Contrast-Enhanced Ultrasound in Locating Axillary Sentinel Lymph Nodes in Patients with Breast Cancer: A Prospective Study

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.

Value of perfusion characteristics evaluated by CEUS combined with STQ parameters in diagnosing the properties of SLN in breast cancer

BACKGROUND

Accurate sentinel lymph node (SLN) characterization is essential for breast cancer management, prompting advancements in imaging technologies such as contrast-enhanced ultrasound (CEUS) and sound touch quantification (STQ) to enhance diagnostic precision.

OBJECTIVE

To explore the value of perfusion characteristics evaluated by CEUS combined with STQ parameters in diagnosing the properties of sentinel lymph node (SLN) in breast cancer.

METHODS

A total of 91 breast cancer patients (91 SLNs) admitted to the hospital from February 2022 to December 2023 were selected for this study. Among them, 26 patients with metastatic SLN confirmed by surgery and pathology were included in the metastatic SLN group, and 65 patients with non-metastatic SLN were included in the non-metastatic SLN group. Preoperative examination results of CEUS and STQ were retrospectively analyzed. The diagnostic efficacy of perfusion characteristics evaluated by CEUS and STQ parameters for the properties of SLN in breast cancer was analyzed using the receiver operating characteristics (ROC) curve. Statistical methods such as chi-square tests and logistic regression analysis were employed to analyze the data.

RESULTS

Enhancement patterns differed significantly between the metastatic SLN and non-metastatic SLN groups (p< 0.05). ROC curve analysis indicated that CEUS perfusion characteristics had an AUC value of 0.823 for diagnosing SLN properties, with a sensitivity of 84.62% and specificity of 70.77% using type I as the critical value. Additionally, STQ measurement showed significantly higher values in the metastatic SLN group (44.18 ± 6.53 kPa) compared to the non-metastatic SLN group (34.69 ± 6.81 kPa) (t= 6.075, p< 0.001). The AUC value for STQ parameters in diagnosing metastatic SLN was 0.849, with a sensitivity of 73.08% and specificity of 92.31% using 42.40 kPa as the critical value. Though the AUC value of STQ measurement was higher than CEUS perfusion characteristics alone, the difference was not statistically significant (Z= 0.393, p= 0.695). Moreover, combining CEUS perfusion characteristics with STQ parameters yielded an AUC value of 0.815 for diagnosing SLN properties, showing no significant difference compared to diagnosis with CEUS or STQ parameters alone (Z= 0.149, 0.516, p= 0.882, 0.606).

CONCLUSION

Combined use of perfusion characteristics evaluated by CEUS and STQ parameters can significantly improve the diagnostic specificity of SLN in breast cancer. It is worthy of clinical promotion.

Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography

ABSTRACT

The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

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.

The Value of Percutaneous Contrast-Enhanced Ultrasound in Sentinel Lymph Node Identification, Metastatic Status and Burden Diagnosis in Early Breast Cancer

OBJECTIVE

The aim of this study was to evaluate the value of percutaneous contrast-enhanced ultrasound (PCEUS) in the identification and characterization of sentinel lymph node (SLN).

METHODS

A total of 102 breast cancer patients were collected and underwent preoperative PCEUS, which was used to identify SLN and lymphatic drainage. SLNs were classified into 4 enhancement patterns, including 6 subtypes: homogeneous (I), featured inhomogeneous (II) including inhomogeneous hypoenhancement (IIa) and annular or semi-annular enhancement (IIb), focal filling defect (III) including filling defect area < 50% (IIIa) and filling defect area ≥ 50% (IIIb), and no enhancement (IV). The enhancement patterns of SLNs were compared with the final pathological diagnosis.

RESULTS

The identification rate of SLNs using PCEUS was 100% (102/102); the rate of identification of LCs was 100% (102/102), and the coincidence rate was 98.0% (100/102). Four lymphatic drainage patterns (LDPs) including 5 subtypes were found: single LC/single SLN(74.5%), multiple LCs/ single SLN (13.7%) including 2 subtypes:2 LCs/1 SLN and 3 LCs/1 SLN, single LC/multiple SLNs (7.8%), and multiple LCs/multiple SLNs (3.9%). A total of 86.3% (44/51) of patients without axillary metastasis could be safely selected for types I, IIa, and IIb, while the axillary metastasis rates of types III and IV were 74.4% and 87.5%, respectively (P < .001). Compared with grayscale US, the PCEUS significant improvement in diagnosing metastatic SLNs (.794 versus .579, P < .001). For the SLN metastatic burden, Types I, IIa, IIb, and IIIa had ≤2 SLNs metastases, with a pathological coincidence rate of (64/67, 95.5%), and types IIIb and IV had >2 SLNs metastases, with a pathological coincidence rate of (25/35, 71.4%) (P < .001). The AUC of PCEUS for the diagnosis of SLN metastatic status and burden was .794 and .879, respectively (P < .001).

CONCLUSION

PCEUS has a high identification rate for SLN and has good potential for diagnosing SLN metastatic status and burden by enhancement patterns.

Predictive value for axillary lymph node metastases in early breast cancer: Based on contrast-enhanced ultrasound characteristics of the primary lesion and sentinel lymph node

OBJECTIVE

To evaluate the value of contrast-enhanced ultrasound (CEUS) characteristics based on primary lesion combined with lymphatic contrast-enhanced ultrasound (LCEUS) patterns of SLN in predicting axillary lymph node metastasis (ALNM) with T1-2N0 breast cancer.

METHODS

A retrospective study was conducted in 118 patients with clinically confirmed T1-2N0 breast cancer. Conventional ultrasound (CUS) and CEUS characteristics of the primary lesion and enhancement patterns of SLN were recorded. The risk factors associated with ALNM were selected by univariate and binary logistic regression analysis, and the receiver operating characteristic (ROC) curve was drawn for the evaluation of predictive ALNM metastasis performance.

RESULTS

Univariate analysis showed that age, HER-2 status, tumor size, nutrient vessels, extended range of enhancement lesion, and the enhancement patterns of SLN were significant predictive features of ALNM. Further binary logistic regression analysis indicated that the extended range of enhancement lesion (p <  0.001) and the enhancement patterns of SLN (p <  0.001) were independent risk factors for ALNM. ROC analysis showed that the AUC of the combination of these two indicators for predicting ALNM was 0.931 (95% CI: 0.887-0.976, sensitivity: 75.0%, specificity: 99.8%).

CONCLUSION

The CEUS characteristics of primary lesion combined with enhancement patterns of SLN are highly valuable in predicting ALNM and can guide clinical axillary surgery decision-making in early breast cancer.

Sentinel Lymph Node Identification in Post Neoadjuvant Chemotherapy Breast Cancer Patients Undergoing Surgical Excision Using Lymphosonography

OBJECTIVES

This study evaluated the efficacy of lymphosonography in the identification of sentinel lymph nodes (SLNs) in post neoadjuvant chemotherapy patients with breast cancer scheduled to undergo surgical excision.

METHODS

Seventy-nine subjects scheduled for breast cancer surgery with SLN excision completed this IRB-approved study, out of which 18 (23%) underwent neoadjuvant chemotherapy before surgery. Subjects underwent percutaneous Sonazoid (GE Healthcare) injections around the tumor area for a total of 1.0 mL. Lymphosonography was performed using CPS on an S3000 HELX scanner (Siemens Healthineers) with a linear probe. Subjects received blue dye and radioactive tracer as part of their standard of care. Excised SLNs were classified as positive or negative for the presence of blue dye, radioactive tracer and Sonazoid. The results were compared between methods and pathology findings.

RESULTS

Seventy-two SLNs were surgically excised from 18 subjects, 29 were positive for blue dye, 63 were positive for radioactive tracer and 57 were positive for Sonazoid. Comparison with blue dye showed that both radioactive tracer and lymphosonography achieved an accuracy of 53% (P > .50). Comparison with radioactive tracer showed that blue dye had an accuracy of 53%, while lymphosonography achieved an accuracy of 67% (P < .01). Of the 72 SLNs, 15 were determined malignant by pathology; the detection rate was 47% for blue dye (7/15), 67% for radioactive tracer (10/15) and 100% for lymphosonography (15/15) (P < .001).

CONCLUSIONS

Lymphosonography achieved similar accuracy as radioactive tracer and higher accuracy than blue dye for identifying SLNs. The 15 SLNs positive for malignancy were all identified by lymphosonography.

Anatomy Versus Physiology: Is Breast Lymphatic Drainage to the Internal Thoracic (Internal Mammary) Lymphatic System Clinically Relevant?

Approximately 15%-25% of breast lymphatic drainage passes through the internal thoracic (internal mammary) lymphatic system, draining the inner quadrants of the breast. This study aimed to use lymphosonography to identify sentinel lymph nodes (SLNs) in the axillary and internal thoracic lymphatic systems in patients with breast cancer. Seventy-nine patients received subcutaneous ultrasound contrast agent injections around the tumor. Lymphosonography was used to identify SLNs. In 14 of the 79 patients (17.7%), the tumor was located in the inner quadrant of the breast. Lymphosonography identified 217 SLNs in 79 patients, averaging 2.7 SLNs per patient. The 217 identified SLNs in the 79 patients were located in the axillary lymphatic system; none were located in the internal thoracic (internal mammary) lymphatic system, although it was expected in two to four patients (i.e., 4-11 SLNs). These results implied that SLNs associated with breast cancer are predominantly located in the axillary lymphatic system.

Sentinel Lymph Node Identification in Patients With Breast Cancer Using Lymphosonography

The objective of the work described here was to evaluate the efficacy of lymphosonography in identifying sentinel lymph nodes (SLNs) in patients with breast cancer undergoing surgical excision. Of the 86 individuals enrolled, 79 completed this institutional review board-approved study. Participants received subcutaneous 1.0-mL injections of ultrasound contrast agent (UCA) around the tumor. An ultrasound scanner with contrast-enhanced ultrasound (CEUS) capabilities was used to identify SLNs. Participants were administered with blue dye and radioactive tracer to guide SLN excision as standard-of-care. Excised SLNs were classified as positive or negative for the presence of blue dye, radioactive tracer and UCA, and sent for pathology. Two hundred fifty-two SLNs were excised; 158 were positive for blue dye, 222 were positive for radioactive tracer and 223 were positive for UCA. Comparison with blue dye revealed accuracies of 96.2% for radioactive tracer and 99.4% for lymphosonography (p > 0.15). Relative to radioactive tracer, blue dye had an accuracy of 68.5%, and lymphosonography achieved 86.5% (p < 0.0001). Of 252 SLNs excised, 34 were determined to be malignant by pathology; 18 were positive for blue dye (detection rate = 53%), 23 for radioactive tracer (detection rate = 68%) and 34 for UCA (detection rate = 100%) (p < 0.0001). Lymphosonography was similar in accuracy to radioactive tracer and higher in accuracy than blue dye in identifying SLNs. All 34 malignant SLNs were identified by lymphosonography.

Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer

The incidence of cancer is increasing today, particularly lung and chest cancer. Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed. The goal of the proposed study is to investigate the value of automated breast volume scanning (ABUS) in conjunction with contrast-enhanced ultrasonography (CEUS) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (NAC) in treating the disease. For the research study, information on 98 patients who had NAC and surgery in the breast surgery department of the Shaanxi Provincial Cancer Hospital has been gathered. All patients have received four cycles of NAC and underwent conventional ultrasound (HUSS), CEUS, ABUS, and pathological examination. At the same time, receiver operating characteristic (ROC) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and NAC efficacy evaluation results. The study of this paper is totally based on the data collected from Shaanxi Provincial Cancer Hospital. The statistical and computational analyses are performed on the data collected for drawing inferences. When the findings are compared to the results of the pathological examination, HUSS has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of P < 0.05.ABUS combined with CEUS has shown no considerable differences in correlation study. Except for negative likelihood ratio, the diagnostic performance indexes of CEUS+ ABUS are substantially higher than HHUS with P < 0.05. ROC curve analysis is also performed which shows that CEUS and ABUS combination has higher precision in the analysis of breast cancer. ABUS pooled with CEUS shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients.

Modelling of magnetic microbubbles to evaluate contrast enhanced magnetomotive ultrasound in lymph nodes - a pre-clinical study

OBJECTIVES

Despite advances in MRI the detection and characterisation of lymph nodes in rectal cancer remains complex, especially when assessing the response to neoadjuvant treatment. An alternative approach is functional imaging, previously shown to aid characterisation of cancer tissues. We report proof of concept of the novel technique Contrast-Enhanced Magneto-Motive Ultrasound (CE-MMUS) to recover information relating to local perfusion and lymphatic drainage, and interrogate tissue mechanical properties through magnetically induced deformations.

METHODS

The feasibility of the proposed application was explored using a combination of experimental animal and phantom ultrasound imaging, along with finite element analysis. First, contrast-enhanced ultrasound imaging on one wild type mouse recorded lymphatic drainage of magnetic microbubbles after bolus injection. Second, tissue phantoms were imaged using MMUS to illustrate the force- and elasticity dependence of the magnetomotion. Third, the magnetomechanical interactions of a magnetic microbubble with an elastic solid were simulated using finite element software.

RESULTS

Accumulation of magnetic microbubbles in the inguinal lymph node was verified using contrast enhanced ultrasound, with peak enhancement occurring 3.7 s post-injection. The magnetic microbubble gave rise to displacements depending on force, elasticity, and bubble radius, indicating an inverse relation between displacement and the latter two.

CONCLUSION

Combining magnetic microbubbles with MMUS could harness the advantages of both techniques, to provide perfusion information, robust lymph node delineation and characterisation based on mechanical properties.

ADVANCES IN KNOWLEDGE

(a) Lymphatic drainage of magnetic microbubbles visualised using contrast-enhanced ultrasound imaging and (b) magnetomechanical interactions between such bubbles and surrounding tissue could both contribute to (c) robust detection and characterisation of lymph nodes.

Contrast enhanced magneto-motive ultrasound in lymph nodes - modelling and pre-clinical imaging using magnetic microbubbles

Despite advances in MRI, the detection and characterisation of lymph nodes in rectal cancer remains complex, especially when assessing the response to neo-adjuvant treatment. An alternative approach is functional imaging, previously shown to aid characterization of cancer tissues. We report proof-of-concept of the novel technique Contrast-Enhanced Magneto-Motive Ultrasound (CE-MMUS) to recover information relating to local perfusion and lymphatic drainage, and interrogate tissue mechanical properties through magnetically induced tissue deformations. The feasibility of the proposed application was explored using a combination of pre-clinical ultrasound imaging and finite element analysis. First, contrast enhanced ultrasound imaging on one wild type mouse recorded lymphatic drainage of magnetic microbubbles after bolus injection. Second, preliminary CE-MMUS data were acquired as a proof of concept. Third, the magneto-mechanical interactions of a magnetic microbubble with an elastic solid were simulated using finite element software. Accumulation of magnetic microbubbles in the inguinal lymph node was verified using contrast enhanced ultrasound, with peak enhancement occurring 3.7 s post-injection. Preliminary CE-MMUS indicates the presence of magnetic contrast agent in the lymph node. The finite element analysis explores how the magnetic force is transferred to motion of the solid, which depends on elasticity and bubble radius, indicating an inverse relation with displacement. Combining magnetic microbubbles with MMUS could harness the advantages of both techniques, to provide perfusion information, robust lymph node delineation and characterisation based on mechanical properties. Clinical Relevance- Robust detection and characterisation of lymph nodes could be aided by visualising lymphatic drainage of magnetic microbubbles using contrast enhanced ultrasound imaging and magneto-motion, which is dependent on tissue mechanical properties.

Development of Preclinical Ultrasound Imaging Techniques to Identify and Image Sentinel Lymph Nodes in a Cancerous Animal Model

Simple Summary

Bowel cancer is the fourth most common cancer in the UK. Treatment is dominated by major surgery because current imaging modalities cannot accurately determine lymph node involvement or vascular invasion. Although potentially curative, surgery carries a high risk of short- and long-term morbidity, including stoma formation. Optimized pre-treatment imaging would decrease the number of bowel cancer patients requiring major surgery. Such imaging would also be equally applicable to other cancers where local resection could significantly improve patient quality of life without compromising long-term outcomes (e.g., melanoma, head and neck cancers, gastro-esophageal, bladder). In this study, we created two mouse models (tumor and control) and used the resolution of high-frequency ultrasound imaging and parameters calculated from dynamically contrast-enhanced ultrasound to predict the likelihood of draining lymph nodes to be involved in the disease.

Abstract

Lymph nodes (LNs) are believed to be the first organs targeted by colorectal cancer cells detached from a primary solid tumor because of their role in draining interstitial fluids. Better detection and assessment of these organs have the potential to help clinicians in stratification and designing optimal design of oncological treatments for each patient. Whilst highly valuable for the detection of primary tumors, CT and MRI remain limited for the characterization of LNs. B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) can improve the detection of LNs and could provide critical complementary information to MRI and CT scans; however, the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) guidelines advise that further evidence is required before US or CEUS can be recommended for clinical use. Moreover, knowledge of the lymphatic system and LNs is relatively limited, especially in preclinical models. In this pilot study, we have created a mouse model of metastatic cancer and utilized 3D high-frequency ultrasound to assess the volume, shape, and absence of hilum, along with CEUS to assess the flow dynamics of tumor-free and tumor-bearing LNs in vivo. The aforementioned parameters were used to create a scoring system to predict the likelihood of a disease-involved LN before establishing post-mortem diagnosis with histopathology. Preliminary results suggest that a sum score of parameters may provide a more accurate diagnosis than the LN size, the single parameter currently used to predict the involvement of an LN in disease.