Comparison of three mathematical models for predicting the risk of additional axillary nodal metastases after positive sentinel lymph node biopsy in early breast cancer

Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes

This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection.

Sentinel Node Biopsy in Early Breast Cancer Patients with Palpable Axillary Node

BACKGROUND

Sentinel lymph node biopsy is a reliable method for evaluation of the axillary lymph node status in early stage breast cancer patients with non-palpable lymph nodes. The present study evaluated the status of sentinel and non-sentinel lymph nodes in T1T2 patients with palpable axillary lymph nodes.

MATERIALS AND METHODS

One hundred and two women with early breast cancer were investigated in this study. Patients were selected for axillary sentinel lymph node biopsy and then surgery .Then the rates of false negative and true positive, and diagnostic accuracy of sentinel lymph nodes biopsy were evaluated. In addition, the hormone receptors status of the tumor was determined through IHC and data was analyzed in SPSS21.

RESULTS

In this study, the mean age of the patients was 49 years, 85% had invasive ductal carcinoma  in their pathology reports, 77% were ER/PR positive, 30% HER2 positive and 9.8% triple negative and 69% had KI67<14%. In frozen pathology, 15.7 and 84.3% were sentinel positive and negative, respectively, and in the final pathology, 41 and 58.8% were sentinel positive and negative, respectively. This difference arises from the false negative rate of the frozen pathology, which was about 31.3%. The sensitivity, specificity, and diagnostic accuracy of the frozen section were 24, 90 and 43%, respectively. Lymphovascular invasion is an important effective factor in the involvement of sentinel and non-sentinel lymph nodes. Statistical analysis showed that the probability of sentinel and non-sentinel lymph nodes involvement was higher in receptor positive patients and those with KI67>14% (p<0.002) whereas the rate of involvement was lower in triple negative patients.

CONCLUSION

Sentinel node biopsy can be used in a significant percentage of breast cancer patients with palpable and reactive axillary lymph nodes.

Imaging Predictors for Nonsentinel Lymph Node Metastases in Breast Cancer Patients

Background

The relationship between imaging features and nonsentinel lymph node (NSLN) metastasis is not clear.

Objectives

To determine whether imaging features could predict NSLN metastasis in sentinel lymph node (SLN)-positive breast cancer patients and to provide new clues for avoiding unnecessary axillary lymph node dissection.

Method

171 patients with clinically negative axillary lymph nodes and a pathologically positive SLN were recruited between January 2007 and January 2014. According to the Breast Imaging Reporting and Data System (BI-RADS), the effects of clinicopathological factors, especially imaging features, on NSLN metastases were assessed by univariate and multivariate statistical analyses.

Results

The average number of dissected SLNs was 2.11 (range, 1-6); 56 of the 171 (32.75%) patients exhibited NSLN metastases. In univariate analysis, tumor size, number of positive SLNs, ratio of positive SLNs, mammographic mass margins, ultrasonographic mass margins, and ultrasonographic vascularity were significantly correlated with NSLN involvement. Furthermore, through multivariate analysis, tumor size, number of positive SLNs, mammographic mass margins, and ultrasonographic vascularity were still independent predictors of NSLN involvement. Additionally, in SLN-positive patients, number of positive SLNs and ultrasonographic vascularity could also predict the tumor burden in NSLN.

Conclusions

In addition to tumor size and the number of positive SLNs, mammographic mass margins and ultrasonographic vascularity were also independent predictors of NSLN metastases in SLN-positive patients of breast cancer. The number of positive SLNs and ultrasonographic vascularity could also predict the tumor burden in NSLN.

Nomograms for preoperative prediction of axillary nodal status in breast cancer

Background

Axillary staging in patients with breast cancer and clinically node‐negative disease is performed by sentinel node biopsy (SLNB). The aim of this study was to integrate feasible preoperative variables into nomograms to guide clinicians in stratifying treatment options into no axillary staging for patients with non‐metastatic disease (N0), SLNB for those with one or two metastases, and axillary lymph node dissection (ALND) for patients with three or more metastases.

Methods

Patients presenting to Skåne University Hospital, Lund, with breast cancer were included in a prospectively maintained registry between January 2009 and December 2012. Those with a preoperative diagnosis of nodal metastases were excluded. Patients with data on hormone receptor status, human epidermal growth factor receptor 2 and Ki‐67 expression were included to allow grouping into surrogate molecular subtypes. Based on logistic regression analyses, nomograms summarizing the strength of the associations between the predictors and each nodal status endpoint were developed. Predictive performance was assessed using the area under the receiver operating characteristic (ROC) curve. Bootstrap resampling was performed for internal validation.

Results

Of the 692 patients eligible for analysis, 248 were diagnosed with node‐positive disease. Molecular subtype, age, mode of detection, tumour size, multifocality and vascular invasion were identified as predictors of any nodal disease. Nomograms that included these predictors demonstrated good predictive abilities, and comparable performances in the internal validation; the area under the ROC curve was 0·74 for N0versus any lymph node metastasis, 0·70 for one or two involved nodes versusN0, and 0·81 for at least three nodes versus two or fewer metastatic nodes.

Conclusion

The nomograms presented facilitate preoperative decision‐making regarding the extent of axillary surgery.

Defines need for staging?

Role of Combined Sentinel Lymph Node Biopsy and Axillary Node Sampling in Clinically Node-Negative Breast Cancer

Axillary lymph node status is a prognostic marker in breast cancer management, and axillary surgery plays an important role in staging and local control. This study aims to assess whether a combination of sentinel lymph node biopsy (SLNB) using patent blue dye and axillary node sampling (ANS) offers equivalent identification rate to dual tracer technique. Furthermore, we aim to investigate whether there are any potential benefits to this combined technique. Retrospective study of 230 clinically node-negative patients undergoing breast-conserving surgery for single T1-T3 tumours between 2006 and 2011. Axillae were staged using a combined blue dye SLNB/ANS technique. SLNs were localized in 226/230 (identification rate 98.3 %). Three of one hundred ninety-two patients with a negative SLN were found to have positive ANS nodes and 1/4 failed SLNB patients had positive ANS nodes. Thirty-four of two hundred twenty-six patients had SLN metastases and 11/34 (32.4 %) also had a positive non-sentinel lymph node on ANS. Twenty-one of twenty-four (87.5 %) node-positive T1 tumours had single node involvement. Nine of thirty-eight node-positive patients progressed to completion axillary clearance (cALND), and the rest were treated with axillary radiotherapy. Axillary recurrence was nil at median 5 year follow-up. Complementing SLNB with axillary node sampling (ANS) decreases the unavoidable false-negative rate associated with SLNB. Appropriate operator experience and technique can result in an SLN localization rate of 98 %, rivalling a dual tracer technique. The additional insight offered by ANS into the status of non-sentinel nodes has potential applications in an era of less frequent cALND.

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.

Axillary nodal metastases in Italian early breast cancer patients with positive sentinel lymph node: can axillary node dissection be avoided by using predictive nomograms?

AIMS AND BACKGROUND

Clinical guidelines recommend axillary lymph node dissection (ALND) in cases of metastatic sentinel lymph node (SNL) in patients with clinically node-negative early breast cancer. However, a relevant number of ALND could be avoided in a subset of patients in whom the risk of non-SNL metastases is low. In order to define this population, several authors have proposed mathematical models, which have been validated in many studies. These studies reached different conclusions regarding which model demonstrated the best statistical discrimination power, mainly due to differences in clinical and pathologic variables used, and particularly differences in the number of dissected SLNs.

METHODS

We retrospectively reviewed clinically node-negative patients who underwent ALND in our surgical ward after the diagnosis of breast cancer metastases on SLN biopsy from January 2000 to December 2012. The predictive accuracy of the widely used nomograms to predict the risk of additional nodal disease in our patients with SLN breast cancer metastases was measured by receiver operating characteristic curve. We then attempted to develop a new nomogram by analyzing the dataset.

RESULTS

A total of 105 patients were included in this study, with ratio of metastatic lymph node/removed lymph node of about 0.89; we found axillary nodal metastases on ALND in only 31 patients (29.5%). Applied to our dataset, Mayo nomogram showed the best area under the receiving operator characteristic curve (0.74) followed by our model (0.71). Instead, the Memorial Sloan-Kettering model showed poor discrimination, as did Tenon (0.56).

CONCLUSIONS

Based on our data, we cannot recommend the clinical use of validated predictive nomograms in order to avoid ALND. We suggest setting up a multicenter Italian study to build a model specific to our setting and based on larger series.

Subtype is a predictive factor of nonsentinel lymph node involvement in sentinel node-positive breast cancer patients

Purpose

This study aimed to identify the effect of breast cancer subtype on nonsentinel lymph node (NSLN) metastasis in patients with a positive sentinel lymph node (SLN).

Methods

The records of 104 early breast cancer patients with a positive SLN between April 2009 and September 2013 were retrospectively evaluated. All patients underwent axillary lymph node dissection. The effects of the tumor subtype (luminal A, luminal/HER2+, human epidermal growth factor receptor 2 [HER2] overexpression, and triple-negative) and other clinicopathological factors on NSLN metastasis were examined by univariate and multivariate statistical analyses.

Results

Fifty of 104 patients (48%) exhibited NSLN metastasis. Univariate and multivariate analyses revealed that tumor size and the ratio of positive SLNs were significant risk factors of NSLN metastasis in patients with a positive SLN. The rate of NSLN metastasis was higher in patients with luminal/HER2+ and HER2 overexpression subtypes than that in patients with other subtypes in the univariate analysis (p<0.001). In the multivariate analysis, both patients with luminal/HER2+ (p<0.006) and patients with HER2 overexpression (p<0.031) subtypes had a higher risk of NSLN metastasis than patients with the luminal A subtype.

Conclusion

Subtype classification should be considered as an independent factor when evaluating the risk of NSLN metastasis in patients with a positive SLN. This result supports the development of new nomograms including breast cancer subtype to increase predictive accuracy.

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.

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.

Sentinel lymph node biopsy in breast cancer: predictors of axillary and non-sentinel lymph node involvement

BACKGROUND

Sentinel lymph node biopsy is a standard method for the evaluation of axillary status in patients with T1-2N0M0 breast cancers.

AIMS

To determine the prognostic significance of primary tumour-related clinico-histopathological factors on axillary and non-sentinel lymph node involvement of patients who underwent sentinel lymph node biopsy.

STUDY DESIGN

Retrospective clinical study.

METHODS

In the present study, 157 sentinel lymph node biopsies were performed in 151 consecutive patients with early stage breast cancer between June 2008 and December 2011.

RESULTS

Successful lymphatic mapping was obtained in 157 of 158 procedures (99.4%). The incidence of larger tumour size (2.543±1.21 vs. 1.974±1.04), lymphatic vessel invasion (70.6% vs. 29.4%), blood vessel invasion (84.2% vs. 15.8%), and invasive lobular carcinoma subtype (72.7% vs. 27.3%) were statistically significantly higher in patients with positive SLNs. Logistic stepwise regression analysis disclosed tumour size (odds ratio: 1.51, p=0.0021) and lymphatic vessel invasion (odds ratio: 4.68, p=0.001) as significant primary tumour-related prognostic determinants of SLN metastasis.

CONCLUSION

A close relationship was identified between tumour size and lymphatic vessel invasion of the primary tumour and axillary lymph node involvement. However, the positive predictive value of these two independent variables is low and there is no compelling evidence to recommend their use in routine clinical practice.

Prognostic factors in elderly patients with breast cancer

Background

Breast cancer (BC) remains principally a disease of old ages; with 35-50% of cases occurring in women older than 65 years. Even mortality for cancer increases with aging: 19.7% between 65 and 74 years; 22.6% between 75 and 84 years; and 15.1% in 85 years or more.

The study was aimed to investigate specific predictive factors for elderly patients so to select the best way to treat and follow these patients.

Methods

A search was performed on Medline, Embase, Scopus using the following Key words: Breast cancer, Breast neoplasms, Aged, Elder, Elderly, Eldest, Older, Survival analysis, Prognosis, Prognostic factors, Tumor markers, Biomarkers, Comorbidity, Geriatric assessment, Axilla, Axillary surgery. 3029 studies have been retrieved. Paper in which overall or disease free survival were not end points, or age class was not well defined, or the sample was too small, were excluded. At last 42 papers fulfilled the criteria.

Results and discussion

Lack of screening and delay in diagnosis may be responsible for the minor improvement in survival observed in elderly respect to younger breast cancer patients. Predictive factors are the same and must be assessed with the same attention reserved to younger women.

Conclusions

Most of elderly patient are fit to undergo standard treatment and can get the same benefits of younger women. Nevertheless it is possible that some older women with early breast cancer can be spared too aggressive treatments. Geriatric assessment and co-morbidities can affect the prognosis modifying surveillance, life expectancy and compliance to therapies. They can thus be useful to select the better treatment, either surgical or radio or hormone - or chemo-therapy.

Predicting the likelihood of additional lymph node metastasis in sentinel lymph node positive breast cancer: validation of the Memorial Sloan-Kettering Cancer Centre (MSKCC) nomogram

AIM

To identify important clinicopathological parameters that are most helpful in predicting additional non-sentinel lymph node (SLN) metastasis among patients with a positive SLN biopsy in the Singapore breast cancer population.

METHODS

A total of 1409 patients who underwent SLN biopsy were reviewed over a 5 year period from July 2004 to October 2009. A Singapore General Hospital (SGH) nomogram was developed from predictors in the Memorial Sloan-Kettering Cancer Centre (MSKCC) nomogram using 266 patients with primary invasive breast cancer and a positive SLN biopsy who subsequently had an axillary lymph node dissection. The SGH nomogram was calibrated using bootstrapped data, while the MSKCC nomogram was calibrated using SGH data. The performance of these two nomograms was compared with the calculation of the area under the receiver-operator characteristics curve and adequacy indices.

RESULTS

The MSKCC nomogram achieved an area under the curve (AUC) of 0.716 (range 0.653-0.779) in our study population, while the SGH nomogram, which used only three pathological parameters, lymphovascular invasion, number of positive and negative SLN biopsies, achieved an AUC of 0.750 (range 0.691-0.808). The SGH nomogram with a higher adequacy index (0.969) provided better estimates compared with the MSKCC nomogram (0.689).

CONCLUSIONS

The use of the MSKCC nomogram was validated in our local patient population. The SGH nomogram showed promise to be equally, if not, more predictive as a model in our own population, while using only three pathological parameters.

Which nomogram is best for predicting non-sentinel lymph node metastasis in breast cancer patients? A meta-analysis

To present a systematic [corrected] review and meta-analysis to evaluate the nomograms developed to predict non-sentinel lymph node (NSLN) metastasis in breast cancer patients. We focused on the six nomograms (Cambridge, MSKCC, Mayo, MDA, Tenon, and Stanford) that are the most widely validated. The AUCs were converted to odds ratios for the meta-analysis. In total, the Cambridge, Mayo, MDA, MSKCC, Stanford, and Tenon models were validated in 2,156, 2,431, 843, 8,143, 3,700, and 3,648 patients, respectively. The pooled AUCs for the Cambridge, MDA, MSKCC, Mayo, Tenon, and Stanford models were 0.721, 0.706, 0.715, 0.728, 0.720, and 0.688, respectively. Subgroup analysis revealed that in populations with a higher micrometastasis rate in the SLNs, the Tenon and Stanford models had a significantly higher predictive accuracy. A meta-regression analysis revealed that the SLN micrometastasis rate, but not the NSLN-positivity rate, was associated with improved predictive accuracy in the Tenon and Stanford models. The performance of the MSKCC and Cambridge models was not influenced by these two factors. All of these prediction models perform better than random chance. The Stanford model seems to be relatively inferior to the other models. The accuracy of the Tenon and Stanford models is influenced by the tumor burden in the SLNs.

Molecular subtype classification is a determinant of non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes

Background

Previous studies suggested that the molecular subtypes were strongly associated with sentinel lymph node (SLN) status. The purpose of this study was to determine whether molecular subtype classification was associated with non-sentinel lymph nodes (NSLN) metastasis in patients with a positive SLN.

Methodology and Principal Findings

Between January 2001 and March 2011, a total of 130 patients with a positive SLN were recruited. All these patients underwent a complete axillary lymph node dissection. The univariate and multivariate analyses of NSLN metastasis were performed. In univariate and multivariate analyses, large tumor size, macrometastasis and high tumor grade were all significant risk factors of NSLN metastasis in patients with a positive SLN. In univariate analysis, luminal B subgroup showed higher rate of NSLN metastasis than other subgroup (P = 0.027). When other variables were adjusted in multivariate analysis, the molecular subtype classification was a determinant of NSLN metastasis. Relative to triple negative subgroup, both luminal A (P = 0.047) and luminal B (P = 0.010) subgroups showed a higher risk of NSLN metastasis. Otherwise, HER2 over-expression subgroup did not have a higher risk than triple negative subgroup (P = 0.183). The area under the curve (AUC) value was 0.8095 for the Cambridge model. When molecular subtype classification was added to the Cambridge model, the AUC value was 0.8475.

Conclusions

Except for other factors, molecular subtype classification was a determinant of NSLN metastasis in patients with a positive SLN. The predictive accuracy of mathematical models including molecular subtype should be determined in the future.

A simple nomogram to evaluate the risk of nonsentinel node metastases in breast cancer patients with minimal sentinel node involvement

BACKGROUND

Tumor-positive sentinel node biopsy (SNB) suggests a risk of nonsentinel node metastases in breast cancer. This risk is lower after micrometastasis or isolated tumor cells (ITC) in the sentinel node (SN), and recent studies suggest that completion axillary lymph node dissection (ALND) might not improve outcome in these patients. We aim to validate existing predictive models and to develop a new model for micrometastatic and ITC patients.

METHODS

A series of 484 patients with micrometastases or ITC in SN followed by ALND was used to evaluate factors affecting nonsentinel node involvement. Logistic regression analysis was performed to construct a predictive model, which was validated by a separate series of 51 patients.

RESULTS

Only 7.2% of patients had additional metastases on completion ALND. Tumor diameter and multifocality associated with nonsentinel status on multivariate analysis. A predictive model was constructed showing good [area under the curve (AUC) 0.791] discrimination in the validation series. Previously published models performed poorly in our patient population.

CONCLUSIONS

Nonsentinel node metastases are rare with micrometastasis or ITC in SN. Most published predictive models for nonsentinel node involvement perform poorly in the present patient population. We developed a new predictive model which seems to perform well in discriminating patients with more than 10% risk of additional metastases. However, the presented nomogram needs to be validated with an independent patient series to evaluate its accuracy, especially for high-risk patients.