Preoperative Axillary Lymph Node Evaluation in Breast Cancer: Current Issues and Literature Review

Distinguishing Axillary Lymphadenopathy after COVID-19 Vaccination from Malignant Lymphadenopathy

Objectives: To study the differences between malignant hypermetabolic axillary lymphadenopathy (MHL) and COVID-19 vaccine-associated axillary hypermetabolic lymphadenopathy (VAHL) using clinical imaging. Methods: A total of 1096 patients underwent Positron Emission Tomography-Computed Tomography (PET-CT) between 1 June 2021 and 30 April 2022 at Ehime University Hospital. In total, 188 patients with axillary lymphadenopathy after the COVID-19 vaccination were evaluated. The patients were classified into three groups such as VAHL (n = 27), MHL (n = 21), and equivocal hypermetabolic axillary lymphadenopathy (EqHL; n = 140). Differences in lymph node (LN) swellings were statistically analyzed using clinical imaging (echography, CT, and 18F-FDG PET). Results: MHL included a higher female population (90.5%) owing to a higher frequency of breast cancer (80.9%). Axillary LNs of MHL did not show any LN fatty hilums (0%); however, those of VAHL and EqHL did (15.8 and 36%, respectively). After the logistic regression analysis of the patients who had axillary lymphadenopathy without any LN fatty hilums, the minor axis length and ellipticity (minor axis/major axis) in the largest axillary LN, SUVmax, and Tissue-to-Background Ratio (TBR) were useful in distinguishing malignant lymphadenopathies. A receiver-operating characteristic (ROC) analysis indicated that a cut-off value of ≥7.3 mm for the axillary LN minor axis (sensitivity: 0.714, specificity: 0.684) and of ≥0.671 for ellipticity (0.667 and 0.773, respectively) in the largest LN with the highest SUVmax and TBR were predictive of MHL. Conclusions: Axillary lymphadenopathy of the minor axis and ellipticity in LN without fatty hilums may be useful to be suspicious for malignancy, even in patients who have received COVID-19 vaccination. Further examinations, such as 18F-FDG PET, are recommended for such patients.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis

OBJECTIVE

This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer.

METHODS

The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process.

RESULTS

Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer.

CONCLUSION

The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.

Multimodal ultrasound imaging: a method to improve the accuracy of sentinel lymph node diagnosis in breast cancer

Aim

This study assessed the utility of multimodal ultrasound in enhancing the accuracy of breast cancer sentinel lymph node (SLN) assessment and compared it with single-modality ultrasound.

Methods

Preoperative examinations, including two-dimensional ultrasound (2D US), intradermal contrast-enhanced ultrasound (CEUS), intravenous CEUS, shear-wave elastography (SWE), and surface localization, were conducted on 86 SLNs from breast cancer patients. The diagnostic performance of single and multimodal approaches for detecting metastatic SLNs was compared to postoperative pathological results.

Results

Among the 86 SLNs, 29 were pathologically diagnosed as metastatic, and 57 as non-metastatic. Single-modality ultrasounds had AUC values of 0.826 (intradermal CEUS), 0.705 (intravenous CEUS), 0.678 (2D US), and 0.677 (SWE), respectively. Intradermal CEUS significantly outperformed the other methods (p<0.05), while the remaining three methods had no statistically significant differences (p>0.05). Multimodal ultrasound, combining intradermal CEUS, intravenous CEUS, 2D US, and SWE, achieved an AUC of 0.893, with 86.21% sensitivity and 84.21% specificity. The DeLong test confirmed that multimodal ultrasound was significantly better than the four single-modal ultrasound methods (p<0.05). Decision curve analysis and clinical impact curves demonstrated the superior performance of multimodal ultrasound in identifying high-risk SLN patients.

Conclusion

Multimodal ultrasound improves breast cancer SLN identification and diagnostic accuracy.

Axilla View of Mammography in Preoperative Axillary Lymph Node Evaluation of Breast Cancer Patients: A Pilot Study

PURPOSE

This study aimed to explore a novel position of mammography named axilla view in axillary lymph node (ALN) evaluation in breast cancer.

PATIENTS AND METHODS

Patients were prospectively enrolled and scheduled for mammography before surgery. Investigated imaging patterns included mediolateral oblique (2D-MLO) and axilla view (2D-axilla) of mammography, and axilla view of digital breast tomosynthesis (3D-axilla). The correlation of ALN numbers between imaging and pathology was analyzed. Diagnostic performance was analyzed via AUC.

RESULTS

75 patients were included. A larger and clearer axillary region was displayed in axilla view. The total number of ALNs detected under 2D/3D-axilla view was significantly higher than that under 2D-MLO view (4.6 vs. 2.5, P < .001; 5.6 vs. 4.6, P = .034). Correlations between number of positive ALNs detected under 2D/3D-axilla view and pathologically confirmed metastatic ALNs were stronger than 2D-MLO view (Pearson correlation coefficients: 0.7084,0.7044 and 0.4744). The proportion of cases with ≥5 positive ALNs detected under 3D-axilla view was significantly higher than that under 2D-MLO (38.2% vs. 14.7%, P = .028). The overweight and obese group showed a higher AUC value than the underweight and lean group in ALN evaluation, although not significantly (2D-MLO: 0.7643 vs. 0.6458, P = .2656; 2D-axilla: 0.8083 vs. 0.6586, P = .1522; 3D-axilla: 0.8045 vs. 0.6615, P = .1874). This difference was more pronounced in axilla view.

CONCLUSION

Axilla view exhibited advantages over conventional MLO view in the extent of axilla displayed by mammography in breast cancer. Further studies with larger sample sizes are needed.

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.

A spatially resolved single-cell genomic atlas of the adult human breast

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

Detection of Axillary Lymph Node Involvement in Early-Stage Breast Cancer: Comparison between Staging 18F-2-Fluoro-2-Deoxy-D-Glucose Positron Emission Tomography-Computed Tomography Scans, Mammography, and Sentinel Lymph Node Biopsy

Aims

The aim of this study was to evaluate the role of 18F-2-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) scan in the detection of axillary lymph node (ALN) involvement and comparison with sentinel lymph node biopsy (SLNB) in operable early-stage breast cancer (EBC).

Settings and Design

It is a retrospective analysis of staging PET-CT scan of EBC.

Methods

A total of 128 patients with histopathologically proven breast cancer (BC) were included in the study. Preoperative mammography supplemented with ultrasonography and staging 18F-FDG PET-CT scan was done for all patients. Surgery was done within 30 (mean ± standard deviation = 13.8 ± 10.5) days of staging. SLNB was performed in patients without PET-positive ALNs. All patients with positive sentinel nodes and PET-positive ALNs underwent axillary lymph node dissection (ALND).

Statistical Analysis Used

The comparison between categorical variables was made by Chi-square/Fisher's exact test as applicable. For continuous variables comparisons, Student's t-test and one-way analysis of variance tests were used.

Results

Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PET-CT scan for detection of ALN involvement were 41.7%, 93.2%, 92.1%, and 45.6%, respectively. Sensitivity, specificity, PPV, and NPV of mammography were 84.5%, 54.5%, 78.0%, and 68.6%, respectively. Sixteen out of 46 (34.7%) patients with negative ALNs in PET-CT scan finally showed involvement in histopathology report after SLNB resulting in upstage of the disease. The size of tumor deposits in sentinel nodes was significantly smaller than PET-positive ALNs (P = 0.01). Our observations correlate with the results of earlier studies published in the literature.

Conclusions

18F-FDG PET-CT scan cannot substitute SLNB for ALN screening in EBC. The limitations are most marked in smaller and micrometastatic tumor deposits in ALNs and may be attributed to limitations of PET resolution. However, PET-positive nodes showed good specificity for disease involvement in our study. Therefore, ALND can safely be performed by omitting SLNB in such cases.

The accuracy of ultrasound-guided fine-needle aspiration and core needle biopsy in diagnosing axillary lymph nodes in women with breast cancer: a systematic review and meta-analysis

Background

This study evaluates the diagnostic accuracy of ultrasound-guided fine needle aspiration (US-FNA) and core needle biopsy (US-CNB) for detecting axillary lymph nodes in women with breast cancer.

Methods

Eligible studies and pertinent literature resources were identified in Cochrane, PubMed, Embase, CNKI, VIP, and Wanfang databases using subject-specific keywords. Study outcomes were tested for heterogeneity, and meta-analyses were performed to estimate sensitivity, specificity, and diagnostic odds ratios (DORs). The summary receiver operating characteristic (SROC) curve analysis was also performed.

Results

A total of 22 studies involving 3,548 patients were included to evaluate the diagnostic accuracy of US-FNA and 11 studies involving 758 patients were included to evaluate the diagnostic accuracy of US-CNB in identifying axillary lymph nodes in women with breast cancer. The accuracy of US-FNA in identifying suspicious axillary lymph nodes was as follows: overall sensitivity, 79% (95% CI: 73%-84%); global specificity, 96% (95% CI: 92%-98%); overall positive likelihood ratio, 18.55 (95% CI: 10.53-32.69); overall negative likelihood ratio, 0.22 (95% CI: 0.17-0.28); DOR, 71.68 (95% CI: 37.19-138.12); and the area under the SROC curve, 0.94 (95% CI: 0.92-0.96). The accuracy of US-CNB in identifying suspicious axillary lymph nodes was as follows: overall sensitivity, 85% (95% CI: 81%-89%); global specificity, 93% (95% CI: 87%-96%); overall positive likelihood ratio, 11.88 (95% CI: 6.56-21.50); overall negative likelihood ratio, 0.16 (95% CI: 0.12-0.21); overall DOR, 66.83 (95% CI: 33.28-134.21), and the area under SROC curve 0.96 (95% CI: 0.94-0.97).

Conclusions

The results indicate that both US-FNA and US-CNB have high accuracy for suspicious axillary lymph nodes.

A spatially resolved single cell genomic atlas of the adult human breast

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment

BACKGROUND

Due to recent changes in breast cancer treatment strategy, significantly more patients are treated with neoadjuvant systemic therapy (NST). Radiological methods do not precisely determine axillary lymph node status, with up to 30% of patients being misdiagnosed. Hence, supplementary methods for lymph node status assessment are needed. This study aimed to apply and evaluate machine learning models on clinicopathological data, with a focus on patients meeting NST criteria, for lymph node metastasis prediction.

METHODS

From the total breast cancer patient data (n = 8381), 719 patients were identified as eligible for NST. Machine learning models were applied for the NST-criteria group and the total study population. Model explainability was obtained by calculating Shapley values.

RESULTS

In the NST-criteria group, random forest achieved the highest performance (AUC: 0.793 [0.713, 0.865]), while in the total study population, XGBoost performed the best (AUC: 0.762 [0.726, 0.795]). Shapley values identified tumor size, Ki-67, and patient age as the most important predictors.

CONCLUSION

Tree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.

A multi-center cross-sectional investigation of the diagnosis and treatment of breast cancer with synchronous ipsilateral supraclavicular lymph node metastases based on the CSBrs-009

BACKGROUND

The diagnosis and management of breast cancer with ipsilateral supraclavicular lymph node metastases currently lack any applicable criteria or guidelines (ISLM), and diagnostic and treatment strategies are varied by medical centers. This study aimed to determine the current status of the diagnosis and treatment of breast cancer with ISLM among Chinese patients.

METHODS

Data from 30 hospitals on ISLM breast cancer patients between January 1, 2018, and December 31, 2018, were systematically analyzed for the detection rate, clinicopathological characteristics, diagnosis and treatment strategies of breast cancer with ISLM among Chinese patients.

RESULTS

A total of 26,723 women presented with breast cancer in 30 hospitals over the study period. A total of 127 ISLM breast cancer patients were finally enrolled. Synchronous ISLM without distant metastases was present in 0.48% of cases. The diagnosis of patients with 86.6% of ISLM was based on ultrasound examinations. The proportion of ISLM diagnosed by fine-needle aspiration biopsy (FNA) or core-needle biopsy (CNB) before surgery was 16.5% and 37.0%, respectively. Moreover, 45.7% of the ISLM patients had no pathological evidence. Regarding treatment strategies, 69.3% of ISLM patients received neoadjuvant chemotherapy (NAC) for more than 4 cycles, while 15.7% of patients did not receive NAC. 86.6% of ISLM patients underwent a mastectomy, and breast-conserving surgery was performed in 3.9% of ISLM patients. Moreover, 41.7% of ISLM patients received supraclavicular lymph node dissection (SCLD), while 47.2% were not treated with SCLD.

CONCLUSIONS

The overall detection rate of breast cancer in patients with ISLM is low in China and varies widely between hospitals. There is no consensus on the optimal diagnosis and treatment of patients with ISLM breast cancer.

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

BACKGROUND

This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer.

MATERIALS AND METHODS

Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity.

RESULTS

A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05).

CONCLUSION

Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.

Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm

PURPOSE

The reliable detection of tumor-infiltrated axillary lymph nodes for breast cancer [BC] patients plays a decisive role in further therapy. We aimed to find out whether cross-sectional imaging techniques could improve sensitivity for pretherapeutic axillary staging in nodal-positive BC patients compared to conventional imaging such as mammography and sonography.

METHODS

Data for breast cancer patients with tumor-infiltrated axillary lymph nodes having received surgery between 2014 and 2020 were included in this study. All examinations (sonography, mammography, computed tomography [CT] and magnetic resonance imaging [MRI]) were interpreted by board-certified specialists in radiology. The sensitivity of different imaging modalities was calculated, and binary logistic regression analyses were performed to detect variables influencing the detection of positive lymph nodes.

RESULTS

All included 382 breast cancer patients had received conventional imaging, while 52.61% of the patients had received cross-sectional imaging. The sensitivity of the combination of all imaging modalities was 68.89%. The combination of MRI and CT showed 63.83% and the combination of sonography and mammography showed 36.11% sensitivity.

CONCLUSION

We could demonstrate that cross-sectional imaging can improve the sensitivity of the detection of tumor-infiltrated axillary lymph nodes in breast cancer patients. Only the safe detection of these lymph nodes at the time of diagnosis enables the evaluation of the response to neoadjuvant therapy, thereby allowing access to prognosis and improving new post-neoadjuvant therapies.

Optimal Selection of Imaging Examination for Lymph Node Detection of Breast Cancer With Different Molecular Subtypes

Objective

Axillary lymph node management is an important part of breast cancer surgery and the accuracy of preoperative imaging evaluation can provide adequate information to guide operation. Different molecular subtypes of breast cancer have distinct imaging characteristics. This article was aimed to evaluate the predictive ability of imaging methods in accessing the status of axillary lymph node in different molecular subtypes.

Methods

A total of 2,340 patients diagnosed with primary invasive breast cancer after breast surgery from 2013 to 2018 in Jiangsu Breast Disease Center, the First Affiliated Hospital with Nanjing Medical University were included in the study. We collected lymph node assessment results from mammography, ultrasounds, and MRIs, performed receiver operating characteristic (ROC) analysis, and calculated the sensitivity and specificity of each test. The C-statistic among different imaging models were compared in different molecular subtypes to access the predictive abilities of these imaging models in evaluating the lymph node metastasis.

Results

In Her-2 + patients, the C-statistic of ultrasound was better than that of MRI (0.6883 vs. 0.5935, p=0.0003). The combination of ultrasound and MRI did not raise the predictability compared to ultrasound alone (p=0.492). In ER/PR+HER2- patients, the C-statistic of ultrasound was similar with that of MRI (0.7489 vs. 0.7650, p=0.5619). Ultrasound+MRI raised the prediction accuracy compared to ultrasound alone (p=0.0001). In ER/PR-HER2- patients, the C-statistics of ultrasound was similar with MRI (0.7432 vs. 0.7194, p=0.5579). Combining ultrasound and MRI showed no improvement in the prediction accuracy compared to ultrasound alone (p=0.0532).

Conclusion

From a clinical perspective, for Her-2+ patients, ultrasound was the most recommended examination to assess the status of axillary lymph node metastasis. For ER/PR+HER2- patients, we suggested that the lymph node should be evaluated by ultrasound plus MRI. For ER/PR-Her2- patients, ultrasound or MRI were both optional examinations in lymph node assessment. Furthermore, more new technologies should be explored, especially for Her2+ patients, to further raise the prediction accuracy of lymph node assessment.

Gail model and fifth edition of ultrasound BI-RADS help predict axillary lymph node metastasis in breast cancer-A multicenter prospective study

RATIONALE AND OBJECTIVES

We aim to assess the performance of the Gail model and the fifth edition of ultrasound BI-RADS (Breast Imaging Reporting and Data System) in breast cancer for predicting axillary lymph node metastasis (ALNM).

MATERIALS AND METHODS

We prospectively studied 958 female patients with breast cancer between 2018 and 2019 from 35 hospitals in China. Based on B-mode, color Doppler, and elastography, radiologists classified the degree of suspicion based on the fifth edition of BI-RADS. Individual breast cancer risk was assessed with the Gail model. The association between the US BI-RADS category and the Gail model in terms of ALNM was analyzed.

RESULTS

We found that US BI-RADS category was significantly and independently associated with ALNM (P < 0.001). The sensitivity, specificity, and accuracy of BI-RADS category 5 for predicting ALNM were 63.6%, 71.6%, and 68.6%, respectively. Combining the Gail model with the BI-RADS category showed a significantly higher sensitivity than using the BI-RADS category alone (67.8% vs. 63.6%, P < 0.001). The diagnostic accuracy of the BI-RADS category combined with the Gail model was better than that of the Gail model alone (area under the curve: 0.71 vs. 0.50, P < 0.001).

CONCLUSION

Based on the conventional ultrasound and elastography, the fifth edition of ultrasound BI-RADS category could be used to predict the ALNM of breast cancer. ALNM was likely to occur in patients with BI-RADS category 5. The Gail model could improve the diagnostic sensitivity of the US BI-RADS category for predicting ALNM in breast cancer.

Diagnostic performance of T2-weighted imaging and intravoxel incoherent motion diffusion-weighted MRI for predicting metastatic axillary lymph nodes in T1 and T2 stage breast cancer

BACKGROUND

Non-invasive modalities for assessing axillary lymph node (ALN) are needed in clinical practice.

PURPOSE

To investigate the suspicious ALN on unenhanced T2-weighted (T2W) imaging and intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) for predicting ALN metastases (ALNM) in patients with T1-T2 stage breast cancer and clinically negative ALN.

MATERIAL AND METHODS

Two radiologists identified the most suspicious ALN or the largest ALN in negative axilla by T2W imaging features, including short axis (Size-S), long axis (Size-L)/S ratio, fatty hilum, margin, and signal intensity on T2W imaging. The IVIM parameters of these selected ALNs were also obtained. The Mann-Whitney U test or t-test was used to compare the metastatic and non-metastatic ALN groups. Finally, logistic regression analysis with T2W imaging and IVIM features for predicting ALNM was conducted.

RESULTS

This study included 49 patients with metastatic ALNs and 50 patients with non-metastatic ALNs. Using the above conventional features on T2W imaging, the sensitivity and specificity in predicting ALNM were not high. Compared with non-metastatic ALNs, metastatic ALNs had lower pseudo-diffusion coefficient (D*) (P = 0.043). Logistic regression analysis showed that the most useful features for predicting ALNM were signal intensity and D*. The sensitivity and specificity predicting ALNM that satisfied abnormal signal intensity and lower D* were 73.5% and 84%, respectively.

CONCLUSIONS

The abnormal signal intensity on T2W imaging and one IVIM feature (D*) were significantly associated with ALNM, with sensitivity of 73.5% and specificity of 84%.

The impact of body mass index (BMI) on MRI diagnostic performance and surgical management for axillary lymph node in breast cancer

Background

We hypothesized that different BMI might have different impact on pre-operative MRI axillary lymph node (ALN) prediction accuracy and thereby subsequent surgical lymph node management. The aim of this study is to evaluate the effect of BMI on presentation, surgical treatment, and MRI performance characteristics of breast cancer with the main focus on ALN metastasis evaluation.

Methods

The medical records of patients with primary invasive breast cancer who had pre-operative breast MRI and underwent surgical resection were retrospectively reviewed. They were categorized into 3 groups in this study: underweight (BMI < 18.5), normal (BMI of 18.5 to 24), and overweight (BMI > 24). Patients’ characteristics, surgical management, and MRI performance for axillary evaluation between the 3 groups were compared.

Results

A total of 2084 invasive breast cancer patients with a mean age of 53.4 ± 11.2 years were included. Overweight women had a higher rate of breast conserving surgery (56.7% vs. 54.5% and 52.1%) and initial axillary lymph node dissection (15.9% vs. 12.2% and 8.5%) if compared to normal and underweight women. Although the post-operative ALN positive rates were similar between the 3 groups, overweight women were significantly found to have more axillary metastasis on MRI compared with normal and underweight women (50.2% vs 37.7% and 18.3%). There was lower accuracy in terms of MRI prediction in overweight women (65.1%) than in normal and underweight women (67.8% and 76.1%).

Conclusion

Our findings suggest that BMI may influence the diagnostic performance on MRI on ALN involvement and the surgical management of the axilla in overweight to obese women with breast cancer.

Decreased Expression of circ_0000160 in Breast Cancer With Axillary Lymph Node Metastasis

Background: Circular RNAs (circRNAs) have been shown to play important roles in the development and progression of human cancers. Emerging evidence shows that circRNAs have the potential to be promising biomarkers for cancer diagnosis and prognosis. However, the roles of circRNAs in breast cancer axillary lymph node metastasis (ALNM) remain to be determined.

Methods: Transcriptome sequencing was utilized to screen the differentially expressed circRNAs in three breast cancer tissues with ALNM and three without ALNM. Differentially expressed circRNAs were further verified by quantitative real-time PCR. Moreover, receiver operating characteristic (ROC) curve analysis was performed to calculate the value of circRNAs to distinguish breast cancer tissues with ALNM and those without ALNM. To explore the potential mechanism of the circRNAs, a circRNA–miRNA–mRNA network was constructed based on the CircInteractome, circBank, and mirDIP online software.

Results: In total, 31 differentially expressed circRNAs were identified by transcriptome sequencing; among them, 21 were upregulated and 10 were downregulated in breast cancer with ALNM compared to those without ALNM. Circ_0000160 was validated to be downregulated in breast cancer tissues with ALNM compared with those without ALNM. The ROC curve showed the ability of circ_0000160 to distinguish breast cancer tissues with ALNM and those without ALNM, with an area under the curve of 0.7435. Furthermore, bioinformatics analysis revealed that the predicted mRNAs for circ_0000160 may be related to lymph node metastasis. The predicted mRNAs for circ_0000160 may be involved in many cancer-related pathways.

Conclusion: A decreased expression of circ_0000160 was found in breast cancer with axillary lymph node metastasis. Circ_0000160 may have the potential to distinguish breast cancer with axillary lymph node metastasis from those without axillary lymph node metastasis.

The value of contrast-enhanced ultrasound enhancement patterns for the diagnosis of sentinel lymph node status in breast cancer: systematic review and meta-analysis

BACKGROUND

The sentinel lymph node (SLN) can represent the metastasis status of axillary lymph nodes and is a prognostic factor of breast cancer. Preoperative imaging provides information for axillary surgery decision-making, and this meta-analysis evaluated the diagnostic value of contrast-enhanced ultrasound (CEUS) for SLN status in breast cancer patients.

METHODS

The PubMed, Embase, Medline, Google Scholar, Clinical Trails gov. and Cochrane Library databases were searched from inception until 31 March 2020. Two review authors independently screened and selected the relevant studies and extracted data, and the assessment of the methodological quality of studies was according to the QUADAS-2 tool. The diagnostic value of CEUS was assessed by calculating the pooled sensitivity, specificity, area under the curve, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio, and a summary receiver operating characteristic curve and hierarchical modeling method was used to conduct the meta-analysis.

RESULTS

Five studies with 771 breast cancer patients were included, and the results showed CEUS could provide additional information for SLN preoperative diagnosis. A homogeneous or uniform enhancement pattern suggested a benign lymph node, and a heterogeneous, no pattern, or weak enhancement pattern suggested a node was malignant, demonstrating high sensitivity of 0.960 (95% CI: 0.856, 0.989) and moderate specificity of 0.807 (0.581, 0.926). The pooled positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 4.987 (2.104, 11.822) and 0.049 (0.014, 0.168), and 101.294 (31.202, 328.837), respectively.

CONCLUSIONS

A homogeneous enhancement pattern was highly suggestive of benign lymph nodes with high sensitivity. CEUS could effectively identify the SLN, and facilitate the diagnosis of its metastatic status.

REGISTRATION NUMBER

PROSPERO protocol CRD42020176828.

ADAMTS8 Expression is a Potential Prognostic Biomarker for Postoperative Metastasis in Lymph Node-Negative Early-Stage Invasive Breast Carcinoma Patients

Purpose

Invasive breast carcinoma (BC) is the most common malignant breast tumor. Most lymph node-negative (LN-) early-stage BC patients usually have a good prognosis, but 7% of patients still develop metastasis after surgery. It is not yet clear how to screen candidates with poorer prognosis in LN- early-stage patients, so that they can receive intensive therapy. Hence, we expect to identify a prognostic biomarker to assess postoperative metastasis in LN- early-stage BC patients.

Patients and Methods

Screening and verifying of candidate genes by gene expression profiling of LN- early-stage BC samples (n = 640) from 3 independent public datasets. Univariable and multivariable Cox regression analyses showed the relation between the candidate genes and postoperative metastasis. Distant metastasis-free survival (DMFS) analysis was performed to examine the prognostic significance. Quantitative real-time polymerase chain reaction (qRT-PCR) assays were performed to examine ADAMTS8 expression and prognostic association in our clinical samples (n = 25).

Results

In the discovery cohort (TCGA and GSE20685 datasets), we found that ADAMTS8 tend to be low expression in LN- early-stage BC, and low ADAMTS8 expression was associated with postoperative metastasis and shortened DMFS. Moreover, the above finding was confirmed in the validation cohort (GSE6538 dataset). Lower ADAMTS8 expression was related to poorer prognostic clinical stage and PAM50 subtypes and shorter DMFS. Gene enrichment analysis indicated that ADAMTS8 may be correlated with BC metastasis. qRT-PCR assays of our clinical tumor sample showed that patients with low ADAMTS8 expression seem to be prone to developing metastasis and have a shorter DMFS time.

Conclusion

Our research shows that low ADAMTS8 expression is associated with postoperative metastasis and shortened DMFS in LN- early-stage BC patients, which suggests that ADAMTS8 may be a potential prognostic marker for postoperative metastasis in LN- early-stage BC patients.

Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

INTRODUCTION

To establish a nomogram for predicting axillary lymph node (ALN) involvement in patients with early-stage invasive breast cancer (BC) based on magnetic resonance imaging (MRI) features and clinicopathological characteristics.

MATERIALS AND METHODS

Patients with confirmed early-stage invasive BC between 03/2016 and 05/2017 were retrospectively reviewed at the National Cancer Center/Cancer Hospital. Risk factors for ALN metastasis (ALNM) were identified by univariable and multivariable logistic regression analysis. The independent risk factors were used to create a nomogram.

RESULTS

This study included 214 early-stage invasive BC patients, including 57 (26.6%) with positive ALNs. Tumor location (OR = 4.019, 95% CI: 1.304 -12.383, P = .015), tumor size (OR = 3.702, 95%CI: 1.517 -9.034, P = .004), multifocality (OR = 3.534, 95%CI: 1.249 -9.995, P = .017), MR-reported suspicious ALN (OR = 9.829, 95%CI: 4.132 -23.384, P <0.001), apparent diffusion coefficient (ADC) value (OR = 0.367, 95%CI: 0.158 -0.852, P = .020), and lymphovascular invasion (LVI) (OR = 3.530, 95%CI: 1.483 -8.400, P = .004) were identified as independent risk factors associated with ALNM. A nomogram was created for predicting the probability of ALNM by using these risk factors. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual ALNM rate. The area under the curve was 0.88 (95% CI: 0.83 -0.93). The nomogram had a bootstrapped-concordance index of 0.88 and was well-calibrated.

CONCLUSION

The nomogram based on MRI and clinicopathologic features might be a useful tool for predicting ALNM in early-stage invasive BC and could help clinical decision-making.

Predictors of sentinel lymph node metastasis in Chinese women with clinical T1-T2 N0 breast cancer and a normal axillary ultrasound

BACKGROUND

The clinicopathological predictors of sentinel lymph node (SLN) metastasis in clinical T1-T2 N0 (cT1-T2 N0) patients with a normal axillary ultrasound (AUS) are unclear.

PURPOSE

To assess the association between clinicopathological characteristics of a primary tumor and SLN metastasis in cT1-T2 N0 patients with a normal AUS.

MATERIAL AND METHODS

Patients who were diagnosed with cT1-T2 N0 invasive breast cancer and who obtained normal AUS results between October 2016 and September 2018 in a single hospital were included. Clinicopathological data were collected to explore the predictors of SLN metastasis using a multivariate logistic regression model.

RESULTS

SLN metastasis occurred in 26 patients (18.4%) among 141 AUS-normal patients, of which 24 cases (17.0%) had one or two nodal involvements. In the univariate analysis, tumor location, estrogen receptor (ER) status, progesterone receptor (PR) status, and lymphovascular invasion (LVI) were significantly associated with SLN metastasis (P < 0.05). The multivariate analysis showed that tumor location in the upper outer quadrant (odds ratio [OR] = 4.49, 95% confidence interval [CI] = 1.63-12.37; P = 0.004), positive PR status (OR = 13.35, 95% CI = 1.60-111.39; P = 0.017), and positive LVI (OR = 8.66, 95% CI = 2.20-34.18; P = 0.002) were independent high-risk factors for SLN metastasis. The area under the receiver operating characteristic curve of the regression model was 0.787 (95% CI = 0.694-0.881; P < 0.001).

CONCLUSION

Tumor location in the upper outer quadrant, positive PR, and LVI status were found to be significantly high-risk factors for SLN metastasis among cT1-T2 N0 breast cancer patients with a normal AUS result.

Clinical Value of Three Combined Ultrasonography Modalities in Predicting the Risk of Metastasis to Axillary Lymph Nodes in Breast Invasive Ductal Carcinoma

Objective

The present study aimed to assess the clinical value of conventional ultrasound (C-US), ultrasound elastography (UE), percutaneous contrast-enhanced ultrasound (P-CUES), and the combination of these three ultrasonography modalities for evaluating the risk of axillary lymph node (ALN) metastasis in breast invasive ductal carcinoma (IDC).

Methods

This retrospective analysis included 120 patients with pathologically confirmed IDC who underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Based on the gold standard of postoperative pathology, ALN pathology results were evaluated and compared with findings obtained using C-US, UE, P-CUES, and the three modalities combined.

Results

(1) There was a statistically significant difference between the histological grade of the tumor and the pathological condition of ALNs. (2) The difference between C-US parameters and UE score were statistically significant. The accuracy of P-CEUS localization of SLNs was 100% (96/96) when compared with localization guided by methylene blue. The difference in the distribution of the four SLN enhancement patterns was statistically significant. (3) The sensitivity, specificity, positive predictive value, and negative predictive value of C-US and UE were 75%, 71%, 58%, and 89%, and 71%, 72%, 50%, and 86%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of P-CUES were 91%, 82%, 78%, 92%, respectively. When all three modalities were combined, the sensitivity, specificity, positive predictive value, and negative predictive value were 94%, 89%, 86%, and 95%, respectively. In the detection of ALN metastasis, there was a good correlation between histopathological results and evaluations based on the three combined ultrasonography modalities (kappa: 0.82, p<0.001).

Conclusions

When compared to C-US, UE, or P-CEUS alone, the combination of the three ultrasonography modalities was found to be superior in distinguishing metastatic and non-metastatic ALNs. This combined strategy may aid physicians in determining the most appropriate approach to ALN surgery as well as the prognosis of breast IDC.

Non-invasive prediction of lymph node status for patients with early-stage invasive breast cancer based on a morphological feature from ultrasound images

Background

This study aimed to estimate the value of a morphological feature on ultrasound (US) for preoperative diagnosis of axillary lymph node (ALN) status in patients with early-stage invasive breast cancer (ESIBC).

Methods

In this retrospective work, a total of 239 ESIBC patients, were recruited, and their preoperative US images and postoperative pathology results were collected. The relationship between US images based on morphological features and ALN metastasis was investigated. The tumor circularity and US-reported ALN status were developed as a nomogram to predict the ALN status.

Results

Among the 239 participants, 82 (34.31%) had ALN metastasis, and 157 (65.69%) did not. There was a statistically significant difference in tumors between participants diagnosed with and without ALN metastasis. The median value was 0.47 vs. 0.62 (P<0.001) in the training group, respectively, and the value was 0.50 vs. 0.60 (P<0.001) in the validation group, respectively. The clinical model nomogram was shown to have high efficiency in predicting ALN status among our research population. The area under the curve (AUC) was 0.89 in the training group and 0.90 in the validation group and the accuracy was 85.79% and 81.63%, respectively.

Conclusions

The clinical model nomogram based on tumor circularity and US-reported ALN status is a non-invasive approach for ALN metastasis prediction in ESIBC patients with high efficacy.

Predicting pathological axillary lymph node status with ultrasound following neoadjuvant therapy for breast cancer

Purpose

High-performing imaging and predictive markers are warranted to minimize surgical overtreatment of the axilla in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NACT). Here we have investigated whether axillary ultrasound (AUS) could identify axillary lymph node (ALN) metastasis (ALNM) pre-NACT and post-NACT for BC. The association of tumor, AUS features and mammographic density (MD) with axillary-pathological complete response (axillary-pCR) post-NACT was also assessed.

Methods

The NeoDense-study cohort (N = 202, NACT during 2014–2019), constituted a pre-NACT cohort, whereas patients whom had a cytology verified ALNM pre-NACT and an axillary dissection performed (N = 114) defined a post-NACT cohort. AUS characteristics were prospectively collected pre- and post-NACT. The diagnostic accuracy of AUS was evaluated and stratified by histological subtype and body mass index (BMI). Predictors of axillary-pCR were analyzed, including MD, using simple and multivariable logistic regression models.

Results

AUS demonstrated superior performance for prediction of ALNM pre-NACT in comparison to post-NACT, as reflected by the positive predictive value (PPV) 0.94 (95% CI 0.89–0.97) and PPV 0.76 (95% CI 0.62–0.87), respectively. We found no difference in AUS performance according to neither BMI nor histological subtype. Independent predictors of axillary-pCR were: premenopausal status, ER-negativity, HER2-overexpression, and high MD.

Conclusion

Baseline AUS could, to a large extent, identify ALNM; however, post-NACT, AUS was insufficient to determine remaining ALNM. Thus, our results support the surgical staging of the axilla post-NACT. Baseline tumor biomarkers and patient characteristics were predictive of axillary-pCR. Larger, multicenter studies are needed to evaluate the performance of AUS post-NACT.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06283-8.

Preoperative prediction of axillary lymph node metastasis in patients with breast cancer based on radiomics of gray-scale ultrasonography

Background

To investigate the performance of a radiomics model based on gray-scale ultrasonography (US) for the preoperative non-invasive prediction of ipsilateral axillary lymph node (ALN) metastasis in patients with breast cancer (BC).

Methods

A total of 192 pathologically confirmed BC patients were included in this study. The training set was comprised of 132 patients from hospital 1 and the test set was comprised of 60 patients from hospital 2. All patients underwent US before percutaneous core biopsy and the results of ALN status reported by a radiologist with 12 years of experience were recorded. Radiomic features were extracted from the gray-scale US images. Max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for data dimension reduction and feature selection. A radiomics model was constructed using LASSO and was validated using the leave group out cross-validation (LGOCV) method. The performance of the model was validated with receiver operating characteristic (ROC), calibration curve, and decision curve analysis.

Results

A total of 860 features were extracted from the gray-scale US images of each breast lesion, and 9 radiomic features were selected for model construction. The area under the curve (AUC), sensitivity, and specificity of the model for predicting ALN metastasis were 0.85, 78.9%, and 77.3% in the training set and 0.65, 68.0%, and 79.4% in the test set, respectively. The prediction performance of the model was significantly higher than that of the radiologist (AUC: 0.85 vs. 0.59, P<0.01) in the training set and was slightly higher than that of the radiologist (AUC: 0.65 vs. 0.63, P>0.05) in the test set.

Conclusions

The non-invasive radiomics model has the ability to predict ALN metastasis for patients with breast cancer and may outperform US-reported ALN status performed by the radiologist.

Determining the axillary nodal status with four current imaging modalities including 18F-FDG PET/MRI in newly diagnosed breast cancer: A comparative study using histopathology as reference standard

Purpose: To compare breast magnetic resonance imaging (MRI), thoracal MRI, thoracal 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/MRI and axillary sonography for the detection of axillary lymph node metastases in women with newly diagnosed breast cancer. Materials and Methods: This prospective double-center study included patients with newly diagnosed breast cancer between March 2018 and December 2019. Patients underwent thoracal (18F-FDG PET/)MRI, axillary sonography, and dedicated prone breast MRI. Datasets were evaluated separately regarding nodal status (nodal+ vs. nodal-). Histopathology served as reference standard in all patients. The diagnostic performance of breast MRI, thoracal MRI, thoracal PET/MRI and axillary sonography in detecting nodal positive patients was tested by creating receiver-operating-characteristic curves (ROC) with a calculated area under the curve (AUC). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for all four modalities. A McNemar test was used to assess differences. Results: 112 female patients (mean age 53.04 ± 12.6 years) were evaluated. Thoracal PET/MRI showed the highest ROC-AUC with a value of 0.892. The AUC for breast MRI, thoracal MRI and sonography were 0.782, 0.814 and 0.834, respectively. Differences between thoracal PET/MRI and axillary sonography, thoracal MRI and breast MRI were statistically significant (PET/MRI vs. axillary sonography, P = 0.01; PET/MRI vs. thoracal MRI, P = 0.02; PET/MRI vs. breast MRI, P = 0.03). PET/MRI showed the highest sensitivity (81.8%, 36/44) (95%-CI: 67.29-91.81%) while axillary sonography had the highest specificity (98.5%, 65/66), 95%-CI: 91.84-99.96%). Conclusion: 18F-FDG PET/MRI outperforms axillary sonography, breast MRI and thoracal MRI in determining the axillary lymph node status. In a clinical setting, the combination of 18F-FDG PET/MRI and axillary sonography might be considered to provide even more accuracy in diagnosis.

Preoperative Axillary Ultrasound Helps in the Identification of a Limited Nodal Burden in Breast Cancer Patients

Since the Z0011 trial, the clinical evaluation of axillary status has been redirected to predicting nodal tumor burden rather than nodal metastases. Our study aimed to evaluate the value of clinicopathological factors and axillary ultrasound (US) for the prediction of a high nodal burden (≥3 metastatic lymph nodes) in breast cancer patients. A total of 532 consecutive patients who underwent preoperative axillary US and subsequent surgery for clinical T1-2 breast cancer with a final pathologic analysis were included. Clinical and pathologic variables were retrospectively evaluated. Univariate and multivariate statistical analyses were performed to identify the variables that were associated with a high nodal burden. Among the 532 patients, 110 (20.7%) had a high axillary nodal burden and 422 (79.3%) had a limited nodal burden. The multivariate analysis showed that suspicious axillary US findings (P < 0.001), clinical T2 stage (P = 0.011), the presence of lymphovascular invasion (P < 0.001), and estrogen receptor positivity (P < 0.001) were significantly associated with a high nodal burden. Patients with negative axillary US findings seldom had a high nodal burden, with a negative predictive value of 93.0% (294/316). Patients with suspicious axillary US findings, clinical T2 stage, lymphovascular invasion, and estrogen receptor positivity are more likely to have a high nodal burden, which may provide additional information for the treatment plan of breast cancer patients. Preoperative axillary US helps identify a limited nodal burden in breast cancer patients and has implications for axillary lymph node dissection and adjuvant treatment.

Development and validation of a nomogram for prediction of lymph node metastasis in early-stage breast cancer

Background

Lymph node status is an important factor in determining the prognosis of early-stage breast cancer. We endeavored to build and validate a simple nomogram to predict lymph node metastasis (LNM) in patients with early-stage breast cancer.

Methods

Patients with T1-2 and non-metastasis (M0) breast cancer registered in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. All patients were divided into primary cohort and validation cohort in a 2:1 ratio. In order to assess risk factors for LNM, we performed univariate and multivariate binary logistic regression, and based on results of multivariable analysis, we built the predictive nomogram model. The C-index, receiver operating characteristic (ROC) and calibration plots were applied to assess LNM model performance. Moreover, the nomogram efficiency was further validated through the validation cohort, part of which was from the First Affiliated Hospital of Nanjing Medical University database.

Results

Totally, 184,531 female breast cancer with T1-2 tumor size from SEER database and 1,222 patients from the Chinese institutional data were included. There were 123,019 patients in the primary cohort and 62,734 patients in validation cohort. The LNM nomogram was composed of seven features including age at diagnosis, race, primary site, histologic type, grade, tumor size and subtype. The model showed good discrimination, with a C-index of 0.720 [95% confidence interval (CI): 0.717-0.723] and good calibration. Similar C-index was 0.718 (95% CI: 0.713-0.723) in validation cohort. Consistently, ROC curves presented good discrimination in the primary cohort [area under the curve (AUC) =0.720] and the validation set (AUC =0.718) for the LNM nomogram. Calibration curve of the nomogram demonstrated good agreement.

Conclusions

With the prediction of novel validated nomogram for women with early-stage breast cancer, doctors may distinguish patients with high possibility of LNM and devise individualize treatments.

Percutaneous Sonazoid-enhanced ultrasonography combined with in vitro verification for detection and characterization of sentinel lymph nodes in early breast cancer

Objectives

To assess the efficacy of percutaneous Sonazoid-enhanced ultrasound and in vitro verification for identification sentinel lymph nodes (SLNs) and diagnosis of metastatic SLNs in patients with early breast cancer (BC).

Methods

Sixty-eight patients with early BC were enrolled finally. After the induction of general anesthesia, 0.4 ml of Sonazoid (SNZ), a new second-generation tissue-specific ultrasound contrast agent (UCA), mixed with 0.6 ml of methylene blue, was injected intradermally. The lymphatic vessels and connected SLNs were immediately observed and marked. After being resected, these SLNs were soaked in saline water and examined still in the mode of contrast-enhanced ultrasound (CEUS) in vitro. This procedure could ensure that all the enhanced nodes had been removed as much as possible. The numbers of SLNs detected by UCA and blue dye were recorded. The enhancement patterns of SLNs were compared with the final pathological results.

Results

SLNs detection rate by SNZ-CEUS was 100%, which was higher than that by blue dye (95.59%). CEUS identified a median of 1.5 nodes, while blue dye identified a median of 1.9 nodes per case (p = 0.0012). When homogeneous high perfusion and complete annular high perfusion were regarded as negative nodes, the sensitivity and negative predictive value were 92.31% and 96.79% respectively, while the specificity was 84.21%.

Conclusions

Percutaneous SNZ-enhanced ultrasonography combined with in vitro verification is a feasible and reliable method for SLNs identification intraoperatively. Enhancement patterns can be helpful in determining the status of SLNs.

Key Points

• CEUS with percutaneous injection of Sonazoid can successfully identify SLNs with the rate of 100% in early breast cancer patients, higher than 95.59% of blue dye.

• Sonazoid, with high affinity with reticuloendothelial cells, increases the imaging time of SLNs and facilitates biopsy intraoperatively better than Sonovue as a lymphatic tracer.

• Homogenous high and complete annular high perfusions have a sensitivity of 92.31% and a negative predictive value of 96.79% in the prediction of uninvolved SLNs.

Is axillary imaging for invasive lobular carcinoma accurate in determining clinical node staging?

PURPOSE

Preoperative evaluation of clinical N-stage (cN) is difficult in breast cancer patients with invasive lobular carcinoma (ILC). Our goal was to assess the predictive value of axillary imaging in ILC by comparing imaging cN and pathologic N-stage (pN).

METHODS

A single-institution retrospective review was performed for newly diagnosed stage I-III ILC patients undergoing preoperative breast imaging from 2011 to 2016. Clinicopathologic factors; mammogram, MRI, and ultrasound findings; and surgical pathology data were reviewed. Sub-analysis for pN2-N3 patients was performed to determine imaging sensitivity for patients with a larger nodal disease burden. Statistical analysis included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each imaging modality.

RESULTS

Of the total 349 patients included, 70.5% were cN0, and 62% were pN0 (p = 0.03). For all patients, mammogram sensitivity was 7%, specificity 97%, PPV 50%, NPV 72%; ultrasound sensitivity was 26%, specificity 86%, PPV 52%, NPV 67%; MRI sensitivity was 7%, specificity 98%, PPV 80%, NPV 51%. For pN2/N3 patients, 38% were identified as cN0. Mammogram sensitivity was 10%; ultrasound 42%; MRI 65%. Pathology evaluation of N2/N3 patients indicated LN were replaced with ILC but maintained normal architecture. The average largest pathologic tumor deposit (1.5 ± 0.8 cm) correlated with average largest imaging LN size (1.4 ± 0.6 cm) (p = 0.58).

CONCLUSION

A statistically significant difference between clinical and pathologic N-stage exists for ILC patients. MRI was most sensitive for identification of pN2-N3 patients and should be considered part of routine axillary imaging evaluation for ILC patients.

Ultrasonographic Algorithm for the Assessment of Sentinel Lymph Nodes That Drain the Mammary Carcinomas in Female Dogs

The status of sentinel lymph nodes (SLNs) is decisive in staging, prognosis, and therapeutic approach. Using an ultrasonographic examination algorithm composed of B-mode, Doppler technique, contrast-enhanced ultrasound (CEUS) and elastography, this study aimed to determine the diagnostic performance of the four techniques compared to histopathological examination. 96 SLNs belonging to 71 female dogs with mammary gland carcinomas were examined. After examinations, mastectomy and lymphadenectomy were performed. Histopathological examination confirmed the presence of metastases in 54 SLNs. The elasticity score had the highest accuracy-89.71%, identifying metastases in SLNs with 88.9.9% sensitivity (SE) and 90.5% specificity (SP), ROC analysis providing excellent results. The S/L (short axis/long axis) ratio showed 83.3% SE and 78.6% SP as a predictor of the presence of metastases in SLN having a good accuracy of 81.2%. On Doppler examination, the resistivity index(RI) showed good accuracy of 80% in characterizing lymph nodes with metastases versus unaffected ones; the same results being obtained by CEUS examination. By assigning to each ultrasonographic parameter a score (0 or 1) and summing up the scores of the four techniques, we obtained the best diagnostic performance in identifying lymph node metastases with 92.2% accuracy. In conclusion, the use of the presented algorithm provides the best identification of metastases in SLNs, helping in mammary carcinoma staging and appropriate therapeutic management.

A Comparison of the Diagnostic Value of Positron Emission Tomography/Computed Tomography and Ultrasound for the Detection of Metastatic Axillary Nodal Disease in Treatment-Naive Breast Cancer

ABSTRACT

The objective of this study was to describe the diagnostic value of positron emission tomography/computed tomography (PET/CT) and ultrasound (US) for identifying metastatic axillary disease in primary breast cancer. This is a retrospective review of 240 patients with treatment-naive unilateral primary breast cancer of at least stage T2. Eighty-five patients met our inclusion criteria. Initial whole-body PET/CT and axillary US examinations were reviewed and compared with the criterion standard of fine-needle aspiration cytology. Sensitivity, accuracy, and positive predictive value (PPV) for each modality were computed. Because of all positive US cases, specificity and negative predictive value of US were not determined. Sensitivity and accuracy between modalities were compared using McNemar test. The majority of the patients were White women with clinical inflammatory breast cancer and with histologically invasive ductal carcinoma. The most common tumor and nodal stage was T4N3. The tumors were predominantly estrogen receptor positive, progesterone receptor negative, and human epidermal growth factor receptor 2 negative. The sensitivities of PET/CT and US were 96.2% and 100%, respectively. The accuracies for PET/CT and US were 91.8% and 94.1%, respectively. The PPV for PET/CT was 95.1%, and for US, the PPV was 94.1%. No significant difference in sensitivity or accuracy was shown between PET/CT and US for the diagnosis of metastatic axillary nodal disease. Three of 85 cases showed discordance between negative PET/CT and positive US and fine-needle aspiration cytology.

Contrast-Enhanced Digital Mammography: Technique, Clinical Applications, and Pitfalls

OBJECTIVE. Contrast-enhanced digital mammography (CEDM) combines the high spatial resolution of mammography with the improved enhancement provided by contrast medium. In this article, CEDM technique-the current and potential clinical applications and current challenges-will be reviewed. CONCLUSION. CEDM is a promising technique in the supplemental evaluation of patients with mammographically inconclusive findings and potentially in the screening of women with mammographically dense breasts. CEDM is emerging as a cost-effective alternative to dynamic contrast-enhanced MRI to stage newly diagnosed breast cancer and evaluate response to neoadjuvant chemotherapy.

Long non-coding RNAs lnc-ANGPTL1-3:3 and lnc-GJA10-12:1 present as regulators of sentinel lymph node metastasis in breast cancer

Long non-coding RNAs (lncRNAs) participate in various biological processed involved in tumorigenesis, metastasis and proliferation. The aim of the present study was to identify candidate long non-coding RNAs (lncRNAs) involved in sentinel lymph node (SLN) metastasis in breast cancer. Specimens of SLNs were collected from patients with SLN metastasis via punch biopsy. Total RNA was extracted and RNA sequencing (RNA-seq) was conducted. Differential expression profiles of mRNAs and lncRNAs were obtained via bioinformatics analysis, and Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on differentially expressed mRNAs. The expression levels of lncRNAs were analyzed via reverse transcription-quantitative PCR (RT-qPCR), and the regulation network of the lncRNAs to downstream microRNAs (miRs) and mRNAs was predicted. Based on RNA-seq results, six differentially expressed candidate lncRNAs were identified in patients with and without SLN metastasis: lnc-ANGPTL1-3:3, lnc-GJA10-12:1, lnc-ACAN-2:1, lnc-ZPBP2-4:1, lnc-GATA3-16:1 and lnc-ACOX3-5:1. KEGG and GO analysis identified that the mitogen-activated protein kinase (MAPK) and PI3K/Akt signaling pathways were the most enriched pathways. After RT-qPCR analysis, lnc-ANGPTL1-3:3 and lnc-GJA10-12:1 exhibited expression patterns that were consistent with those from RNA-seq. Moreover, receiver operating characteristic curve analysis demonstrated that lnc-ANGPTL1-3:3 and lnc-GJA10-12:1 expression levels had high sensitivity and specificity in the diagnosis of SLN metastasis, and that their expression levels were upregulated in patients with axillary lymph node metastasis. Further analysis revealed that lnc-GJA10-12:1 and lnc-ANGPTL1-3:3 were commonly involved in regulating the miR-302 family, including miR-302d-3p and miR-302c-3p, which together targeted AKT1. Additionally, lnc-ANGPTL1-3:3 was predicted to target miR-520b to regulate MAP3K2 expression. lnc-GJA10-12:1 was also predicted to target miR-34a-5p to regulate MAP2K1 and MAP3K9 expression levels, as well as miR-449a to regulate MAP2K1 expression. The results of the present study suggested that lnc-ANGPTL1-3:3 and lnc-GJA10-12:1 may potentially serve a role in SLN metastasis of breast cancer by regulating the PI3K/Akt and MAPK signaling pathways via targeting the miR-302 family, miR-520a-3p, miR-34a-5p and miR-449a. Thus, lnc-ANGPTL1-3:3 and lnc-GJA10-12:1 in SLN may serve as potential markers of breast cancer metastasis.

Axillary ultrasound during neoadjuvant systemic therapy in triple-negative breast cancer patients

PURPOSE

To investigate the value of performing mid-treatment axillary ultrasound (AUS) in triple-negative breast cancer (TNBC) patients who are undergoing neoadjuvant systemic therapy (NAST) by determining the optimal cutoff number of abnormal nodes associated with residual nodal disease on surgical pathology.

MATERIALS AND METHODS

This sub-study, an interim analysis of an ongoing single-institution clinical trial enrolling patients with stage I-III TNBC, included 106 patients. Number of abnormal nodes at mid-treatment was assessed and recorded by experienced breast radiologists, who empirically categorized lymph nodes using a binary approach of sonographically-normal versus abnormal. Pathologic lymph node positivity was defined as presence of macrometastasis or micrometastasis in ≥1 axillary node from sentinel lymph node biopsy and/or axillary lymph node dissection.

RESULTS

Of 106 patients, 26 (25 %) had residual nodal disease and 80 (75 %) had no nodal disease at surgery. Median number of abnormal nodes at mid-treatment was 5 (standard deviation [SD], 5) for patients with residual nodal disease and 0 (SD, 2) for patients with no nodal disease at surgery (p < 0.0001). TNBC patients with >4 abnormal nodes at mid-treatment had a significantly higher chance of being node-positive at surgery (AUC = 0.908, p < 0.0001; PPV = 90 %).

CONCLUSION

Our data suggest that a cutoff of >4 abnormal nodes on mid-treatment AUS is associated with residual disease post-NAST. If our findings are substantiated by subsequent analyses, then mid-treatment AUS could be used to identify patients unlikely to achieve nodal pathologic complete response and who should be offered alternative therapy.

Correlation between the contrast-enhanced ultrasound image features and axillary lymph node metastasis of primary breast cancer and its diagnostic value

PURPOSE

To analyze the correlation between contrast-enhanced ultrasound image features and axillary lymph node metastasis of primary breast cancer and its diagnostic value.

METHODS

In this study, 64 patients with axillary lymph node metastasis of primary breast cancer diagnosed and treated in our hospital from February 2011 to March 2013 were collected as an observation group, and 54 patients without axillary lymph node metastasis were collected as a control group. All patients underwent a contrast-enhanced ultrasound examination, and the correlation between the contrast-enhanced ultrasound image features and axillary lymph node metastasis and its diagnostic value were analyzed. They were divided into two groups according to their survival conditions: the group with good efficacy and group with poor efficacy, and the prognostic factors of breast cancer in the two groups were analyzed.

RESULTS

There were statistical differences in the peripheral acoustic halo, blood flow classification, ratio of length to diameter (L/D), maximum cortical thickness, and enhancement mode of lymph nodes between the two groups (p < 0.05). The area under ROC curve for diagnosis of axillary lymph node metastasis by contrast-enhanced ultrasound was 0.854, sensitivity was 83.33%, and specificity was 87.5%; L/D and enhancement mode were independent prognostic factors for breast cancer.

CONCLUSIONS

Contrast-enhanced ultrasound image features have diagnostic and prognostic value for axillary lymph node metastasis of breast cancer.

Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools

The detection of lymph node metastasis affects the management of patients with primary breast cancer significantly in terms of staging, treatment, and prognosis. The main goal for the radiologist is to determine and detect the presence of metastatic disease in nonpalpable axillary lymph nodes with a positive predictive value that is high enough to initially select patients for upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with different imaging modalities, but ultrasound is the method of choice for evaluating axillary lymph nodes and for performing image-guided lymph node interventions. This review aims to provide a comprehensive overview of the available imaging modalities for lymph node assessment in patients diagnosed with primary breast cancer. IMPLICATIONS FOR PRACTICE: The detection of lymph node metastasis affects the management of patients with primary breast cancer. The main goal for the radiologist is to detect lymph node metastasis in patients to allow for the selection of patients who should undergo upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with mammography, computed tomography, and magnetic resonance imaging, but ultrasonography is the imaging modality of choice for evaluating axillary lymph nodes. A normal axillary lymph node is characterized by a reniform shape, a maximal cortical thickness of 3 mm without focal bulging, smooth margins, and, depending on size, a discernable central fatty hilum.

Clinical and pathological stage discordance among 433,514 breast cancer patients

BACKGROUND

We aim to determine clinical and pathological stage discordance rates and to evaluate factors associated with discordance.

METHODS

Adults with clinical stages I-III breast cancer were identified from the National Cancer Data Base. Concordance was defined as cTN = pTN (discordance: cTN≠pTN). Multivariate logistic regression was used to identify factors associated with discordance.

RESULTS

Comparing clinical and pathological stage, 23.1% were downstaged and 8.7% were upstaged. After adjustment, factors associated with downstaging (vs concordance) included grade 3 (OR 10.56, vs grade 1) and HER2-negative (OR 3.79). Factors associated with upstaging (vs concordance) were grade 3 (OR 10.56, vs grade 1), HER2-negative (OR 1.25), and lobular histology (OR 2.47, vs ductal). ER-negative status was associated with stage concordance (vs downstaged or upstaged, OR 0.52 and 0.87).

CONCLUSIONS

Among breast cancer patients, nearly one-third exhibit clinical-pathological stage discordance. This high likelihood of discordance is important to consider for counseling and treatment planning.

Diagnostic value of MRI combined with ultrasound for lymph node metastasis in breast cancer: Protocol for a meta-analysis

BACKGROUND

Early diagnosis and treatment of breast cancer are important to prevent fatal tumor progression. Axillary lymph node (ALN) status is the most significant prognostic factor for diagnosing overall survival in breast cancer patients. Axillary lymph node dissection (ALND) is regarded as the reference standard for determining ALN status. However, ALND is an invasive therapy with high morbidity and complications such as lymphedema, seroma and nerve injury. Comparatively, magnetic resonance imaging (MRI) and ultrasound are noninvasive and non-radiative techniques that are common imaging methods to diagnose breast cancer lymph node metastasis. Many studies have investigated the diagnostic value of MRI combined with ultrasound for breast cancer ALN metastasis, but the evidence has been insufficient to apply these modalities when diagnosing new patients.

METHODS

We will search electronic databases including PubMed, EMbase, The Cochrane Library, Chinese Biomedical Database, WangFang Database, and China National Knowledge Infrastructure. The language of studies is limited in English or Chinese. The final search includes articles published in June, 2018. Stata 14.0 software will be used for all statistical analyses, and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) will be utilized to evaluate the quality of the included studies. Meta-regression and subgroup analyses will be performed to explore heterogeneity, which will be derived from the different countries of origin of the included studies. Deeks' funnel plot asymmetry test will be demonstrated the inexistence of publication bias.

RESULT

This study will provide a rational synthesis of current evidences for magnetic resonance imaging combined with ultrasound for breast cancer.

CONCLUSION

The conclusion of this study will provide evidence for the diagnostic value of MRI combined with ultrasound for lymph node metastasis in breast cancer.

REGISTRATION

PROS-PERO CRD42019134474.

Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer

Introduction: Axillary lymph nodes (ALN) status is an essential component in tumor staging and treatment planning for patients with breast cancer. The aim of present study was to evaluate the predictive value of preoperative multidetector-row computed tomography (MDCT) for ALN metastasis in breast cancer patients.

Methods: A total of 148 cases underwent preoperative MDCT examination and ALN surgery were eligible for the study. Logistic regression analysis of MDCT variates was used to estimate independent predictive factors for ALN metastasis. The prediction of ALN metastasis was determined with MDCT variates through receiver operating characteristic (ROC) analysis.

Results: Among the 148 cases, 61 (41.2%) cases had ALN metastasis. The cortical thickness in metastatic ALN was significantly thicker than that in non-metastatic ALN (7.5 ± 5.0 mm vs. 2.6 ± 2.8 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of >3 mm (OR: 12.32, 95% CI: 4.50–33.75, P < 0.001) and non-fatty hilum (OR: 5.38, 95% CI: 1.51–19.19, P = 0.009) were independent predictors for ALN metastasis. The sensitivity, specificity and AUC of MDCT for ALN metastasis prediction based on combined-variated analysis were 85.3%, 87.4%, and 0.893 (95% CI: 0.832–0.938, P < 0.001), respectively.

Conclusions: Cortical thickness (>3 mm) and non-fatty hilum of MDCT were independent predictors for ALN metastasis. MDCT is a potent imaging tool for predicting ALN metastasis in breast cancer. Future prospective study on the value of contrast enhanced MDCT in preoperative ALN evaluation is warranted.

Validation of Breast Cancer Models for Predicting the Nonsentinel Lymph Node Metastasis After a Positive Sentinel Lymph Node Biopsy in a Chinese Population

Objectives:

Over the years, completion axillary lymph node dissection is recommended for the patients with breast cancer if sentinel lymph node metastasis is found. However, not all of these patients had nonsentinel lymph node metastasis on final histology. Some predicting models have been developed for calculating the risk of nonsentinel lymph node metastasis. The aim of our study was to validate some of the predicting models in a Chinese population.

Method:

Two hundred thirty-six patients with positive sentinel lymph node and complete axillary lymph node dissection were included. Patients were applied to 6 models for evaluation of the risk of nonsentinel lymph node involvement. The receiver–operating characteristic curves were shown in our study. The calculation of area under the curves and false negative rate was done for each model to assess the discriminative power of the models.

Results:

There are 105 (44.5%) patients who had metastatic nonsentinel lymph node(s) in our population. Primary tumor size, the number of metastatic sentinel lymph node, and the proportion of metastatic sentinel lymph nodes/total sentinel lymph nodes were identified as the independent predictors of nonsentinel lymph node metastasis. The Seoul National University Hospital and Louisville scoring system outperformed the others, with area under the curves of 0.706 and 0.702, respectively. The area under the curve values were 0.677, 0.673, 0.432, and 0.674 for the Memorial Sloan-Kettering Cancer Center, Tenon, Stanford, and Shanghai Cancer Hospital models, respectively. With adjusted cutoff points, the Louisville scoring system outperformed the others by classifying 26.51% of patients with breast cancer to the low-risk group.

Conclusion:

The Louisville and Seoul National University Hospital scoring system were found to be more predictive among the 6 models when applied to the Chinese patients with breast cancer in our database. Models developed at other institutions should be used cautiously for decision-making regarding complete axillary lymph node dissection after a positive biopsy in sentinel lymph node.