Characteristics of the sentinel lymph node in breast cancer predict further involvement of higher-echelon nodes in the axilla: a study to evaluate the need for complete axillary lymph node dissection

Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review

Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.

Sentinel lymph node biopsy and immediate two-stage implant-based breast reconstruction: A propensity score-matched analysis

BACKGROUND

There are important differences between patients requiring sentinel lymph node biopsy (SLNB) and those who do not require axillary surgery at the time of breast reconstruction. We aimed to perform a propensity score-matched analysis to evaluate the impact of SLNB at the time of immediate implant-based breast reconstruction (IBBR) with tissue expanders compared with IBBR alone.

METHODS

Consecutive female patients undergoing total mastectomy and immediate two-stage IBBR between January 2011 and May 2021 were included. A 1:1 nearest-neighbor matching method without replacement was implemented with a caliper width of 0.01. Patients were matched for age, diabetes, hypertension, hyperlipidemia, premastectomy radiotherapy, neoadjuvant chemotherapy, the plane of prosthesis placement, mastectomy specimen weight, number of drains, and radiation of the expander.

RESULTS

We included 320 two-stage immediate IBBRs after propensity score matching, 160 reconstructions per group. Relevant surgical variables were comparable between groups. A higher rate of 30-day seroma formation was reported in immediate reconstructions that had SLNB at the time of mastectomy compared with reconstructions that did not have axillary surgery (16.3% versus 8.1%, p = 0.039). The time to complete outpatient expansions and time for expander-to-implant exchange were comparable between patients who underwent IBBRs with SLNB and those who did not.

CONCLUSION

SLNB performed at the time of mastectomy and IBBR with tissue expander increased the risk of seroma formation compared with reconstructions that did not have axillary surgery. The rate of infection, hematoma, and unplanned procedures to manage complications did not differ between groups.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

Background

For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images.

Methods

In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740.

Findings

For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model.

Interpretation

The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively.

Funding

National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI

BACKGROUND

Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear.

PURPOSE

To investigate the potential of the proposed attention-based DL model for the preoperative differentiation of ALN metastasis in breast cancer on dynamic contrast-enhanced MRI (DCE-MRI).

STUDY TYPE

Retrospective.

POPULATION

A total of 941 breast cancer patients who underwent DCE-MRI before surgery were included in the training (742 patients), internal test (83 patients), and external test (116 patients) cohorts.

FIELD STRENGTH/SEQUENCE

A 3.0 T MR scanner, DCE-MRI sequence.

ASSESSMENT

A DL model containing a 3D deep residual network (ResNet) architecture and a convolutional block attention module, named RCNet, was proposed for ALN metastasis identification. Three RCNet models were established based on the tumor, ALN, and combined tumor-ALN regions on the images. The performance of these models was compared with ResNet models, radiomics models, the Memorial Sloan-Kettering Cancer Center (MSKCC) model, and three radiologists (W.L., H.S., and F. L.).

STATISTICAL TESTS

Dice similarity coefficient for breast tumor and ALN segmentation. Accuracy, sensitivity, specificity, intercorrelation and intracorrelation coefficients, area under the curve (AUC), and Delong test for ALN classification.

RESULTS

The optimal RCNet model, that is, RCNet-tumor+ALN , achieved an AUC of 0.907, an accuracy of 0.831, a sensitivity of 0.824, and a specificity of 0.837 in the internal test cohort, as well as an AUC of 0.852, an accuracy of 0.828, a sensitivity of 0.792, and a specificity of 0.853 in the external test cohort. Additionally, with the assistance of RCNet-tumor+ALN , the radiologists' performance was improved (external test cohort, P < 0.05).

DATA CONCLUSION

DCE-MRI-based RCNet model could provide a noninvasive auxiliary tool to identify ALN metastasis preoperatively in breast cancer, which may assist radiologists in conducting more accurate evaluation of ALN status.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Predictive Factors for Unnecessary Axillary Dissection According to SLN Metastasis in T1, T2 Stage Breast Cancer

The axillary nodes' status is essential in determining the treatment algorithm according to complete clinical staging. Unnecessary axillary lymph node dissection (ALND) has been prevented after sentinel lymph node biopsy (SLNB) has occurred in current practice. However, approximately half of patients with positive SLNB do not have axillary metastatic lymph nodes. Our study aims to predict unnecessary ALND in patients with SLN metastases by evaluating the patients' clinicopathological data. In total, 221 patients with macrometastasis in SLNB who underwent completion ALND were evaluated retrospectively. Patients were divided into two groups: patients with metastases only in the sentinel lymph node and additional axillary lymph nodes. Univariate and multivariate logistic regression analyses were used to analyze the correlation between SLN metastasis and axillary lymph node metastasis; clinicopathological characteristics, including patient age, menopause status, tumor size and grade, receptor status proliferative marker status, and molecular subtypes of the tumor. In the evaluation of T1-2, cN0 breast cancer patients with SLNB in the form of macrometastasis, only SLNB metastasis was found in 118 (53.4%) patients. In 103 (46.6%) patients, additional axillary node metastasis was observed. The risk of additional nodal spread correlated with patient age older than fertility age (age of 49) (p = 0.015, OR: 1.96, 95% CI: 1.14-3.39) and the number of increased metastatic sentinel nodes (p < 0.001). In line with the data shown by our study, the rate of axillary metastases increases in patients over the age of fertility and as the number of metastatic SLNs increases.

Quantitative Indicators of Retraction Phenomenon on an Automated Breast Volume Scanner: Initial Study in the Diagnosis and Prognostic Prediction of Breast Tumors

Retraction phenomenon is a unique sign on an automated breast volume scanner coronal plane image and has high specificity in differentiating benign lesions from malignant breast cancer. The purpose of this study was to quantify the retraction phenomenon by setting different rules to describe connected regions from different dimensions. In total, six quantitative indicators (F_Ω1,F_Γ,F_S,F_Ω2,F_Ω3and F_L) were obtained. F_Ω1, F_Ω2 and F_Ω3 represent the relative areas of the connected region under different rules. F_ΓandF_S represent the number ratio and absolute area of the connected region, respectively. F_L represents the ratio of edge numbers. Two hundred fourteen patients with 214 lesions (90 benign and 124 malignant) were enrolled in this study. All quantitative indicators in the malignant group were significantly higher than those in the benign group (all p values <0.001). The indicator F_Γ achieved the highest area under the receiver operating characteristic curve (AUC) (0.701, 95% confidence interval: 0.631-0.771). Both F_Γ and F_S had significant associations with axillary lymph node metastasis (p = 0.023 and 0.049). Compared with the classic texture feature gray-level co-occurrence matrix, retraction phenomenon quantization improved the AUC by 8.3%. The results indicate that retraction phenomenon quantitative indicators have certain value in distinguishing benign and malignant breast lesions and seem to be associated with axillary lymph node metastasis.

Predictive factors of non-sentinel lymph node disease in breast cancer patients with positive sentinel lymph node

INTRODUCTION

Management of positive sentinel lymph node biopsy (SLNB) in breast cancer remains a matter of debate. Our aim was to evaluate the incidence and identify predictive factors of non-sentinel lymph node metastases.

METHODS

Retrospective review of all cN0 breast cancer patients treated between January 2013 and December 2017, with positive SLNB that were submitted to ALND.

RESULTS

Of the 328 patients included, the majority of tumors were cT1 or cT2, with lymphovascular invasion in 58.4% of cases. The mean isolated nodes in SLNB was 2.7, with a mean of 1.6 positive nodes, 60.7% with extracapsular extension. Regarding ALND, a mean of 13.9 nodes were isolated, with a mean of 2.1 positive nodes. There was no residual disease in the ALND in 50.9% of patients, with 18.9% having ≥4 positive nodes. In the multivariate analysis, lymphovascular invasion, extracapsular extension in SLN, largest SLN metastases size (>10 mm) and ratio of positive SNL (>50%) were independent predictors of non-sentinel lymph node metastases. These four factors were used to build a non-pondered score to predict the probability of a positive ALND after a positive SLNB. The AUC of the model was 0.69 and 81% of patients with score = 0 and 65.6% with score = 1 had no additional disease in ALND.

CONCLUSION

The absence of non-sentinel lymph node metastases in the majority of patients with 1-2 positive SLN with low risk score questions the need of ALND in this population. The identified predictive factors may help select patients in which ALND can be omitted.

A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases

Background

We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients.

Methods

We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set.

Results

Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P = 0.011).

Conclusions

This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.

Quantitative analysis of shear wave elastic heterogeneity for prediction of lymphovascular invasion in breast cancer

OBJECTIVE

To evaluate the correlation between elastic heterogeneity (EH) and lymphovascular invasion (LVI) in breast cancers and assess the clinical value of using EH to predict LVI pre-operatively.

METHODS

This retrospective study consisted of 376 patients with breast cancers that had undergone shear wave elastography (SWE) with virtual touch tissue imaging quantification between June 2017 and June 2018. The EH was determined as the difference between the averaged three highest and three lowest shear wave value. Clinicalpathological parameters including histological type and grades, LVI, axillary lymph node status and molecular markers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 and Ki-67) were reviewed and recorded. Relationship EH and clinicalpathological parameters was investigated respectively. The diagnostic performance of EH in distinguishing LVI or not was analyzed.

RESULTS

At multivariate regression analysis, only EH (p = 0.017) was positively correlated with LVI in all tumors. EH (p = 0.003) and Ki-67 (p = 0.025) were positively correlated with LVI in tumors ≤ 2 cm. None of clinicalpathological parameters were correlated with LVI in tumors > 2 cm (p > 0.05 for all). Using EH to predict LVI in tumors ≤ 2 cm, the sensitivity and negative predictive value were 93 and 89% respectively.

CONCLUSION

EH has the potential to be served as an imaging biomarker to predict LVI in breast cancer especially for tumors ≤ 2 cm.

ADVANCES IN KNOWLEDGE

There was no association between LVI and other most commonly used elastic features such as SWV_mean and SWV_max. Elastic heterogeneity is an independent predictor of LVI, so it can provide additional prognostic information for routine preoperative breast cancer assessment.For tumors ≤ 2cm, using EH value higher than 1.36 m/s to predict LVI involvement, the sensitivity and negative predictive value can reach to 93% and 89%, respectively, suggesting that breast cancer with negative EH value was more likely to be absent of LVI.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

OBJECTIVES

To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.

METHODS

Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.

RESULTS

SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort.

CONCLUSION

The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.

KEY POINTS

• Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Metastatic Breast Cancer, Organotropism and Therapeutics: A Review

The final stage of breast cancer involves spreading breast cancer cells to the vital organs like the brain, liver lungs and bones in the process called metastasis. Once the target organ is overtaken by the metastatic breast cancer cells, its usual function is compromised causing organ dysfunction and death. Despite the significant research on breast cancer metastasis, it's still the main culprit of breast cancer-related deaths. Exploring the complex molecular pathways associated with the initiation and progression of breast cancer metastasis could lead to the discovery of more effective ways of treating the devastating phenomenon. The present review article highlights the recent advances to understand the complexity associated with breast cancer metastases, organotropism and therapeutic advances.

Lymph nodal radiotherapy in breast cancer: what are the unresolved issues?

Introduction: Sentinel lymph node biopsy (SLNB) is the gold standard in invasive breast cancer. Axillary dissection (ALND) is controversial in some presentations.Areas covered: Key questions were formulated and explored focused on four different scenarios in adjuvant axillary radiation management in early and locally advanced breast cancer. Answers to these questions were searched in MEDLINE, PubMed from June 1946 to August 2020. Clinical trials, retrospective studies, international guidelines, meta-analysis, and reviews were explored.Expert opinion: Analysis according to biological disease characteristics is necessary to establish the impact of ALND avoidance in unexpectedly positive SLNB (pN1) in cN0 patients. A low-risk probability of axillary recurrence was observed if axillary radiotherapy (ART) or ALND were offered without impact on outcomes. Adjuvant RNI in pT1-3 pN1 treated with mastectomy or BCS should be proposed in unfavorable disease and risk factors. In ycN0 after NACT, SLNB can be offered in selected cases or ALND should be performed. After SLNB post-NACT (ypN1), ALND and adjuvant radiotherapy are mandatory.

Predictive factors of non-sentinel lymph node disease in breast cancer patients with positive sentinel lymph node

INTRODUCTION

Management of positive sentinel lymph node biopsy (SLNB) in breast cancer remains a matter of debate. Our aim was to evaluate the incidence and identify predictive factors of non-sentinel lymph node metastases.

METHODS

Retrospective review of all cN0 breast cancer patients treated between January 2013 and December 2017, with positive SLNB that were submitted to ALND.

RESULTS

Of the 328 patients included, the majority of tumors were cT1 or cT2, with lymphovascular invasion in 58.4% of cases. The mean isolated nodes in SLNB was 2.7, with a mean of 1.6 positive nodes, 60.7% with extracapsular extension. Regarding ALND, a mean of 13.9 nodes were isolated, with a mean of 2.1 positive nodes. There was no residual disease in the ALND in 50.9% of patients, with 18.9% having ≥ four positive nodes. In the multivariate analysis, lymphovascular invasion, extracapsular extension in SLN, largest SLN metastases size (>10 mm) and ratio of positive SNL (> 50%) were independent predictors of non-sentinel lymph node metastases. These four factors were used to build a non-pondered score to predict the probability of a positive ALND after a positive SLNB. The AUC of the model was 0.69 and 81% of patients with score = 0 and 65.6% with score = 1 had no additional disease in ALND.

CONCLUSION

The absence of non-sentinel lymph node metastases in the majority of patients with 1-2 positive SLN with low risk score questions the need of ALND in this population. The identified predictive factors may help select patients in which ALND can be omitted.

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

Breast cancer is frequently diagnosed using ultrasound. Here, the authors show that, in addition to ultrasound, shear wave elastography can be used to diagnose breast cancer and, in conjunction with deep learning and radiomics, can predict whether the disease has spread to axillary lymph nodes.

Nomograms for predicting the likelihood of non-sentinel lymph node metastases in breast cancer patients with a positive sentinel node biopsy

BACKGROUND

Breast cancer patients with sentinel lymph node (SLN) metastases may have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the non-SLN metastases can avoid many complications mentioned the axillary lymph node dissection (ALND). This study aims to develop a new model based on Chinese real-world patients to ascertain the likelihood of non-SLN metastases in a breast cancer patient with disease-positive SLN, enabling the surgeons to make a better choice of surgical procedures.

METHODS

Out of the 470 patients from CSCO Breast Cancer Database collaborated Group, a proportion of 3 (347 cases): 1 (123 cases) was considered for assigning patients to training and validation groups, respectively. Two training models were created to predict the likelihood of having additional, non-SLN metastases in an individual patient. Training model 1 was created with pathological size of the tumor, pathological type, lymphovascular invasion, the number of positive SLNs/number of total SLNs ratio, and the Her-2 status based on multivariable logistic regression (P < .05). Training model 2 was based on the variables in model 1 and age, estrogen receptor status, progesterone receptor status, Ki-67 count, menopause status.

RESULTS

The area under the receiver operating characteristic (ROC) curve of the training model 1 was 0.754, while the area of training model 2 was 0.766. There was no difference between model 1 and model 2 regarding the ROC curve, P = .243. Next, the validation cohort (n = 123) was developed to confirm the model 1's performance and the ROC curve was 0.703. The nomogram achieved good concordance indexes of 0.754 (95% CI, 0.702-0.807) and 0.703 (95% CI, 0.609-0.796) in predicting the non-SLN metastases in the training and validation cohorts, respectively, with well-fitted calibration curves. The positive and negative predictive values of the nomogram were calculated, resulting in positive values of 59.3% and 48.6% and negative predictive values of 79.7% and 83.0% for the training and validation cohorts, respectively.

CONCLUSION

We developed 2 models that used information commonly available to the surgeon to calculate the likelihood of having non-SLN metastases in an individual patient. The numbers of variables in model 1 were less than in model 2, while model 1 had similar results as model 2 in calculating the likelihood of having non-SLN metastases in an individual patient. Model 1 was more user-friendly nomogram than model 2. Using model 1, the risk for an individual patient having ALND could be determined, which would lead to a rational therapeutic choice.

Is Low-Volume Disease in the Sentinel Node After Neoadjuvant Chemotherapy an Indication for Axillary Dissection?

BACKGROUND/OBJECTIVE

Intraoperative evaluation of sentinel lymph nodes (SLNs) after neoadjuvant chemotherapy (NAC) has a higher false-negative rate than in the primary surgical setting, particularly for small tumor deposits. Additional tumor burden seen with isolated tumor cells (ITCs) and micrometastases following primary surgery is low; however, it is unknown whether the same is true after NAC. We examined the false-negative rate of intraoperative frozen section (FS) after NAC, and the association between SLN metastasis size and residual disease at axillary lymph node dissection (ALND).

METHODS

Patients undergoing SLN biopsy after NAC were identified. The association between SLN metastasis size and residual axillary disease was examined.

RESULTS

From July 2008 to July 2017, 702 patients (711 cancers) had SLN biopsy after NAC. On FS, 181 had metastases, 530 were negative; 33 negative cases were positive on final pathology (false-negative rate 6.2%). Among patients with a positive FS, 3 (2%) had ITCs and no further disease on ALND; 41 (23%) had micrometastases and 125 (69%) had macrometastases. Fifty-nine percent of patients with micrometastases and 63% with macrometastases had one or more additional positive nodes at ALND. Among those with a false-negative result, 10 (30%) had ITCs, 15 (46%) had micrometastases, and 8 (24%) had macrometastases; 17 had ALND and 59% had one or more additional positive lymph nodes. Overall, 1/6 (17%) patients with ITCs and 28/44 (64%) patients with micrometastases had additional nodal metastases at ALND.

CONCLUSION

Low-volume SLN disease after NAC is not an indicator of a low risk of additional positive axillary nodes and remains an indication for ALND, even when not detected on intraoperative FS.

Validation of Memorial Sloan Kettering Cancer Center nomogram to detect non-sentinel lymph node metastases in a United Kingdom cohort

AIM

Axillary lymph node dissection, although associated with long-term morbidity, has been the standard of treatment for all nodepositive breast cancer patients. We assessed the risk prediction ability (validity) of Memorial Sloan Kettering Cancer Center (MSKCC) nomogram for non-sentinel lymph node metastases and analysed the outcome of patients with sentinel node metastases.

PATIENTS AND METHODS

All operable early breast cancer patients with sentinel node macro metastases (size > 2mm) who underwent axillary dissection from April 2009 to March 2015 were considered eligible. The risk of non-sentinel lymph node metastases was calculated using an online MSKCC calculator, and accuracy was determined based on the area under the receiver-operating characteristic curve (AUC-ROC). Tumour characteristics and overall survival were also analysed as secondary end points.

RESULTS

Of 1745 patients who were diagnosed with operable breast cancer during the study period, 114 patients were considered eligible. The AUC-ROC was 0.66 suggestive of lesser accuracy in prediction and not statistically significant (p value = 0.7303). Seventysix (50.7%) of these patients did not have any non-sentinel node metastases. At a mean follow up of four years, the disease-free survival was 86.4% and overall survival rate was 88.4%.

CONCLUSIONS

The MSKCC nomogram was unable to accurately predict the risk in our cohort of patients with more than half of this cohort of patients not requiring axillary dissection. These findings are consistent with other European studies. This study thus highlights the need for modified prediction model for European cohorts.

Organotropism: new insights into molecular mechanisms of breast cancer metastasis

Metastasis accounts for 90% of breast cancer mortality. Despite the significant progress made over the past decade in cancer medicine our understanding of metastasis remains limited, therefore preventing and targeting metastasis is not yet possible. Breast cancer cells preferentially metastasize to specific organs, known as “organotropic metastasis”, which is regulated by subtypes of breast cancer, host organ microenvironment, and cancer cells-organ interactions. The cross-talk between cancer cells and host organs facilitates the formation of the premetastatic niche and is augmented by factors released from cancer cells prior to the cancer cells’ arrival at the host organ. Moreover, host microenvironment and specific organ structure influence metastatic niche formation and interactions between cancer cells and local resident cells, regulating the survival of cancer cells and formation of metastatic lesions. Understanding the molecular mechanisms of organotropic metastasis is essential for biomarker-based prediction and prognosis, development of innovative therapeutic strategy, and eventual improvement of patient outcomes. In this review, we summarize the molecular mechanisms of breast cancer organotropic metastasis by focusing on tumor cell molecular alterations, stemness features, and cross-talk with the host environment. In addition, we also update some new progresses on our understanding about genetic and epigenetic alterations, exosomes, microRNAs, circulating tumor cells and immune response in breast cancer organotropic metastasis.

A cut-off of 2150 cytokeratin 19 mRNA copy number in sentinel lymph node may be a powerful predictor of non-sentinel lymph node status in breast cancer patients

Since 2007, one-step nucleic acid amplification (OSNA) has been used as a diagnostic system for sentinel lymph node (SLN) examination in patients with breast cancer. This study aimed to define a new clinical cut-off of CK19 mRNA copy number based on the calculation of the risk that an axillary lymph node dissection (ALND) will be positive. We analyzed 1529 SLNs from 1140 patients with the OSNA assay and 318 patients with positive SLNs for micrometastasis (250 copies) and macrometastasis (5000 copies) underwent ALND. Axillary non–SLNs were routinely examined. ROC curves and Youden’s index were performed in order to identify a new cut-off value. Logistic regression models were performed in order to compare OSNA categorical variables created on the basis of our and traditional cut-off to better identify patients who really need an axillary dissection. 69% and 31% of OSNA positive patients had a negative and positive ALND, respectively. ROC analysis identified a cut-off of 2150 CK19 mRNA copies with 95% sensitivity and 51% specificity. Positive and negative predictive values of this new cut-off were 47% and 96%, respectively. Logistic regression models indicated that the cut-off of 2150 copies better discriminates patients with node negative or positive in comparison with the conventional OSNA cut-off (p<0.0001). This cut-off identifies false positive and false negative cases and true-positive and true negative cases very efficiently, and therefore better identifies which patients really need an ALND and which patients can avoid one. This is why we suggest that the negative cut-off should be raised from 250 to 2150. Furthermore, we propose that for patients with a copy number that ranges between 2150 and 5000, there should be a multidisciplinary discussion concerning the clinical and bio-morphological features of primary breast cancer before any decision is taken on whether to perform an ALND or not.

The Feasibility and Accuracy of Sentinel Lymph Node Biopsy in Initially Clinically Node-Negative Breast Cancer after Neoadjuvant Chemotherapy: A Systematic Review and Meta-Analysis

Background

With the increased use of neoadjuvant chemotherapy (NAC) in breast cancer, the timing of sentinel lymph node biopsy (SLNB) has become increasingly important. In this study, we aimed to evaluate the feasibility and accuracy of SLNB for initially clinically node-negative breast cancer after NAC by conducting a systematic review and meta-analysis.

Methods

We searched PubMed, Embase, and the Cochrane Library from January 1, 1993 to November 30, 2015 for studies on initially clinically node-negative breast cancer patients who underwent SLNB after NAC followed by axillary lymph node dissection (ALND).

Results

A total of 1,456 patients from 16 studies were included in this review. The pooled identification rate (IR) for SLNB was 96% [95% confidence interval (CI): 95%-97%], and the false negative rate (FNR) was 6% (95% CI: 3%-8%). The pooled sensitivity, negative predictive value (NPV) and accuracy rate (AR) were 94% (95% CI: 92%-97%, I² = 27.5%), 98% (95% CI: 98%-99%, I² = 42.7%) and 99% (95% CI: 99%-100%, I² = 32.6%), respectively. In the subgroup analysis, no significant differences were found in either the IR of an SLNB when different mapping methods were used (P = 0.180) or in the FNR between studies with and without immunohistochemistry (IHC) staining (P = 0.241).

Conclusion

Based on current evidence, SLNB is technically feasible and accurate enough for axillary staging in initially clinically node-negative breast cancer patients after NAC.

Factors Influencing Non-sentinel Node Involvement in Sentinel Node Positive Patients and Validation of MSKCC Nomogram in Indian Breast Cancer Population

Current guidelines recommend completion axillary lymphnode dissection (ALND) when sentinel lymphnode (SLN) contains metastatic tumor deposit. In consequent ALND sentinel node is the only node involved by tumor in 40-70 % of cases. Recent studies demonstrate the oncologic safety of omitting completion ALND in low risk patients. Several nomograms (MSKCC, Stanford, MD Anderson score, Tenon score) had been developed in predicting the likelihood of additional nodes metastatic involvement. We evaluated accuracy of MSKCC nomogram and other clinicopathologic variables associated with additional lymph node metastasis in our patients. A total of 334 patients with primary breast cancer patients underwent SLN biopsy during the period Jan 2007 to June 2014. Clinicopathologic variables were prospectively collected. Completion ALND was done in 64 patients who had tumor deposit in SLN. The discriminatory accuracy of nomogram was analyzed using Area under Receiver operating characteristic curve (ROC). SLN was the only node involved with tumor in 69 % (44/64) of our patients. Additional lymph node metastasis was seen in 31 % (20/64). On univariate analysis, extracapsular infiltration in sentinel node and multiple sentinel nodes positivity were significantly associated (p < 0.05) with additional lymph node metastasis in the axilla. Area under ROC curve for nomogram was 0.58 suggesting poor performance of the nomogram in predicting NSLN involvement. Sentinel nodes are the only nodes to be involved by tumor in 70 % of the patients. Our findings indicate that multiple sentinel node positivity and extra-capsular invasion in sentinel node significantly predicted the likelihood of additional nodal metastasis. MSKCC nomogram did not reliably predict the involvement of additional nodal metastasis in our study population.

Intradermal microbubbles and contrast-enhanced ultrasound (CEUS) is a feasible approach for sentinel lymph node identification in early-stage breast cancer

BACKGROUND

Microbubbles and contrast-enhanced ultrasound (CEUS) is a new technique for locating sentinel lymph node (SLN). The aim of this study is to explore the feasibility of SLNs tracing by CEUS using microbubbles in breast cancer patients and the value of enhancing patterns in diagnosing lymph nodes metastases.

METHODS

A clinical trial was registered (trial registration: ChiCTR-DDT-13003778). One hundred and one consecutive consenting patients with breast cancer undergoing SLN biopsy were enrolled. Before the surgery, microbubble was injected periareolarly. Lymphatic drainage pathway was detected by CEUS, and guidewire was deployed to locate the SLN before the operation. Blue dye was also used to help in tracing sentinel lymph node during the operation. The identification rate and the accuracy rate were recorded. Enhancing patterns of lymph nodes were recorded and compared with the pathological diagnosis.

RESULTS

Of the 101 cases, SLNs in 99 cases were successfully identified by at least one tracer, including 98 cases identified by CEUS and 97 cases by blue dye. There was no significant difference between the two methods (P = 0.705). Guidewires were deployed successfully in all 98 cases, and the localized SLNs were all isolated successfully in the following operations. The status of SLNs isolated by CEUS was completely identical to that of the whole axillary lymph node while 7.1 % cases were misdiagnosed as negative by blue dye method. The sensitivity of predicting SLNs metastases by CEUS enhancing pattern was 81.8 %, the specificity was 86.2 %, and the positive and negative predictive values were 75.0 and 90.3 %, respectively.

CONCLUSIONS

Microbubbles and CEUS are feasible approaches for SLN identification. The enhancing patterns on CEUS may be helpful to recognize the metastasizing SLNs. This novel method may be a promising technique for the clinical application.

Elaboration of a nomogram to predict non sentinel node status in breast cancer patients with positive sentinel node, intra-operatively assessed with one step nucleic acid amplification method

BACKGROUNDS

Tumor-positive sentinel node(SLN) biopsy results in a risk of nonsentinel node metastases in case of micro and macro metastases ranging from 20 to 50 %, respectively. Therefore, most patients underwent unnecessary axillary lymph node dissections. Thus, the development of a mathematical model for predicting patient-specific risk of non sentinel node(NSLN) metastases is strongly warranted.

METHODS

The following parameters were recorded:

CLINICAL

hospital, age, medical record number Bio-pathological: tumor (T) size, grading (G), multifocality, histological type, LVI, ER-PR status, HER-2, ki67, molecular classification (luminal A, luminal B, HER2 like, triple negative) Sentinel and nonsentinel lymph node related: number of removed SLNs, number of positive and negative SLNs, copy number of positive sentinel nodes, ratio: number of positive SLNs to number of removed SLNs, number of removed and number of positive nodes after ALND. A total of 2460 patients have been included in the database. All the patients have been provided by the authors of this paper.

RESULTS

Multivariate logistic regression analysis demonstrated that only the number of a CK19 mRNA copies (p < 0.0001), T size (p < 0.0001) and LVI (p < 0.0001) were associated with NSN metastases. The discrimination of the model, quantified with the area under the receiver operating characteristics curve, was 0.71 (95 %, C.I. 0.69-0.73), thus confirming a good level of reliability.

CONCLUSIONS

The nomogram may be employed by the surgeon as a decision making tool on whether to perform an intraoperative axillary lymph node dissection on breast cancer patients with SLN positive. The large population employed and the standardized method of measuring the value of CK19 mRNA copies are appropiate prerequisites for a reliable nomogram.

Comparison of five different popular scoring systems to predict nonsentinel lymph node status in patients with metastatic sentinel lymph nodes: a tertiary care center experience

Sentinel lymph node biopsy (SLNB) is the current standard of care for breast cancers with no clinically palpable axillary lymph nodes. Almost 50 % of sentinel lymph node positive patients have negative non-sentinel nodes and undergo non-therapeutic axillary dissection. Five different scoring systems, reported in the literature, were compared for their predictive ability of non-SLN involvement in patients with SLN positive breast cancer. 242 patients who underwent breast surgery and SLNB were included in the study. Of these, 70 who were confirmed to have SLN metastasis and received complementary ALND and constituted the final study population. The nomograms (MSKCC, M.D. Anderson Cancer Center, Tenon model, Stanford and Turkish) were statistically compared for their prediction of non-SLN metastasis (95 % confidence interval). We have determined only two clinicopathologic (multifocality and size of the primary tumor) situations which have a statistically significant association between SLN metastasis with using a multivariate logistic regression analysis. Multifocality (P = 0.001) and size of the primary tumor (P = 0.001) were associated with a higher probability of-SLN metastasis. No predictive model was constructed that showed good area under the curve (AUC) discrimination in the validation series. Currently published predictive models lack accuracy when applied to a different population. Multi-institutional heterogenic population studies are important to determine the exact combination of scoring systems and/or nomograms.

A new model for predicting non-sentinel lymph node status in Chinese sentinel lymph node positive breast cancer patients

BACKGROUND

Our goal is to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Stanford Online Calculator (SOC) for predicting non-sentinel lymph node (NSLN) metastasis in Chinese patients, and develop a new model for better prediction of NSLN metastasis.

METHODS

The MSKCC nomogram and SOC were used to calculate the probability of NSLN metastasis in 120 breast cancer patients. Univariate and multivariate analyses were performed to evaluate the relationship between NSLN metastasis and clinicopathologic factors, using the medical records of the first 80 breast cancer patients. A new model predicting NSLN metastasis was developed from the 80 patients.

RESULTS

The MSKCC and SOC predicted NSLN metastasis in a series of 120 patients with an area under the receiver operating characteristic curve (AUC) of 0.688 and 0.734, respectively. For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively. Tumor size, Kiss-1 expression in positive SLN and size of SLN metastasis were independently associated with NSLN metastasis (p<0.05). A new model (Peking University People's Hospital, PKUPH) was developed using these three variables. The MSKCC, SOC and PKUPH predicted NSLN metastasis in the second 40 patients from the 120 patients with an AUC of 0.624, 0.679 and 0.795, respectively.

CONCLUSION

MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients. As a new predictor, Kiss-1 expression in positive SLN correlated independently with NSLN metastasis strongly. PKUPH model achieved higher accuracy than MSKCC and SOC in predicting NSLN metastasis in Chinese patients.

Histologic evaluation of sentinel and non-sentinel axillary lymph nodes in breast cancer by multilevel sectioning and predictors of non-sentinel metastasis

Sentinel lymph node (SLN) provides accurate nodal staging for breast cancer. This technique has been introduced in Siriraj Hospital since 1998. The goal of this study is to assess its accuracy in predicting the state of the axilla, and compare the results of standard examination and multilevel sectioning. A retrospective analysis of 195 breast cancer patients who underwent both SLN biopsy (using dye alone as the lymphatic mapping) and axillary node dissection during 1998-2002 were analyzed. All slides including SLNs and the non-SLNs (NSLNs) were reviewed and multilevel study was performed on all SLNs and NSLNs [four levels of hematoxylin-eosin (HE) at 200 µm interval and keratin stains on the first and fourth levels]. Of 195 patients, 30% of cases were SLN-positive (32 NSLN-positive and 27 NSLN-negative). Additional study could detect positive axillary nodes 10.8% (4 SLN-positive and 5 NSLN-positive) more than standard HE stain. The false negative rate increased from 20.3% to 24.1%. The concordance between SLN and NSLN statuses was 89.7%. The sensitivity was 75.9%. By multivariate analysis, the significant predictors for axillary node metastasis were tumor size of more than 2.2 cm, histologic type of invasive ductal carcinoma (IDC), not otherwise specified (NOS) and lymphovascular invasion (LVI). By univariable analysis, the significant predictors of NSLN metastasis after positive-SLN were outer location of the tumor, LVI and perinodal extension. In conclusion, use of multilevel and immunohistochemistry increased detection of positive-SLNs. Caution should be kept in accepting SLN biopsy using peritumoral dye technique alone as the procedure for staging due to a high false-negative rate. The concordance rate of 89.7% confirmed the reliability of SLN. Outer location of tumor, LVI and perinodal extension is significant predictors of positive-NSLN after positive-SLN.

A new clinical cut-off of cytokeratin 19 mRNA copy number in sentinel lymph node better identifies patients eligible for axillary lymph node dissection in breast cancer

AIMS

Cytokeratin 19 (CK19) mRNA copy number predicts the probability of tumour load in axillary lymph nodes (ALN) and can help in decision-making regarding the axillary dissection. The purpose of this study was to define a new cut-off of CK19 mRNA copy number using the one-step nucleic acid amplification (OSNA) assay on metastatic sentinel lymph nodes (SLN) in order to identify cases at risk of having one or more positive ALN.

METHODS

1296 SLN from 1080 patients were analysed with the OSNA assay. 194 patients with positive SLN underwent ALN dissection and the mean value of CK19 copy number (320 000) of their SLN was set as initial cut-off. Receiver operative characteristics curve identify a best cut-off of 7700 (sensitivity 78%, specificity 57%). A comparison between our and the traditional cut-off (5000) was performed.

RESULTS

The cut-off of 7700 successfully identifies patients with positive ALN (p=0.001, false- negative cases: 17%). In the range between 5000 and 7700, one patient with positive ALN would not undergo axillary dissection, whereas eight patients with negative ALN would be correctly identified.

CONCLUSIONS

We suggest that the level of CK19 mRNA copy number could be the only parameter to consider in the intraoperative management of the axilla.

Axillary ultrasonography in breast cancer patients helps in identifying patients preoperatively with limited disease of the axilla

BACKGROUND

The sentinel lymph node biopsy (SLNB) procedure is the method of choice for the identification and monitoring of regional lymph node metastases in patients with breast cancer. In the case of a positive sentinel lymph node (SLN), additional lymph node dissection is still warranted for regional control, although 40-65 % have no additional axillary disease. Recent studies show that after breast-conserving surgery, SLNB, and adjuvant systemic therapy, there is no significant difference between recurrence-free period and overall survival if there are ≤2 positive axillary nodes. The purpose of this study was preoperative identification of patients with limited axillary disease (≤2 macrometastases) by using ultrasonography.

METHODS

Data from 1,103 consecutive primary breast cancer patients with tumors smaller than 50 mm, no palpable adenopathy, and a maximum of 2 SLNs with macrometastases were collected. The variable of interest was US of the axilla.

RESULTS

Of the 1,103 patients included, 1,060 remained after exclusion criteria. Of these, 102 (9.6 %) had more than 2 positive axillary nodes on ALND. Selected by unsuspected US, the chance of having >2 positive lymph nodes (LNs) is substantially lower (4.2 %). This is significant on univariate and multivariate analysis. After excluding the patients with extracapsular extension of the SLN, the chance of having >2 positive LNs is only 2.6 %. For pT1-2, this is 2.2 %.

CONCLUSIONS

The risk of more than 2 positive axillary nodes is relatively small in patients with cT1-2 breast cancer. US of the axilla helps in further identifying patients with a minimal risk of additional axillary disease, putting ALND up for discussion.

A new and simple predictive formula for non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes, and validation of 3 different nomograms in Turkish breast cancer patients

BACKGROUND

Nomogram accuracies for predicting non-sentinel lymph node (SLN) involvement vary between different patient populations. Our aim is to put these nomograms to test on our patient population and determine our individual predictive parameters affecting SLN and non-SLN involvement.

PATIENTS AND METHODS

Data from 932 patients was analyzed. Nomogram values were calculated for each patient utilizing MSKCC, Tenon, and MHDF models. Moreover, using our own patient- and tumor-depended parameters, we established a unique predictivity formula for SLN and non-SLN involvement.

RESULTS

The calculated area under the curve (AUC) values for MSKCC, Tenon, and MHDF models were 0.727 (95% confidence interval (CI) 0.64-0.8), 0.665 (95% CI 0.59-0.73), and 0.696 (95% CI 0.59-0.79), respectively. Cerb-2 positivity (p = 0.004) and size of the metastasis in the lymph node (p = 0.006) were found to correlate with non-SLN involvement in our study group. The AUC value of the predictivity formula established using these parameters was 0.722 (95% CI 0.63-0.81).

CONCLUSION

The most accurate nomogram for our patient group was the MSKCC nomogram. Our unique predictivity formula proved to be as equally effective and competent as the MSKCC nomogram. However, similar to other nomograms, our predictivity formula requires future validation studies.

An independent assessment of the 7 nomograms for predicting the probability of additional axillary nodal metastases after positive sentinel lymph node biopsy in a cohort of British patients with breast cancer

INTRODUCTION

Axillary lymph node dissection (ALND) is currently the recommended procedure in patients with tumor-positive sentinel lymph node biopsy (SLNB). A significant proportion of patients with positive SLNs will not have any additional metastases in nonsentinel lymph nodes (NSLNs). Predictive nomograms could identify a subgroup of patients with low or high risk of further disease in whom completion ALND can be avoided or recommended. The aim of this study was to assess the accuracy of the currently available 7 nomograms in a cohort of British patients with breast cancer.

PATIENTS AND METHODS

A total of 138 patients with positive SLNs who underwent completion ALND were identified. Data were then used to calculate the probability of further metastases in NSLNs predicted by the 7 nomograms that are currently in use: the MSKCC (Memorial Sloan Kettering Cancer Center), Cambridge, Turkish, Stanford, MDACC (University of Texas MD Anderson Cancer Center), Tenon, and MOU (Masarykuv onkologický ústav, Masaryk Memorial Cancer Institute) models. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for each nomogram.

RESULTS

Of the 138 patients, 54 (41%) had additional metastases in NSLNs. AUC values for the MSKCC, Cambridge, Turkish, Stanford, MDACC, Tenon, and MOU models are 0.68, 0.68, 0.70, 0.69, 0.56, 0.63, and 0.74, respectively.

CONCLUSION

The MOU nomogram was more predictive than the other nomograms, with a better AUC value and false-negative rate. None of the models were able to achieve AUC value ≥ 0.80 in a cohort of British patients with breast cancer.

Evolution of axillary nodal staging in breast cancer: clinical implications of the ACOSOG Z0011 trial

BACKGROUND

Management of the axilla in breast cancer patients has evolved from routine axillary lymph node dissection (ALND) for all patients to a highly selective approach based on the assessment of the sentinel lymph nodes (SLNs) as well as tumor and patient characteristics. Although ALND continues to have an important role in staging and regional control for many breast cancer patients, recent trial results question the need for routine ALND in patients who have positive SLNs.

METHODS

Not all axillary disease becomes clinically detectable or relevant with respect to recurrence and survival. Therefore, recent trends indicate that many surgeons have omitted ALND in subgroups of patients, particularly those with clinically node-negative, SLN-positive, early-stage breast cancer undergoing breast-conserving therapy with postoperative irradiation. This review explores trends in axillary management, focusing primarily on the clinical implications of the results from the American College of Surgeons Oncology Group (ACOSOG) Z0011 randomized controlled trial.

RESULTS

According to the results of the ACOSOG Z0011 trial, the use of SLN dissection alone did not result in inferior survival compared with ALND in patients with limited SLN disease treated with breast-conserving therapy. This subgroup of women was spared the morbidity associated with ALND. However, several points of debate, including the smaller than anticipated sample size, the older study population, and the length of follow-up, suggest caution when applying these findings to all women with breast cancer.

CONCLUSIONS

Although the findings of ACOSOG Z0011 are impressive, in clinical practice they are applicable to a limited number of women with breast cancer: those with T1-2 primary tumors with clinically negative axilla and 1 to 2 positive SLNs undergoing breast-conserving surgery and adjuvant whole-breast irradiation. The next generation of clinical trials may answer some of the remaining questions regarding how best to manage the axilla in additional subsets of patients undergoing treatment of breast cancer.

Quantitation of sentinel node metastatic burden and Her-2/neu over-expression accurately predicts residual axillary nodal involvement and extranodal disease in breast cancer

BACKGROUND DATA

Recent literature has suggested that completion axillary lymph node dissection (ALND) in breast carcinoma patients with positive SLN may not be necessary. However, a method for determining the risk of non-SLN or extranodal disease remains to be established.

AIMS

To determine if pathological variables from primary tumors and sentinel lymph node (SLN) metastases could predict the probability of non-sentinel lymph node (NSLN) metastases and extranodal disease in patients with breast carcinoma and SLN metastases.

METHODS

84 women with T1-3 breast cancer and clinically-negative axillae underwent completion ALND. Maximum diameter and width of SLN metastases were measured to calculate metastatic area. When multiple SLNs contained metastases, areas were summed to calculate the Total Metastatic Area (TMA). Multiple linear regression models were used to identify predictive factors.

RESULTS

Her-2/neu over-expression increased the odds of NSLN metastases (OR 4.3, p = 0.01) and extranodal disease (OR 7.9, p < 0.001). Independent SLN predictors were ≥1 positive SLN (OR, 7.35), maximum diameter and area of SLN metastases (OR 2.26, 1.85 respectively) and TMA (OR, 2.12). Maximum metastatic diameter/SLN diameter (OR 3.71, p = 0.04) and the area of metastases/SLN area (OR 3.4, p = 0.04) were predictive. For every 1 mm increase in diameter of SLN metastases, the odds of NSLN extranodal disease increased by 8.5% (p = 0.02). TMA >0.40 cm(2) was an independent predictor for NSLN metastases and extranodal disease.

CONCLUSION

Her-2/neu over-expression and parameters assessing metastatic burden in the SLN, particularly TMA, predicted the presence of NSLN involvement and extranodal disease in patients with breast carcinoma and SLN metastases.

Omission of axillary dissection according to ACOSOG Z0011: impact on adjuvant treatment recommendations

OBJECTIVE

A recent, randomized trial (ACOSOG Z0011) has demonstrated that omission of completion axillary lymph node dissection (ALND) in patients with one or two sentinel lymph node (SLN) metastases treated with breast conserving therapy (BCT) does not have a negative impact on survival. This study evaluates the impact of omitting ALND on adjuvant treatment recommendations.

METHODS

Performing a search of our clinical database, we identified patients meeting the main inclusion and exclusion criteria of ACOSOG Z0011 treated at the University of Heidelberg Breast Center. We performed blinded mock interdisciplinary tumor boards based on patient and tumor characteristics as well as (1) SLN information or (2) final nodal status after ALND. Differences between treatment recommendations were noted and analyzed.

RESULTS

A total of 132 patients were included; 80.3 % of these had one and 19.7 % had two metastatic sentinel nodes with a rate of micrometastases only of 19.7 %, and 39.7 % of patients had additional nonsentinel node metastases upon ALND. Overall, there was a change in adjuvant chemotherapy in 18.2 % of cases. Treatment recommendations based on ALND lead to a more aggressive therapy in 16.6 % of cases, all of them with additional metastatic nonsentinel nodes. Chemotherapy was not recommended in only two cases (1.5 %) based on ALND. Based on ALND, irradiation of the supraclavicular and infraclavicular nodes was added in 5.3 % of patients.

CONCLUSIONS

Completion ALND for patients with one or two metastatic sentinel nodes in pT1-2 cN0 PBC treated with BCT does have a relevant impact on adjuvant treatment. This should be considered in shared decision making.

A logistic regression model for predicting axillary lymph node metastases in early breast carcinoma patients

Nodal staging in breast cancer is a key predictor of prognosis. This paper presents the results of potential clinicopathological predictors of axillary lymph node involvement and develops an efficient prediction model to assist in predicting axillary lymph node metastases. Seventy patients with primary early breast cancer who underwent axillary dissection were evaluated. Univariate and multivariate logistic regression were performed to evaluate the association between clinicopathological factors and lymph node metastatic status. A logistic regression predictive model was built from 50 randomly selected patients; the model was also applied to the remaining 20 patients to assess its validity. Univariate analysis showed a significant relationship between lymph node involvement and absence of nm-23 (p = 0.010) and Kiss-1 (p = 0.001) expression. Absence of Kiss-1 remained significantly associated with positive axillary node status in the multivariate analysis (p = 0.018). Seven clinicopathological factors were involved in the multivariate logistic regression model: menopausal status, tumor size, ER, PR, HER2, nm-23 and Kiss-1. The model was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.702 when applied to the validation group. Moreover, there is a need discover more specific candidate proteins and molecular biology tools to select more variables which should improve predictive accuracy.

Lymph node metastasis in patients with frozen section analyses that are negative for tumors

OBJECTIVES

This study was designed to assess the necessity of delayed complete axillary lymph node dissection (cALND) for patients whose sentinel lymph nodes (SLNs) were negative for tumors on intraoperative frozen section analysis, but later proven positive on hematoxylin and eosin staining or immunohistochemistry.

METHODS

We identified 341 patients who underwent sentinel lymph node biopsy (SLNB) with cALND at the Samsung Medical Center between 1998 and 2008, and reviewed the clinicopathological records of women diagnosed with invasive carcinoma of the breast.

RESULTS

Of the 341 patients, 59 underwent delayed cALND due to negative results on frozen section. Only 1 patient had a non-SLNs metastasis in the group of delayed cALND. Delayed cALND was associated with higher rates of breast-conserving surgery, smaller primary tumor and metastasis size in SLNs, fewer metastatic lymph nodes and SLNs and a lower TNM stage. The detection of metastases of SLNs on frozen section and the number of metastatic SLNs were related to the detection of additional metastases of nonsentinel lymph nodes (NSLNs) in cALND.

CONCLUSION

Our findings suggest that the lack of detection of metastases on frozen sections may be a predictive factor for nonmetastasis in NSLNs. cALND could therefore be omitted in such cases.

Factors predicting the non-sentinel lymph node involvement in breast cancer patients with sentinel lymph node metastases

OBJECTIVE

In a significant proportion of patients, the sentinel lymph node (SLN) is the only involved axillary node. The goal of the present study was to identify predictive factors associated with a positive SLN and with a positive non-SLN in patients in whom axillary lymph node dissection (ALND) was performed.

METHODS

Data was reviewed for patients with T1-2 invasive breast cancer who underwent SLN biopsy with or without axillary dissection in a single institution between July 2000 and May 2010. The SLNs were examined by serial sectioning and H&E staining, and by cytokeratin immunostaining in suspicious cases.

RESULTS

Of 332 patients with SLNB, 134 had SLN positivity, and 116 of them further underwent completion axillary dissection. Patients with T2 tumors (OR=3.2; 95% CI, 1.74-5.58), or tumors with lymphovascular invasion (OR=8.0; 95% CI, 4.44-14.27), or invasive ductal cancer (OR=2.92; 95% CI, 1.1-8.0) were more likely to have a positive SLN. In patients with ALND, the non-SLN involvement rates were 10%, 11.5% and 50% in patients with isolated tumor cells (ITC), micrometastasis and macrometastasis, respectively. Finding of ITC or micrometastasis in SLNs (OR=0.28; 95% CI, 0.08-0.99) or presence of extracapsular invasion (ECI) in SLN (OR=0.24; 95% CI, 0.09-0.67) were the predictive factors of not having a non-SLN metastasis in logistic regression analysis.

CONCLUSIONS

These findings suggest further axillary surgery can be best omitted in patients with micrometastasis while validation of nomograms including factors such as ECI are still needed to be studied in patients with macrometastasis.