUCRA value among the five ultrasound diagnostic methods. This study provides a theoretical basis for preoperative noninvasive evaluation of axillary lymph node metastases in breast cancer patients and clinical treatment decisions.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022351977.

Evaluation of diagnostic efficacy of multimode ultrasound in BI-RADS 4 breast neoplasms and establishment of a predictive model

Objectives

To explore the diagnostic efficacy of ultrasound (US), two-dimensional and three-dimensional shear-wave elastography (2D-SWE and 3D-SWE), and contrast-enhanced ultrasound (CEUS) in breast neoplasms in category 4 based on the Breast Imaging Reporting and Data System (BI-RADS) from the American College of Radiology (ACR) and to develop a risk-prediction nomogram based on the optimal combination to provide a reference for the clinical management of BI-RADS 4 breast neoplasms.

Methods

From September 2021 to April 2022, a total of 104 breast neoplasms categorized as BI-RADS 4 by US were included in this prospective study. There were 78 breast neoplasms randomly assigned to the training cohort; the area under the receiver-operating characteristic curve (AUC), 95% confidence interval (95% CI), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 2D-SWE, 3D-SWE, CEUS, and their combination were analyzed and compared. The optimal combination was selected to develop a risk-prediction nomogram. The performance of the nomogram was assessed by a validation cohort of 26 neoplasms.

Results

Of the 78 neoplasms in the training cohort, 16 were malignant and 62 were benign. Among the 26 neoplasms in the validation cohort, 6 were malignant and 20 were benign. The AUC values of 2D-SWE, 3D-SWE, and CEUS were not significantly different. After a comparison of the different combinations, 2D-SWE+CEUS showed the optimal performance. Least absolute shrinkage and selection operator (LASSO) regression was used to filter the variables in this combination, and the variables included Emax, Eratio, enhancement mode, perfusion defect, and area ratio. Then, a risk-prediction nomogram with BI-RADS was built. The performance of the nomogram was better than that of the radiologists in the training cohort (AUC: 0.974 vs. 0.863). In the validation cohort, there was no significant difference in diagnostic accuracy between the nomogram and the experienced radiologists (AUC: 0.946 vs. 0.842).

Conclusions

US, 2D-SWE, 3D-SWE, CEUS, and their combination could improve the diagnostic efficiency of BI-RADS 4 breast neoplasms. The diagnostic efficacy of US+3D-SWE was not better than US+2D-SWE. US+2D-SWE+CEUS showed the optimal diagnostic performance. The nomogram based on US+2D-SWE+CEUS performs well.

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.

Clinical Value of Preoperative Ultrasound Signs in Evaluating Axillary Lymph Node Status in Triple-Negative Breast Cancer

Purpose. To explore the clinical value of preoperative ultrasound signs in evaluating axillary lymph node status in triple-negative breast cancer (TNBC). Methods. A retrospective study was conducted on 162 patients with TNBC who were admitted to our hospital from January 2017 to June 2021. A total of 62 patients with axillary lymph node metastasis and 100 patients with normal axillary lymph nodes were included. Univariate and logistic regression was used to analyze the correlation between clinicopathological parameters, ultrasound features, and axillary lymph node metastasis between these two groups. The receiver operating characteristic (ROC) curve of each index was drawn to predict positive axillary lymph node. Results. The lymph node positive rate was higher in patients with tumor size ( $2\text{mm}<T\le 5\text{mm}$ ) and tumor stage III, and the difference between these two groups was statistically significant ( $P<0.05$ ). The patients with $\text{cortical}\text{thickness}\ge 3$ , blood flow grades II-III, $\text{aspect}\text{ratio}\left(\text{L}/\text{S}\right)\ge 2$ , and $\text{RI}\ge 0.7$ had higher lymph node positive rate, and the difference between these two groups was statistically significant ( $P<0.05$ ). Other index shows no correlation with ancillary lymph node positive rate, or the correlation was not statistically significant ( $P>0.05$ ). Further regression analysis indicated that the blood flow grade and L/S of axillary lymph nodes were independent influencing factors of axillary lymph node metastasis in TNBC patients ( $P<0.05$ ). Relevant receiver operating characteristic (ROC) curves were constructed, and the AUC of axillary lymph node blood flow grade and L/S for predicting axillary lymph node status was 0.6329 and 0.6498, respectively. The AUC for the joint prediction of the two indicators is 0.6898. Conclusion. Ultrasound sign combined with clinicopathological characteristics can predict the axillary lymph nodes metastasis in TNBC, which could guide clinical decision of axillary lymph node surgery.

The Value of Shear Wave Elastography in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis and Its Correlation With Molecular Classification of Breast Masses

OBJECTIVE

To explore the diagnostic value of shear wave elastography examination (SWE) on axillary node metastasis (ANM) in breast cancer, this study aimed to evaluate the correlation between the SWE features and different molecular types of breast cancer, and to check the elastic modulus differences among the molecular types.

METHODS

Breast cancer patients from November 2020 to December 2021 were subjected to both conventional ultrasonic examination (CUE) and SWE before ultrasound-guided percutaneous biopsy or axillary lymph node dissection (ALND). We used the pathological results as the gold standard to draw the receiver operating characteristic (ROC) curve.

RESULTS

SWE outperforms CUE, but their conjunctive use is the best option. No significant correlation was found between the elastic modulus values and the molecular types of breast cancer.

CONCLUSION

SWE can be used as an routine auxiliary method of CUE for ANM.