Risk Stratification For Axillary Lymph Node Metastases in Breast Cancer Patients: What Clinicopathological and Radiological Factors of Primary Breast Cancer Can Predict Preoperatively Axillary Lymph Node Metastases?

Preoperative Role of Superb Microvascular Imaging and Shear-Wave Elastography for Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer

ABSTRACT

This study aims to evaluate the role of shearwave elastography (SWE) and superb microvascular imaging (SMI) for preoperative prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. In a cohort of 214 women with breast cancer, B-Mode ultrasonography (US), SMIvascular-index (SMIvi), and SWE (E-mean, E-ratio) values were recorded before tru-cut biopsy. Axillary fine-needle aspiration biopsy (FNAB) and sentinel lymph node sampling results were collected. Imaging findings and histopathological data were statistically compared. Receiver operating characteristic curve analysis was used to evaluate diagnostic performance. Reverse stepwise logistical regression analysis was conducted. Although ALNM was negative in 111 cases, it was positive in 103 patients. Axillary lymph node metastasis (+) group had larger size ( P < 0.001), higher vascularization (SMIvi: 8.0 ± 6.0 versus 5.0 ± 4.3, P < 0.001), and higher elasticity value (E-mean: 129 ± 31 kPa versus 117.3 ± 40 kPa, P = 0.014). Axillary lymph node metastasis was observed statistically more frequently in Her-2 positive cases ( P = 0.005). There was no significant difference between other B-mode US findings ( P > 0.05), SMI Adler ( P = 0.878), and E-ratio ( P = 0.212). The most appropriate cutoff value for the prediction of ALNM was 23.5 mm for size, 3.8 for SMIvi, and 138.5 kPa for E-mean. The most sensitive (77%) method was the SMIvi measurement, while the most specific (86%) finding was Her-2 positivity. The combined model (being Her-2 positive, >23.5 cm, and >3.8 SMIvi) increased the specificity (78%), PPV (71%), and accuracy (68%). Although the increased size is a previously studied parameter in predicting the risk of ALNM, Her-2 and data obtained by SWE, and SMI can be used to assist conventional US.

Molybdenum target mammography-based prediction model for metastasis of axillary sentinel lymph node in early-stage breast cancer

Sentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.

Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer

In the past few years, the axillary lymph node dissection technique has been steadily replaced by sentinel lymph node biopsy for treating and diagnosing breast cancer, thereby minimizing the complications and sequelae of the patients. Nevertheless, sentinel lymph node biopsy still presents limitations, such as high operation requirements, prolonged surgical duration, and adverse reactions to tracer agents. This study developed a novel non-invasive method to predict sentinel lymph node metastasis in breast cancer by analyzing the ultrasound imaging characteristics of the primary tumor, combined with the analysis of peripheral blood T-cell subsets that reflect the immune status of the body. The radiomic features analyzed in this study were extracted from preoperative ultrasound images of 199 solitary breast cancer patients, who were undergoing surgery and were pathologically diagnosed at the Yancheng First People's Hospital. All cases were randomly categorized in a 4:1 ratio to the training (n = 159) and validation (n = 40) cohorts. The extracted radiomics features were subjected to dimensional reduction with the help of the least absolute shrinkage and selection operator technique, resulting in the inclusion of 19 radiomics features. Four classifiers, including naïve Bayesian, logistic regression, classification decision tree, and support vector machine, were utilized to model the radiomics features, conventional ultrasound features, and peripheral blood T cell subsets in the training dataset, and validated using the validation dataset. The best-performing model was chosen for constructing the combined model. The radiomics model constructed using the logistic regression showed the best performance, with the training and validation cohorts showing areas under the curve (AUCs) of 0.77 and 0.68, respectively. The conventional ultrasound and peripheral blood T cell models constructed using the classification decision tree showed the best performance, wherein the training cohort presented AUCs of 0.71 and 0.81, respectively, while the validation cohort presented AUCs of 0.68 and 0.69, respectively. The combined model constructed by logistic regression showed AUCs of 0.91 and 0.79 in the training and validation datasets, respectively. The resulting combined model can be considered a simple, non-invasive method with strong reproducibility and clinical significance. Thus, it can be utilized to predict sentinel lymph node metastasis in breast cancer. Furthermore, the combined model can be effectively used to guide clinical decisions related to the selection of surgical procedures in breast surgery.

Ultrasound and clinicopathological characteristics of breast cancer for predicting axillary lymph node metastasis

OBJECTIVES

The goal of this study was to assess the clinicopathological and ultrasound (US) features of breast cancer for predicting the risk of axillary lymph node metastasis.

METHODS

Patients with breast cancer were included in this retrospective, monocentric, observational study. Their preoperative ultrasound features, clinical data, laboratory results and postoperative pathologic results and immunophenotyping were collected. The association of these factors of breast cancer with axillary lymph node metastasis was evaluated by univariate and multivariate analysis.

RESULTS

In this study, 471 patients diagnosed with breast cancer at the First Affiliated Hospital of Xi'an Jiaotong University between July 2016 and September 2019 were collected, with a total of 471 nodules, of which 231(49.0%) had axillary lymph node metastasis, and 240(51.0%) did not. The parameters of hyperechoic halo, posterior acoustic decrease, microcalcification, carcinogenic embryonic antigen (CEA), cancer antigen-153 (CA153), CK5/6 (+), Ki67 (≥40%), AR (+) and histological grade (grade II and grade III) were significantly and independently associated with axillary lymph node metastasis (p < 0.05 for all).

CONCLUSIONS

The combination of ultrasound features, tumor markers, pathology, and immunohistochemistry can predict axillary lymph node metastasis in breast cancer patients.

The ratio of lymphocyte/red blood cells and platelets/lymphocytes are predictive biomarkers for lymph node metastasis in patients with breast cancer

BACKGROUND

Axillary lymph node metastasis (LNM) affects the progression of breast cancer. However, it is difficult to preoperatively diagnose axillary lymph node status with high sensitivity. Therefore, we hypothesized that platelets/lymphocytes ratio (PLR) and lymphocytes/ red blood cells ratio (LRR) might help in the prognosis of lymph node metastasis in T1-T2 breast cancer.

METHODS

166 patients (Chang Ning Maternity & Infant Health Institute) were included in our study, and the associations of PLR and LPR with lymph node metastasis were investigated. Peripheral blood was collected one week before the surgery, and the patients were divided into different categories based on their PLR and LRR.

RESULTS

The incidence of LNM was significantly increased in the high PLR group (p= 0.002) compared with the low PLR group; LNM was also significantly increased in the low LRR group (p= 0.036) compared with the high LPR group. Further, our study revealed that high PLR (p< 0.001, OR = 4.397, 95% CI = 2.005-9.645), low LRR (p= 0.017, OR = 0.336, 95%CI = 0.136-0.825) and high clinical T stage (p< 0.001, OR = 3.929, 95%CI = 1.913-8.071) are independent predictors of LNM.

CONCLUSIONS

PLR and LRR could be identified as predictors of LNM in patients with T1/T2 breast cancer.

Feasibility and efficiency of contrast enhanced ultrasound real time guided fine needle aspiration for sentinel lymph node of breast cancer

AIM

To assess the feasibility and efficiency of contrast-enhanced ultrasound (CEUS) real-time guided fine needle aspiration (FNA) for sentinel lymph node (SLN) of breast cancer.

MATERIALS AND METHODS

This retrospective study reviewed 21 breast cancer patients who scheduled for surgical resection performed CEUS real-time guided SLN-FNA and intraoperative SLN biopsy (SLNB). The success rate of CEUS real-time guided SLN-FNA was analyzed. The FNA diagnostic efficiency of SLN metastasis was analyzed compared to SLNB.

RESULTS

Twenty-six SLNs were detected by intradermal CEUS whereas 130 SLNs were detected by SLNB. The median SLNs detected by intradermal CEUS (n = 1) and by SLNB (n = 5) was significantly difference (p < 0.001). All 26 CE-SLNs of 21 patients were successfully performed intradermal CEUS dual image real-time guided SLN-FNA including 5 SLNs of 4 patients which were difficult to distinguish in conventional ultrasound. Compared to SLNB, FNA found 2 of 5 cases of SLN metastasis, the diagnosis sensitivity, specificity, positive predictive value, negative predictive value, false negative rate, false positive rate and Yoden index were 40%, 100%, 100%, 84.2%, 60%, 0%and 40%, respectively.

CONCLUSION

SLN-FNA real-time guided by dual CEUS image mode was technically feasible. Patients with a positive SLN-FNA should be advised to ALND without intraoperative SLNB according to Chinese surgeon and patients' conservatism attitude. But a negative SLN-FNA did not obviate the need of conventional SLNB because of the high false negative rate.

The Role of US in Depicting Axillary Metastasis in High-Risk Breast Cancer Patients

Purpose: The aim of this study is to evaluate the role of US in depicting axillary nodal disease in high-risk patients with and without pathogenic mutations. Methods: The retrospective study included consecutive high-risk breast cancer (BC) patients who underwent a multigene testing panel for hereditary cancers, pre-operative axillary US and breast/axillary surgery. The group was divided into patients with pathogenic mutations (PM group) and patients without PM. Statistical analyses were performed using GraphPad Prism by applying Chi-square and Fisher exact tests, with a reference p-value < 0.05 and a CI of 95%. Results: Out of 190 patients with BC, 96 (51%) were negative and 94 (49%) were positive for PM as follows: 28 (25.5%) BRCA1, 16 (17%) BRCA2, 15 (16%) CHECK2, 14 (14%) RAD Group, 7 (7%) PALB, 6 (6%) NBN, 3 (3%) TP53 and ATM and 2 (2%) BARD1. US was positive in 88 of the patients, 36 with PM and 52 without PM. US and surgery (≥N1 stage) were both positive in 31 (62%) of PM patients and 44 (88%) of patients without genetic changes. There were 19 (61%) false negative US examinations in the PM group and 6 (13%) in the group without genetic changes, respectively. If the US is positive, there is a 2.6 times greater risk of positive nodes in PM patients (p-value < 0.000, 95% CI = 4.2–37.9), and a 6.2 times greater risk of positive nodes in patients without genetic changes (p-value < 0.000, 95%CI = 8.4–37.4). In the PM group, US compared to surgery reached a sensitivity = 62, with PPV = 86 and NPV = 67. In the BRCA1/2 subgroup, there is 2.5 greater times risk of nodal disease if the US is positive (p-value = 0.001, 95%CI = 2.6–76). In patients without PM, US compared to surgery reached a sensitivity = 88, PPV = 84 and NPV = 86. Conclusion: US is more sensitive in depicting axillary nodal disease in high-risk patients without PM compared to PM patients. Furthermore, there are more false negative US examinations in PM patients, compared to surgery patients.

Is image-guided core needle biopsy of borderline axillary lymph nodes in breast cancer patients clinically helpful?

BACKGROUND

When borderline axillary lymph nodes (bALN) are identified on ultrasound (US) for breast cancer (BC) patients, preoperative management is unclear. We aimed to evaluate if core needle biopsy (CNB) for bALN is clinically helpful or disruptive.

METHODS

Retrospective review of BC patients with bALN from 2014 to 2019 was performed. Clinicopathologic data were compared for those who did and did not have CNB.

RESULTS

CNB (n = 34) and no CNB (n = 31) were similar with respect to clinicopathologic factors. Surgical LN-positive rate was the same between cohorts (p = 0.26). CNB was disruptive in 58.8 %; all had CNB for pN0 disease. CNB was helpful in 34.2 %: 14.7 % proceeded directly to axillary dissection; 17.6 % had positive LN localized after neoadjuvant chemotherapy.

CONCLUSIONS

CNB for bALN is more likely clinically disruptive and did not impact surgical LN positive rate. BC patients with bALN should undergo CNB only if it will change clinical management.

Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients

Objective

To develop a nomogram for predicting axillary lymph node (ALN) metastases using the breast imaging reporting and data system (BI-RADS) ultrasound lexicon.

Methods

A total of 703 patients from July 2015 to January 2018 were included in this study as a primary cohort for model construction. Moreover, 109 patients including 51 pathologically confirmed N1 patients (TNM staging) and 58 non-metastatic patients were recruited as an external validation cohort from March 2018 to August 2019. Ultrasound images and clinical information of these patients were retrospectively reviewed. The ultrasonic features based on the BI-RADS lexicon were extracted by two radiologists. The features extracted from the primary cohort were used to develop a nomogram using multivariate analysis. Internal and external validations were performed to evaluate the predictive efficacy of the nomogram.

Results

The nomogram was based on two features (size, lesion boundary) and showed an area under the curve of 0.75 (95% confidence interval [CI], 0.70–0.79) in the primary cohort and 0.91 (95% CI, 0.84–0.97) in the external validation cohort; it achieved an 88% sensitivity in N1 patients.

Conclusion

The nomogram based on BI-RADS ultrasonic features can predict breast cancer ALN status with relatively high accuracy. It has potential clinical value in improving the sensitivity and accuracy of the preoperative diagnosis of ALN metastases, especially for N1 patients.

Risk factors for axillary lymph node metastases in clinical stage T1-2N0M0 breast cancer patients

Axillary lymph node metastasis (ALNM) is commonly the earliest detectable clinical manifestation of breast cancer when distant metastasis emerges. This study aimed to explore the influencing factors of ALNM and develop models that can predict its occurrence preoperatively.Cases of sonographically visible clinical stage T1-2N0M0 breast cancers treated with breast and axillary surgery at West China Hospital were retrospectively reviewed. Univariate and multivariate logistic regression analyses were performed to evaluate associations between ALNM and variables. Decision tree analyses were performed to construct predictive models using the C5.0 packages.Of the 1671 tumors, 541 (32.9%) showed axillary lymph node positivity on final surgical histopathologic analysis. In multivariate logistic regression analysis, tumor size (P < .001), infiltration of subcutaneous adipose tissue (P < .001), infiltration of the interstitial adipose tissue (P = .031), and tumor quadrant locations (P < .001) were significantly correlated with ALNM. Furthermore, the accuracy in the decision tree model was 69.52%, and the false-negative rate (FNR) was 74.18%. By using the error-cost matrix algorithm, the FNR significantly decreased to 14.75%, particularly for nodes 5, 8, and 13 (FNR: 11.4%, 9.09%, and 14.29% in the training set and 18.1%,14.71%, and 20% in the test set, respectively).In summary, our study demonstrated that tumor lesion boundary, tumor size, and tumor quadrant locations were the most important factors affecting ALNM in cT1-2N0M0 stage breast cancer. The decision tree built using these variables reached a slightly higher FNR than sentinel lymph node dissection in predicting ALNM in some selected patients.

Ultrasound Features of Breast Cancer for Predicting Axillary Lymph Node Metastasis

OBJECTIVES

The purpose of this study was to assess the clinical value of ultrasound (US) features of breast lesions for predicting the risk of axillary lymph node metastasis in patients with breast cancer.

METHODS

In this retrospective study, 425 patients with breast cancer were recruited, and their preoperative US features and postoperative pathologic results were collected. The association of these US features of breast cancer with axillary lymph node metastasis was determined by univariate and multivariate analyses.

RESULTS

Among the 425 patients, 200 (47.1%) had axillary lymph node metastasis, and 225 (52.9%) did not. The parameters of tumor shape, color Doppler flow imaging grades, histologic grade, and E-cadherin level were significantly and independently associated with axillary lymph node metastasis (P < .05 for all).

CONCLUSIONS

Axillary lymph node metastasis was prone to happen in patients with US features of an irregular tumor shape and higher color Doppler flow imaging grades. Ultrasound imaging provides a promising tool for predicting axillary lymph node metastasis in patients with breast cancer.

Comparison of 3D and 2D shear-wave elastography for differentiating benign and malignant breast masses: focus on the diagnostic performance

AIM

To evaluate the diagnostic performance of three-dimensional (3D) image shear-wave elastography (SWE) for differentiating benign from malignant breast masses compared to two-dimensional (2D) SWE and B-mode ultrasound (US).

MATERIALS AND METHODS

This study consisted of 205 breast lesions from 199 patients who underwent B-mode US and SWE before biopsy from January 2014 to March 2016. Quantitative elasticity values (maximum and mean elasticity, Emax and Emean) obtained from 2D and 3D SWE (axial, sagittal, and coronal images) were reviewed retrospectively, in addition to the histopathological findings including immunohistochemistry profiles (luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative breast cancer) in cases of malignancy. Histopathological findings were regarded as the reference standard. The diagnostic performance of each data set was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) analysis to compare sensitivity and specificity.

RESULTS

Among 205 lesions, 105 (51.22%) were malignant and 100 (48.78%) were benign. Compared to benign masses, malignant masses had higher values of Emax and Emean on both 2D and 3D SWE, the differences of which were statistically significant (p<0.001). The AUCs of 2D, 3D axial, and sagittal SWE were significantly higher than that of 3D coronal SWE (p<0.05). In addition, the sensitivities of axial, sagittal, and coronal 3D SWE were all higher than that of 2D SWE for Emean (81.9%, 87.6%, and 89.5% versus 70.5%, respectively, p<0.05). Conversely, the specificity of 2D and 3D axial SWE was higher than that of 3D sagittal and coronal SWE (Emax, 84%, 83% versus 76%, 73%; Emean, 85%, 81% versus 68%, 50%, respectively, p<0.05). We also assessed changes in Breast Imaging-Reporting and Data System (BI-RADS) category 3 and category 4a lesions by adding each of the parameters for 2D and 3D SWE in B-mode US. The specificity, PPV, and accuracy of combined 2D or combined 3D SWE with B-mode US was statistically higher than that of B-mode US alone for differentiating benign and malignant lesions (p<0.05).

CONCLUSIONS

Among SWE images, 2D SWE, and 3D SWE axial and sagittal images exhibited superior diagnostic performance compared to 3D coronal images. Addition of 3D SWE images to B-mode US improved the diagnostic performance for distinguishing benign from malignant masses.