Prediction of tumour involvement in remaining axillary lymph nodes when the sentinel node in a woman with breast cancer contains metastases

Consistent Learning-Based Breast Tumor Segmentation and Its Application in Sentinel Lymph Node Metastasis Prediction

Accurate staging of lymph nodes provides crucial diagnostic information for breast cancer patients, where segmentation is of great importance by localizing and visualizing the breast tumor of interest. Nevertheless, current segmentation methods perform average when facing large span of tumor sizes, degraded image quality, blurred tumor boundaries, and resulting noise during manual annotation. Therefore, we develop a Multi-scale RepVGG-based Segmentation Network (MPSegNet) to segment breast tumor from MR images. In particular, we construct a consistent learning framework for the MPSegNet to alleviate the impact of noisy labels upon segmentation results. The rationale is that different views covering the same breast tumors are supposed to generate identical segmentation predictions. Then, we predict SLN metastasis given segmented breast tumors, where we evaluate the relationships between the predictive performance and tumor segmentations under different consistencies. The results show the superiority of our method over other state-of-the-art methods. A high consistency among multiple views can boost the segmentation performance during consistent learning. However, the optimal segmentation does not produce the best SLN metastatic prediction results, implying that the dependence of classification upon segmentation needs to be elaborately investigated further.Clinical Relevance- This study facilitates more accurate segmentation of breast tumors with consistent learning, and provides an initial analysis between tumor segmentation and subsequent prediction of SLN metastasis, which has potential significance for the precise medical care of breast cancer patients.

Toward Exempting from Sentinel Lymph Node Biopsy in T1 Breast Cancer Patients: A Retrospective Study

Background and Objective

Sentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram.

Methods

A total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram.

Results

In the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort.

Conclusion

The HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

A Nomogram Based on Molecular Biomarkers and Radiomics to Predict Lymph Node Metastasis in Breast Cancer

Background

The aim of this study was to explore the feasibility and efficacy of a non-invasive quantitative imaging evaluation model to assess the lymphatic metastasis of breast cancer based on a radiomics signature constructed using conventional T1-weighted image (T1WI) enhanced MRI and molecular biomarkers.

Methods

Patients with breast cancer diagnosed via lymph biopsies between June 2015 and June 2019 were selected for the study. All patients underwent T1WI contrast-enhancement before treatment; lymph biopsy after surgery; and simultaneous Ki-67, COX-2, PR, Her2 and proliferating cell nuclear antigen detection. All images were imported into ITK-SNAP for whole tumor delineation, and AK software was used for radiomics feature extraction. Next, the radiomics signature Rad-score was constructed after reduction of specific radiomic features. A multiple regression logistic model was built by combining the Rad-score and molecular biomarkers based on the minimum AIC.

Results

In all, 100 patients were enrolled in this study, including 45 with non-lymph node (LN) metastasis and 55 with LN metastasis. A total of 1,051 texture feature parameters were extracted, and LASSO was used to reduce the dimensionality of the radiomics features. The log(λ) was set to 0.002786, and 19 parameters were retained for the construction of the radiomics tag Rad-score. ROC was used to evaluate the diagnostic efficiency of Rad-score: the area under the ROC curve (AUC) of the Rad-score for identifying non-lymphatic and lymphatic metastases was 0.891 in the training cohort and 0.744 in the validation cohort. With the incorporation of tumor molecular markers, the AUCs of the training cohort and validation cohort of the nomogram were 0.936 and 0.793, respectively, which were notably higher than the AUCs of the clinical parameters in the training and validation cohorts (0.719 and 0.588, respectively).

Conclusion

The combined model constructed using the Rad-score and molecular biomarkers can be used as an effective non-invasive method to assess LN metastasis of breast cancer. Furthermore, it can be used to quantitatively evaluate the risk of breast cancer LN metastasis before surgery.

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.

Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

Background

Sentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied on postoperative data, thus limiting their application in the preoperative setting. Therefore, it is necessary to find a new model for preoperative risk prediction for SLN metastasis to help clinicians facilitate individualized clinical decisions.

Materials and Methods

BC patients who underwent SLN biopsy in two different institutions were included in the training and validation cohorts. Demographic characteristics, preoperative tumor pathological features, and ultrasound findings were evaluated. Multivariate logistic regression was used to develop the nomogram. The discrimination, accuracy, and clinical usefulness of the nomogram were assessed using Harrell’s C-statistic and ROC analysis, the calibration curve, and the decision curve analysis, respectively.

Results

A total of 624 patients who met the inclusion criteria were enrolled, including 444 in the training cohort and 180 in the validation cohort. Young age, high BMI, high Ki67, large tumor size, indistinct tumor margins, calcifications, and an aspect ratio ≥1 were independent predictive factors for SLN metastasis of BC. Incorporating these parameters, the nomogram achieved a robust predictive performance with a C-index and accuracy of 0.92 and 0.85, and 0.82 and 0.80 in the training and validation cohorts, respectively. The calibration curves also fit well, and the decision curve analysis revealed that the nomogram was clinically useful.

Conclusions

We established a nomogram to preoperatively predict the risk of SLN metastasis in BC patients, providing a non-invasive approach in clinical practice and serving as a potential tool to identify BC patients who may omit unnecessary SLN biopsy.

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.

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.

A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development

BACKGROUND

Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort.

METHODS

We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017 at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort.

RESULTS

Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0.575-0.817; P < 0.001) were independent statistically significant predictors of non-sentinel lymph node metastasis. Furthermore, luminal B (OR = 3.311, 95% CI 1.593-6.884; P = 0.001) and HER2 overexpression (OR = 4.308, 95% CI 1.097-16.912; P = 0.036) were independent and statistically significant predictor of non-sentinel lymph node metastasis versus luminal A. A regression model based on the results of multivariate analysis was established to predict the risk of non-sentinel lymph node metastasis, which had an AUC of 0.8188. The model was validated in the validation cohort and showed excellent diagnostic performance.

CONCLUSIONS

The mathematical prediction model that incorporates five variables including breast cancer molecular subtype demonstrates excellent diagnostic performance in assessing the risk of non-sentinel lymph node metastasis in sentinel lymph node-positive patients. The prediction model could be of help surgeons in evaluating the risk of non-sentinel lymph node involvement for breast cancer patients; however, the model requires further validation in prospective studies.

Medico-economic impact of MSKCC non-sentinel node prediction nomogram for ER-positive HER2-negative breast cancers

Background

Avoiding axillary lymph node dissection (ALND) for invasive breast cancers with isolated tumor cells or micrometastatic sentinel node biopsy (SNB) could decrease morbidity with minimal clinical significance.

Purpose

The aim of this study is to simulate the medico-economic impact of the routine use of the MSKCC non-sentinel node (NSN) prediction nomogram for ER+ HER2- breast cancer patients.

Methods

We studied 1036 ER+ HER2- breast cancer patients with a metastatic SNB. All had a complementary ALND. For each patient, we calculated the probability of the NSN positivity using the MSKCC nomogram. After validation of this nomogram in the population, we described how the patients’ characteristics spread as the threshold value changed. Then, we performed an economic simulation study to estimate the total cost of caring for patients treated according to the MSKCC predictive nomogram results.

Results

A 0.3 threshold discriminate the type of sentinel node (SN) metastases: 98.8% of patients with pN0(i+) and 91.6% of patients with pN1(mic) had a MSKCC score under 0.3 (false negative rate = 6.4%). If we use the 0.3 threshold for economic simulation, 43% of ALND could be avoided, reducing the costs of caring by 1 051 980 EUROS among the 1036 patients.

Conclusion

We demonstrated the cost-effectiveness of using the MSKCC NSN prediction nomogram by avoiding ALND for the pN0(i+) or pN1(mic) ER+ HER2- breast cancer patients with a MSKCC score of less than or equal to 0.3.

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.

Clinical and Pathological Factors Affecting the Sentinel Lymph Node Metastasis in Patients with Breast Cancer

Sentinel lymph node biopsy has become the routine procedure in axilla-negative breast cancer patients at most medical centers for axillary staging and local control in the recent years. Sentinel lymph node is the only focus in axillary lymph metastasis in a large portion of patients. In our trial, we investigated the clinical and pathological factors that affect the positive status of sentinel lymph node. We included 89 patients, who underwent sentinel lymph node biopsy (SLNB) with methylene blue and/or technetium-99 m Sulphur Colloid due to early-stage breast cancer. Five patients, in whom SLN was not detected and who underwent axillary dissection, were excluded from the trial. The patient age, location of the tumor, the type of the tumor, the T stage by the TNM staging system, the histological grade and type of the tumor, the status of multifocality, the lymphovascular invasion status of the tumor, and the ER, PR, and HER-neu2 status were recorded. The median age of the 89 patients was 52, 9 (±10) years. Fifty-seven (64 %) and 32 (36 %) of the 89 patients were detected to have positive and negative SLN, respectively. Assessing the SLNB positivity and the patient age, tumor size, tumor grade, multifocality, tumor localization, the T stage by the TNM staging, the ER/PR positivity/negativity, and the HER/neu2 and p53 status, the data revealed no statistically significant results with respect to SLN metastasis. The lymphovascular invasion status (LVI) was observed to statistically affect the SLN positivity (p < 0.016). We showed that LVI could be an important marker in predicting the SLN positivity in patients with axilla-negative early-stage breast cancer. In the future, upon introduction of new biomarkers and with relevant studies, it may be possible to predict the SLNB status of patients at an adequately high accuracy and a low risk.

[Axillary management in breast cancer: The French practice versus recommendations in the post-2011 era]

OBJECTIVES

Today, according to St-Gallen and ASCO clinical guidelines, axillary lymph node dissection (ALND) should be avoided in patients who meet the ACOSOG Z011 criteria. In French guidelines, ALND is still recommended in case of macrometastasis in sentinel lymph node (SLN) and in case of micrometastasis without systemic treatment. We performed a survey of the French practices in the management of the axilla.

MATERIAL AND METHODS

A questionnaire was sent to 454 breast surgeons between June 2014 and January 2015. Questionnaire included items about: indications of SLN biopsy, frequency of ALND in case of metastatic SLN and modality of radiotherapy in case of metastatic SLN without ALND.

RESULTS

A total of 169 surgeons (37%) answer the questionnaire. Twenty-one percent of surgeons avoid ALND in case of macrometastasis. Thirty-two percent do not perform extemporaneous examination of SLN. Only 8.4% of practionners performed a SLN biopsy after neoadjuvant chemotherapy. Fourteen percent performed a SLN biopsy in case of multicentric tumors, while it is not recommended. In case of positive SLN without ALND completion, radiotherapy does not change in 34% while irradiation fields are expanded in 44%.

CONCLUSIONS

Significant unconformities are observed towards national recommendations. The divergence between French and international guidelines leads to heterogeneous surgical practices.

The appropriate axillary procedure after a positive sentinel node in breast cancer patients: the "Hôpital Tenon" score revisited. A two-institution study

INTRODUCTION

Until recently, completion ALND has been considered the standard of care after a positive SN in breast cancer patients. However, most patients will not display further axillary involvement. The Tenon score is a simple nomogram that can be used intraoperatively to avoid completion ALND in low-risk patients. We aimed at validating the Tenon score on a SN-positive patient sample that had been preoperatively selected using axillary US examination.

PATIENTS AND METHOD

We used a retrospective analysis of our bicentric database that included 246 breast cancer patients with a positive SN. We calculated sensitivity, specificity, as well as positive and negative predictive values for each cut-off point. ROCs were constructed and corresponding AUC values were calculated as a measure of discriminative capacity.

RESULTS

At least one non-SN was positive in 52 patients (21.1 %). 118 patients (48 %) had a score up to 5. Among them, three had at least one positive non-SN. NPV was 97.5 %. Using that threshold, the ROCs analysis showed an AUC of 0.822 (95 % CI 0.764-0.880).

CONCLUSION

Use of preoperative axillary US examination led to a modification of the proposed Tenon cut-off value from 3.5 to 5 to attain good predictive power for non-SN status. Straightforward intraoperative use of the Tenon score may be considered an advantage over other available nomograms.

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.

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.

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.

Tumor characteristics of breast cancer in predicting axillary lymph node metastasis

Background

Tumor characteristics was sought to be related to axillary lymph node metastasis (ALNM), the paramount prognostic factor in patients with invasive breast cancer. This study was aimed to identify the ALNM-associated tumor characteristics and to determine the predictive clinical pathway.

Material/Methods

Data from 1325 patients diagnosed with invasive breast cancer between January 2004 and January 2010 were retrospectively reviewed. The structure equation model (SEM) was used to build the predictive clinical pathway.

Results

Among the factors found in the final model, the status of human epidermal growth factor receptor 2 is the primary influence on ALNM through histology grade (β=0.18), followed by tumor size (β=0.16). Tumor size was highly relevant to lymphovascular invasion (LVI) and influenced ALNM through LVI (β=0.26), the strongest predictor of ALNM in the final model (β=0.46) and the highest risk of ALNM (odds ratio=9.282; 95% confidence interval: 7.218–11.936).

Conclusions

The structure equation model presented the relation of these important predictors, and might help physicians to assess axillary nodal condition and appropriate surgical procedures.

In breast cancer patients sentinel lymph node metastasis characteristics predict further axillary involvement

The aim of the study was to correlate various primary tumor characteristics with lymph node status, to examine sentinel lymph node (SLN) metastasis size and non-SLN axillary involvement, to look for a cut-off size/number value possibly predicting additional axillary involvement with more accuracy and to examine the relationship of SLN metastasis size to overall survival. Of 301 patients who underwent SLN biopsy, 75 had positive SLNs. The size of the metastases was measured. For different size categories, association with the prevalence of non-SLN metastases was assessed. Associations between metastasis size and tumor characteristics and overall survival (OS) were studied. The prevalence of axillary lymph node (ALN) involvement was not significantly different between cases with micrometastasis or macrometastasis in SLNs (p = 0.124). However, for metastases larger than 6, 7, and 8 mm, the prevalence of ALN involvement was significantly higher (p = 0.046, 0.022, and 0.025). OS was significantly lower in SLN-positive than in SLN-negative cases (p = 0.0375). Primary tumor size larger than 20 mm was associated with a significantly higher incidence of SLN metastasis (p < 0.001), and primary tumor size over 26 mm was associated with additional positive non-SLN (p < 0.001). Higher mitotic index (≥ 7) in primary tumors was significantly (p < 0.001) associated with ALN involvement in SLN-positive cases, whereas higher Ki67 labeling index was not significantly correlated with SLN or ALN involvement. Lymphovascular invasion (LVI) in primary tumors was significantly correlated with SLN positivity (p < 0.001) but not with further ALN involvement or OS. Tumor size and LVI are predictive for SLN metastasis. Mitotic index, primary tumor size, and larger volume SLN involvement are determinants of further ALN involvement. SLN metastasis size over 6 mm is a strong predictor of further axillary involvement. OS is shorter in the presence of positive SLN.

Prediction of axillary lymph node metastases in breast cancer patients based on pathologic information of the primary tumor

BACKGROUND

Axillary lymph nodes (ALN) are the most commonly involved site of disease in breast cancer that has spread outside the primary lesion. Although sentinel node biopsy is a reliable way to manage ALN, there are still no good methods of predicting ALN status before surgery. Since morbidity in breast cancer surgery is predominantly related to ALN dissection, predictive models for lymph node involvement may provide a way to alert the surgeon in subgroups of patients.

MATERIAL AND METHODS

A total of 1325 invasive breast cancer patients were analyzed using tumor biological parameters that included age, tumor size, grade, estrogen receptor, progesterone receptor, lymphovascular invasion, and HER2, to test their ability to predict ALN involvement. A support vector machine (SVM) was used as a classification model. The SVM is a machine-learning system developed using statistical learning theories to classify data points into 2 classes. Notably, SVM models have been applied in bioinformatics.

RESULTS

The SVM model correctly predicted ALN metastases in 74.7% of patients using tumor biological parameters. The predictive ability of luminal A, luminal B, triple negative, and HER2 subtypes using subgroup analysis showed no difference, and this predictive performance was inferior, with only 60% accuracy.

CONCLUSIONS

With an SVM model based on clinical pathologic parameters obtained in the primary tumor, it is possible to predict ALN status in order to alert the surgeon about breast cancer counseling and in decision-making for ALN management.

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 node biopsy in breast cancer revisited

The axilla has long been a focus of clinicians' attention in the management of breast cancer. The approach to the axilla has undergone dramatic changes over the last century, from radical and extended radical excisions, through the introduction of sentinel node biopsy for node negative patients to the current situation where selective management of those with nodal involvement is being introduced. The introduction of lymphatic mapping and sentinel node biopsy in the 1990's has been key to the major changes that have occurred. In less than 20 years it has moved from a hypothesis to a situation where it is the default approach to almost all clinically node negative patients and is being considered in other situations where axillary clearance was previously considered standard. This article reviews the development and introduction of sentinel node biopsy, its current uncertainties and limitations, and possible future developments.

Treatment of sentinel node-positive breast cancer

Sentinel node biopsy has become well accepted as a minimally invasive means of accurately staging the axilla in breast cancer patients. Patients with metastases in the sentinel node(s) have traditionally proceeded to completion of axillary node dissection, whereas patients who are node negative can be spared the morbidity of this procedure. Recently, there has been some debate as to what constitutes node-positive disease and whether patients with metastasis in the sentinel node(s) require completion axillary dissection. This review addresses the controversies regarding the management of sentinel node-positive breast cancer patients.

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.

Non-sentinel lymph node metastasis prediction in breast cancer with metastatic sentinel lymph node: impact of molecular subtypes classification

Introduction

To decipher the interaction between the molecular subtype classification and the probability of a non-sentinel node metastasis in breast cancer patients with a metastatic sentinel lymph-node, we applied two validated predictors (Tenon Score and MSKCC Nomogram) on two large independent datasets.

Materials and Methods

Our datasets consisted of 656 and 574 early-stage breast cancer patients with a metastatic sentinel lymph-node biopsy treated at first by surgery. We applied both predictors on the whole dataset and on each molecular immune-phenotype subgroups. The performances of the two predictors were analyzed in terms of discrimination and calibration. Probability of non-sentinel lymph node metastasis was detailed for each molecular subtype.

Results

Similar results were obtained with both predictors. We showed that the performance in terms of discrimination was as expected in ER Positive HER2 negative subgroup in both datasets (MSKCC AUC Dataset 1 = 0.73 [0.69–0.78], MSKCC AUC Dataset 2 = 0.71 (0.65–0.76), Tenon Score AUC Dataset 1 = 0.7 (0.65–0.75), Tenon Score AUC Dataset 2 = 0.72 (0.66–0.76)). Probability of non-sentinel node metastatic involvement was slightly under-estimated. Contradictory results were obtained in other subgroups (ER negative HER2 negative, HER2 positive subgroups) in both datasets probably due to a small sample size issue. We showed that merging the two datasets shifted the performance close to the ER positive HER2 negative subgroup.

Discussion

We showed that validated predictors like the Tenon Score or the MSKCC nomogram built on heterogeneous population of breast cancer performed equally on the different subgroups analyzed. Our present study re-enforce the idea that performing subgroup analysis of such predictors within less than 200 samples subgroup is at major risk of misleading conclusions.

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.

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.

Predictors of non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph node (Pilot study)

BACKGROUND

Sentinel Lymph Node Biopsy (SLNB) procedure was found to be an accurate method of staging the axilla in patients with early stage breast cancer. The standard of care for breast cancer patients with positive SLN metastasis includes complete Axillary Lymph Node Dissection (ALND). However, in 40-70% of patients, the SLN is the only involved axillary node. Factors predicting non SLN metastasis should be identified in order to define subgroups of patients with positive SLN in whom the axilla may be staged by SLNB alone.

OBJECTIVES

To identify the factors predicting metastatic involvement of the non-SLNs in breast cancer patients having SLN metastasis.

PATIENTS AND METHODS

Data were collected and analyzed from 80 patients with early stage invasive breast cancer (T1, T2, N0, M0) who underwent SLNB at the Surgical Oncology Department, Kuwait Cancer Control Center (KCCC) between November 2004 and February 2009. SLNB was performed using a combined technique (radioactive colloid, and blue dye) in the majority of cases. In some cases, only one technique was used. Complete ALND was performed in the case of failure of SLN identification and in patients with positive SLN. Multiple variables (patient, tumor, and SLN characteristics) were tested as possible predictors of nonsentinel lymph node metastasis.

RESULTS

The mean age of patients at diagnosis was 46.6years. The median tumor size was 2cm. The SLN identification rate was 96.2% (77 out of 80 patients). The SLN was positive in 24 patients (31%), and half of these showed evidence of capsular invasion. The median number of SLNs removed was two. The median number of positive SLNs was one. The incidence of non-SLN metastasis associated with positive SLN was 50% (12 out of 24 patients). Lymphovascular invasion was found to be the only factor associated with non-SLN metastases. In addition, two trends were observed, though they did not reach the statistical significance: the first is that the majority of patients having capsular invasion of the SLN (8 out of 12 patients, 67%) had positive non-SLN metastasis, and the second is that the patients having more than one SLN metastasis were more likely to have non-SLN metastasis (4 out of 5, 80%).

CONCLUSION

In the current pilot study, only the lymphovascular invasion in the area of the primary tumor was found to be significantly related to the nonsentinel lymph node metastasis. There was a tendency toward higher incidence of nonsentinel lymph node metastasis associated with the number of positive SLN and capsular invasion of SLN, though this did not reach the statistical significance. This could be attributed to the small number of patients recruited. Further evaluation of the predictors of nonsentinel lymph node metastasis on a larger number of patients is required. The validation of these predictors in prospective studies may enable approximately half of early stage breast cancer patients with positive SLN to be staged with SLNB alone while avoiding the morbidity of unnecessary ALND.

Sentinel lymph node micrometastasis in human breast cancer: an update

INTRODUCTION

The advent of sentinel lymph node biopsy (SLNB) and advances in histopathological and molecular analysis techniques have been associated with an increase in micrometastasis (MM) detection rate. However, the clinical significance of sentinel lymph node micrometastasis (SLN MM) continues to be a subject of much debate. In this article we review the literature concerning SLN MM, with particular emphasis on the prognostic significance of SLN MM. The controversies regarding histopathological assessment, clinical relevance and management implications are also discussed.

METHODS

Literature review facilitated by Medline and PubMed databases. Cross referencing of the obtained articles was used to identify other relevant studies.

RESULTS

Published studies have reported divergent and rather conflicting results regarding the clinical significance and implications of axillary lymph node (ALN) MM in general and SLN MM in particular. Some earlier studies demonstrated no associations, however most recent studies have found SLN MM to be an indicator of poorer prognosis and to be associated with non-SLN involvement. The use of adjuvant chemotherapy and/or hormonal manipulation therapy is associated with an improved survival in patients with SLN MM. Complete ALND may be safely omitted provided that adjuvant systemic therapy recommendations are equal to patients with node-positive disease. However, optimal management of SLN MM is yet to conclude. Furthermore, the identification of MM remains largely dependent on the analytical technique employed and the use of immunohistochemistry (IHC) increases the detection rate of SLN MM. Discrepancies in the histopathological interpretation of TNM classification of SLN tumour burden do exist. Published studies were non-randomized and have significant limitations including a small sample size, limited follow-up period, and lack of standardization and reproducibility of pathological examination of the SLN.

CONCLUSION

Patients with SLN MM have a poorer prognosis than those who are SLN negative. Therapeutic recommendations regarding patients with SLN MM should be taken in the context of multidisciplinary team setting and in selected cases of SLN MM, complete ALND may be safely omitted. A better reproducibility of pathological interpretation of the TNM classification is required so that future therapeutic guidelines can be applied without confusion.

The Impact of Lymphovascular Invasion on Lymph Node Status in Patients with Breast Cancer

Lymphovascular invasion (LVI) is not uniformly found or reported in breast cancer tumor reports. We sought to determine the impact of the finding of LVI on various parameters of lymph node status in patients with breast cancer. A chart review was performed of 400 node-positive patients from a cohort of patients in a prospective multicenter national sentinel node registry. The finding of LVI was then correlated to number of positive sentinel nodes, the number of positive non-sentinel nodes, the lymph node ratio, and the size of the largest metastatic deposit. Of the 400 patients, data regarding LVI were missing in 98 (24.5%) cases. Although all of these patients were node-positive, LVI was noted to be present in 155 patients (38.8%) and absent in 147 (36.8%). LVI was found to correlate with more positive sentinel nodes (mean, 1.72 vs 1.35; P < 0.001), more positive nonsentinel nodes (mean, 2.16 vs 0.54; P < 0.001), and a higher lymph node ratio (0.29 vs 0.16; P < 0.001). LVI also correlated with size of largest metastatic deposit ( P = 0.002). Although LVI is known to be associated with lymph node status, it is not frequently noted on pathology reports. Given its prognostic value, LVI should be carefully evaluated and reported.

Meta-analysis of predictive factors for non-sentinel lymph node metastases in breast cancer patients with a positive SLN

AIMS

A meta-analysis was performed to identify the clinicopathological variables most predictive of non-sentinel node (NSN) metastases when the sentinel node is positive.

METHODS

A Medline search was conducted that ultimately identified 56 candidate studies. Original data were abstracted from each study and used to calculate odds ratios. The random-effects model was used to combine odds ratios to determine the strength of the associations.

FINDINGS

The 8 individual characteristics found to be significantly associated with the highest likelihood (odds ratio >2) of NSN metastases are SLN metastases >2mm in size, extracapsular extension in the SLN, >1 positive SLN, ≤1 negative SLN, tumour size >2cm, ratio of positive sentinel nodes >50% and lymphovascular invasion in the primary tumour. The histological method of detection, which is associated with the size of metastases, had a correspondingly high odds ratio.

CONCLUSIONS

We identified 8 factors predictive of NSN metastases that should be recorded and evaluated routinely in SLN databases. These factors should be included in a predictive model that is generally applicable among different populations.

Validation of the S classification of sentinel lymph node and microanatomic location of sentinel lymph node metastases to predict additional lymph node involvement and overall survival in breast cancer patients

BACKGROUND

Most patients with a positive sentinel lymph node (SN) have no further metastases in the axillary lymph nodes and may therefore not benefit from axillary lymph node dissection. In patients with melanoma, evaluation of the centripetal depth of tumor invasion in the SN, also known as the S classification of SN, and microanatomic localization of SN metastases were shown to predict non-SN involvement. This phenomenon has been less extensively studied in breast cancer. We sought to validate the S classification and microanatomic location of SN metastases in breast cancer patients with regard to their predictive value for non-SN involvement and overall survival (OS).

METHODS

A total of 236 patients with positive SN followed by axillary lymph node dissection were reevaluated according to the S classification and the microanatomic location of SN (subcapsular, parenchymal, combined subcapsular and parenchymal, multifocal, extensive) metastases to predict the likelihood of non-SN metastases and OS.

RESULTS

S classification and the microanatomic location of SN metastases were significantly correlated with non-SN status (P < 0.001). Especially patients with a maximum depth of invasion ≤0.3 mm (stage I according to the S classification) and those with SN metastases only in subcapsular location had a low probability of further non-SN metastases (7.8 and 6.1%) and a good prognosis for OS.

CONCLUSIONS

S classification and microanatomic location of SN metastases predicts the likelihood of non-SN involvement. Especially patients with subcapsular or S stage I metastases have a low probability of non-SN metastases and a good prognosis for OS.

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

Background

Women with breast cancer and a positive axillary sentinel lymph node (SLN) are recommended to undergo complete axillary lymph node dissection; however, further nodal disease is not always present. Mathematical models have been constructed to determine the risk of metastatic disease; three of these were evaluated independently.

Methods

Data from 108 women with breast cancer who had a positive SLN biopsy and completion axillary lymph node dissection were used. Measurements of additional parameters over those usually determined (such as size of SLN metastasis) were assessed under the supervision of two pathologists. These data were used to determine the predicted risk of non-SLN metastases using three mathematical models (from Memorial Sloan-Kettering Cancer Center (MSKCC), Cambridge University and Stanford University) and a comparison made with the observed findings. Analyses were made using the area under the receiver operating characteristic (ROC) curve (AUC).

Results

Some 53 (49·1 per cent) of 108 patients had a positive non-sentinel axillary lymph node metastasis. The AUC values were 0·63, 0·72 and 0·67 for the MSKCC, Cambridge and Stanford nomograms respectively.

Conclusion

This independent comparison found no significant difference between the models, although the Cambridge model had the advantage of requiring fewer measurements with a more accurate predictive performance.

Recent advances in the surgical care of breast cancer patients

A tremendous improvement in every aspect of breast cancer management has occurred in the last two decades. Surgeons, once solely interested in the extipartion of the primary tumor, are now faced with the need to incorporate a great deal of information, and to manage increasingly complex tasks.

As a comprehensive assessment of all aspects of breast cancer care is beyond the scope of the present paper, the current review will point out some of these innovations, evidence some controversies, and stress the need for the surgeon to specialize in the various aspects of treatment and to be integrated into the multisciplinary breast unit team.