Lymphovascular invasion is a significant risk factor for non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node (SLN)-positive breast cancer: a cross-sectional study

BACKGROUND

A connection between lymphovascular invasion and axillary lymph node metastases in breast cancer has been observed, but the findings are inconsistent and primarily based on research in Western populations. We investigated the association between lymphovascular invasion and non-sentinel lymph node (non-SLN) metastasis in breast cancer patients with sentinel lymph node (SLN) metastasis in western China.

METHODS

This study comprised 280 breast cancer patients who tested positive for SLN through biopsy and subsequently underwent axillary lymph node dissection (ALND) at The People's Hospital of Guangxi Zhuang Autonomous Region between March 2013 and July 2022. We used multivariate logistic regression analyses to assess the association between clinicopathological characteristics and non-SLN metastasis. Additionally, we conducted further stratified analysis.

RESULTS

Among the 280 patients with positive SLN, only 126 (45%) exhibited non-SLN metastasis. Multivariate logistic regression demonstrated that lymphovascular invasion was an independent risk factor for non-SLN in breast cancer patients with SLN metastasis (OR = 6.11; 95% CI, 3.62-10.32, p < 0.05). The stratified analysis yielded similar results.

CONCLUSIONS

In individuals with invasive breast cancer and 1-2 positive sentinel lymph nodes, lymphovascular invasion is the sole risk factor for non-SLN metastases. This finding aids surgeons and oncologists in devising a plan for local axillary treatment, preventing both over- and undertreatment.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer

BACKGROUND OR PURPOSE

A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status.

METHODS

Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models.

RESULTS

In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability.

CONCLUSION

This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients

Background

Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients.

Methods

This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed.

Results

229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively.

Conclusion

We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.

3D reconstruction based novel methods are more effective than traditional clinical assessment in breast cancer axillary lymph node metastasis prediction

The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy. This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods. A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%). By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

Sentinel Lymph Node Positive Rate Predicts Non-Sentinel Lymph Node Metastasis in Breast Cancer

BACKGROUND

To investigate retrospectively an association between the number of metastatic sentinel lymph nodes (SLNs) per total number of SLNs per patient (i.e., the SLN positive rate, or SLN-PR) and non-SLN metastasis in breast cancer.

METHODS

A large population (n = 2250) underwent SLN dissection from January 1, 2014 to January 1, 2020; 627 (27.87%) had at least one positive SLN (SLN⁺). Among these, 283 underwent axillary lymph node (ALN) dissection, and formed the test group. Four external validation groups comprised 43 patients treated in 2019. SLN mappings were examined using methylene blue and indocyanine green. Lymph node ultrasound, SLN-PR, and pathological characteristics were compared between patients with and without non-SLN metastasis. An SLN-PR cutoff value was calculated using receiver operating characteristic (ROC) curves. Associations between clinicopathological variables and SLN-PR with non-SLN metastasis were analyzed by multivariate logistic regression model.

RESULTS

The median age was 47 years (IQR: 42-56 y). The median number of resected SLNs was 4. Patients with positive non-SLNs (126/283, 44.52%) had a median of 2 positive node. SLN-PR > 0.333 was a risk factor for non-SLN positivity (area under the ROC curve, 0.726); and carried significantly higher risk of non-SLN metastasis (P < 0.001). This was validated in the external group.

CONCLUSIONS

SLN-PR > 0.333 was associated with greater risk of non-SLN metastasis. This provides a reference to non-SLN metastasis in patients with SLN metastasis, an indication for ALN dissection and choice of adjuvant treatment.

A nomogram to predict non-sentinel lymph node metastasis in patients with initial cN+ breast cancer that downstages to cN0 after neoadjuvant chemotherapy

BACKGROUND AND OBJECTIVES

This study mainly explored the factors that influence non-sentinel lymph node (NSLN) metastasis in patients with breast cancer (BC) whose axillary lymph nodal status changed from clinically node positive (cN+) to clinically node negative (cN0) after neoadjuvant chemotherapy (NAC).

METHODS

We retrospectively analyzed the clinicopathological factors affecting NSLN metastasis in a total of 179 patients with cN+ BC downstaged to cN0 (120 in the training set and 59 in the validation set) who underwent both sentinel lymph node (SLN) biopsy and axillary lymph node dissection following NAC.

RESULTS

Among 179 patients enrolled, the overall NSLN metastatic rate was 24.0% (95% confidence interval [CI]: 17.7%-30.3%). In multivariate logistic regression analysis, the number of positive SLNs achieving a pathological complete remission of the breast and clinical node staging was independent predictors of NSLN metastasis. A nomogram was established based on these factors and displayed a good discriminatory capability, with an area under the curve of 0.919 (95% CI: 0.865-0.973) for the training set and 0.900 (95% CI: 0.812-0.988) for the validation set and its clinical utility was confirmed by the decision curve analysis.

CONCLUSIONS

The nomogram established showed the ability to predict NSLN metastases in patients with initial cN+ BC that downstaged to cN0 after NAC.

Intraoperative Prediction Of Non-Sentinel Lymph Node Metastasis Based On The Molecular Assay In Breast Cancer Patients

Purpose

The aim of the study is to construct an intraoperative nomogram for the prediction of non-sentinel lymph node (NSLN) metastasis based on the one-step nucleic acid amplification assay in breast cancer patients.

Methods

A total of 552 patients were enrolled in the training study and 1090 patients were enrolled in the validation study. The nomogram was constructed based on the molecular assay with logistic multivariate regression analysis in the training study and was validated in the validation study.

Results

A novel nomogram model was constructed with the total tumor load, the clinical primary tumor size, the number of positive and negative sentinel lymph nodes. The area under the receiver operating characteristic curve (AUC) of the model was 0.842. The AUC of the model which was sensitive to discern the patients with the stage of pN1 and ≥pN2 was 0.861.

Conclusion

The nomogram model will help to guide the axillary management intraoperatively and precisely confirm the target region of radiotherapy postoperatively.

Predictors of non-sentinel lymph node metastasis in clinical early stage (cT1-2N0) breast cancer patients with 1-2 metastatic sentinel lymph nodes

OBJECTIVE

The purpose of this study was to determine the risk factors that caused non-sentinel lymph nodes (nonSLNs) metastasis by considering the clinicopathological characteristics of patients who have 1-2 sentinel lymph node (SLN) metastasis in the clinical early stage (T1-2, N0) breast cancer.

METHODS

The demographic and clinicopathological characteristics of the patients were recorded retrospectively. Among these, age, size of the primary breast tumor, tumor localization and multifocality/multicentricity status, preoperative serum Neutrophil/Lymphocyte rate (NLR), c-erbB2/HER2-neu status, Estrogen Receptor (ER) and Progesterone Receptor (PR) status, primary tumor proliferation index (Ki-67), histopathological grade, molecular subtypes, histopathological subtypes, nipple/areola infiltration, Lymphatic Invasion (LI), Vascular Invasion (VI), Perineural Invasion (PNI), number of metastatic SLN m(SLN), mSLN diameter, SLN Extranodal Extension (ENE) status, and number of metastatic nonSLNs were recorded.

RESULTS

According to the univariate analysis, the HER2 positivity, Ki-67≥%20, mSLN diameter, LI, VI, PNI, ENE and molecular subtypes were found to be significant. However, the age, tumor localization, multifocality/multicentricity, T stage, ER and PR status, tumor size, histopathological grade and subtypes, nipple/areola infiltration and NLR were not found to be significant. In the multivariate analysis, significant independent predictors in nonSLN metastasis development were found to be HER2 positivity, PNI, mSLN diameter ≥10,5 mm and ENE.

CONCLUSION

The HER2 positivity, ENE, PNI and mSLN diameter ≥10,5 mm were found to be very strong predictors in nonSLN metastasis development. The findings of this study have the potential to be a guideline for surgeons and oncologists when determining their patients' treatment plan. These components are candidates for inclusion among the clinicopathological factors that may be used in the new nomograms due to their higher sensitivity and specificity.

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

Objectives:

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

Method:

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

Results:

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

Conclusion:

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

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.

Factors Influencing Non-sentinel Node Metastasis in Patients with Macrometastatic Sentinel Lymph Node Involvement and Validation of Three Commonly Used Nomograms

OBJECTIVE

Omitting axillary lymph node dissection (ALND) in a subgroup of patients with sentinel lymph node (SLN) metastasis is becoming a widely accepted practice. Avoiding the well-known complications of ALND is the sole aim without compromising the curative intention of surgery.

MATERIALS AND METHODS

The data were probed for breast cancer patients that were operated on between February 2014 and June 2016. SLN biopsies were performed in 507 patients and out of 157 patients who underwent ALND for a metastatic SLN, 151 were found eligible for the analyses as having macrometastatic (>2mm) SLN. MD Anderson, Memorial Sloan Kettering Cancer Center and Helsinki nomograms were also tested in our patient population.

RESULTS

Pathologic tumor size greater than 2 cm, the ratio of metastatic SLN to dissected SLN, metastatic tumor greater than 1 cm and tumors that extended outside the SLN's capsule were found to be associated with non-sentinel node metastasis in both univariate and multivariate tests. MD Anderson nomogram performed well with an area under the curve (AUC) value of 0.72.

CONCLUSION

Our results suggest that ALND should be considered in patients with macrometastatic SLN greater than 10 mm in size, have extracapsular extension, have metastatic SLNs at a rate of more than 50% and whose primary tumor is greater than 2 cm.

Validation of a breast cancer nomogram to predict lymphedema in a Chinese population

BACKGROUND

Upper arm lymphedema (LE) is a common complication after axillary lymph node dissection (ALND) in breast cancer patients. This retrospective cohort study aimed to validate a published nomogram to predict the risk of LE in the Chinese breast cancer patients.

METHODS

A total of 409 breast cancer patients who underwent breast cancer surgery and ALND (level I and II) were identified. Cox regression analysis was used to identify the risk factors for LE. The nomogram predictive of LE of breast cancer was evaluated by receiver-operating curve analysis, calibration plots, and Kaplan-Meier analysis in our study population.

RESULTS

With a median follow-up of 68 months, the 5-year cumulative incidence of LE was 22.3%. Higher body mass index (hazard ratio [HR] = 1.06, 95% CI: 1.00-1.13), neoadjuvant chemotherapy (HR = 3.76, 95% CI: 2.29-6.20), larger extend of axillary surgery (level I/II/III versus level I/II: HR = 2.39, 95% CI: 1.30-4.37), and radiotherapy (HR = 4.90, 95% CI: 1.90-12.5) were independently associated with LE. The AUC value of the nomogram was 0.706 (95% CI: 0.648-0.752). A high-risk subgroup of patients defined by nomogram had significantly higher cumulative risk of LE than those in the low-risk subgroups (P < 0.01). The calibration plots revealed that the nomogram was well calibrated (Hosmer-Lemeshow test, P = 0.0634).

CONCLUSIONS

The nomogram to predict the risk of LE in breast cancer patients with ALND has been validated to be discriminative and accurate. More studies are needed to evaluate the impact of other factors (lifestyle, behaviors, and so forth) on the performance of the nomogram.

Prediction of nonsentinel lymph node metastasis in breast cancer patients with one or two positive sentinel lymph nodes

OBJECTIVE

The aim of the present study was to investigate the association between non sentinel lymph node metastasis (NSLNM) and clinicopathological factors, particularly in the case of sentinel lymph node (SLN) metastasis in one or two, in clinically node negative patients with breast cancer.

METHODS

Between 10/2010 and 10/2014, 350 sentinel lymph node biopsy (SLNB) were performed in patients with histologically proven primary breast cancer in our clinic. The data collection includes the following characteristics: age, pathological tumor size, histological type, histological grade, lymphovascular invasion (LVI), number of positive SLN, size of the SLN metastasis (macrometastasis, micrometastasis, isolated tumor cells), multifocality (MF), extracapsuler invasion (ECI) of the SLN, the estrogen receptor (ER) status, the progesterone receptor (PR) status and the Her 2 receptor status, Ki 67 reseptor status. Data were collected retrospectively and then analyzed.

RESULTS

A successful SLN biopsy were performed in 345 (98.5%) cases. SLN metastases were detected in 110 (31.8%) cases. These patients then underwent axillary dissection; among these patients, 101 (91.8%) had only one to two positive SLNs. Of the 101 patients with positive SLN biopsies, 32 (31.6%) had metastases in the NSLNs. Univariate and multivariate analysis showed that lymphovascular invasion, extracapsular invasion (ECI), Her-2 receptor positive, and Ki-67 > 14% were related to NSLNM (p<.0.05).

CONCLUSION

The predicting factors of NSLNM were LVI, ECI, Ki-67 level, Her-2 reseptor positive and but should be further validated in our institutions, different institutions and different patient groups prospectively.

The Extent of Axillary Surgery Is Associated With Breast Cancer-specific Survival in T1-2 Breast Cancer Patients With 1 or 2 Positive Lymph Nodes: A SEER-Population Study

This study aimed to compare the breast cancer-specific survival (BCSS) of a nonclinical trial population of T1-2 breast cancer patients with 1 to 2 positive lymph nodes who received breast-conserving surgery and either sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND).We used the Surveillance, Epidemiology and End Results (SEER) database to identify 17,028 patients with a median follow-up of 7.1 years. We assigned the patients into a SLNB-cohort (≤5 nodes) and an ALND-cohort (>5 nodes) based on the number of removed lymph nodes. We used Kaplan-Meier analysis to estimate the cumulative BCSS and used Cox-regression analysis to study the risk factors. We also performed subgroup analysis by the patients' age and hormonal receptor (HR) status.The cumulative BCSS and Overall Survival (OS) of the entire population were 94.4% and 91.4% at 5 years and 88.2% and 79.9% at 10 years, respectively. Axillary surgery (ALND vs SLNB) had no association with BCSS when adjusted for stage, HR status, tumor grade, or other factors. In subgroup analysis by age and HR status, ALND was associated with a significantly improved BCSS relative to SNLB (HR = 0.70, HR = 0.026, 95% confidence interval 0.51-0.96) only in patients younger than 50 years with HR- disease (N = 1281), but not in other subgroup of patients.In early-stage breast cancer patients with limited lymph node metastasis, ALND had better BCSS than SLNB only in patients younger than 50 years and with HR- disease. More studies are needed to confirm our findings.

T-stage and positive sentinel nodes ratio are the useful factors to predict non-sentinel node metastasis in breast cancer patients with macro-metastasis in the sentinel node

INTRODUCTION

Current guidelines recommend completion axillary lymph node dissection (cALND) in case of a sentinel lymph node (SN) metastasis larger than 2 mm (macrometastasis). However in many patients of those, the non-sentinel lymph nodes (NSN) contain no further metastasis, indicating that axillary lymph node dissection provides no benefit. To identify cases who could have undergone omission of the ALND with confidence, we have retrospectively evaluated the predictive factors of NSN metastasis with positive macrometastasis in the SN.

METHODS

This study was based on a retrospective database of 420 patients who underwent sentinel lymph node biopsy (SNB) for breast cancer, of whom 61 patients had SN macrometastasis intra- and postoperatively. We examined predictive factors of NSN involvement in 51 cases of these 61 patients who underwent cALND. All clinical and histological variables were analyzed according to NSN status, by using Mann-Whitney U test, univariate and multivariate logistic regression model.

RESULTS

T stage and the proportion of involved SNs among all identified SNs (SN ratio) were correlated with NSN metastasis. Univariate and multivariate analysis showed that T stage and SN ratio were the independent predictive factor of NSN metastasis. The area under ROC curve for SN ratio was 0.71. The best cut off value of SN ratio was 0.667. Negative predictive value to NSN metastasis in cases with both T2 and more than 0.667 of SN ratio was 85.7%.

CONCLUSION

In patients with invasive breast cancer and macrometastasis of SN, T stage and SN ratio were useful for prediction of NSN metastasis.

Cross-validation of three predictive tools for non-sentinel node metastases in breast cancer patients with micrometastases or isolated tumor cells in the sentinel node

BACKGROUND

We cross-validated three existing models for the prediction of non-sentinel node metastases in patients with micrometastases or isolated tumor cells (ITC) in the sentinel node, developed in Danish and Finnish cohorts of breast cancer patients, to find the best model to identify patients who might benefit from further axillary treatment.

MATERIAL AND METHOD

Based on 484 Finnish breast cancer patients with micrometastases or ITC in sentinel node a model has been developed for the prediction of non-sentinel node metastases. Likewise, two separate models have been developed in 1577 Danish patients with micrometastases and 304 Danish patients with ITC, respectively. The models were cross-validated in the opposite cohort.

RESULTS

The Danish model for micrometatases was accurate when tested in the Finnish cohort, with a slight change in AUC from 0.64 to 0.63. The AUC of the Finnish model decreased from 0.68 to 0.58 when tested in the Danish cohort, and the AUC of the Danish model for ITC decreased from 0.73 to 0.52, when tested in the Finnish cohort. The Danish micrometastatic model identified 14-22% of the patients as high-risk patients with over 30% risk of non-sentinel node metastases while less than 1% was identified by the Finish model. In contrast, the Finish model predicted a much larger proportion of patients being in the low-risk group with less than 10% risk of non-sentinel node metastases.

CONCLUSION

The Danish model for micrometastases worked well in predicting high risk of non-sentinel node metastases and was accurate under external validation.

Robust and validated models to predict high risk of non-sentinel node metastases in breast cancer patients with micrometastases or isolated tumor cells in the sentinel node

BACKGROUND

Benefit from axillary lymph node dissection in sentinel node positive breast cancer patients is under debate. Based on data from 1820 Danish breast cancer patients operated in 2002-2008, we have developed two models to predict high risk of non-sentinel node metastases when micrometastases or isolated tumor cells are found in sentinel node. The aim of this study was to validate these models in an independent Danish dataset.

MATERIAL AND METHODS

We included 720 breast cancer patients with micrometastases and 180 with isolated tumor cells in sentinel node operated in 2009-2010 from the Danish Breast Cancer Cooperative Group database. Accuracy of the models was tested in this cohort by calculating area under the receiver operating characteristic curve (AUC) as well as sensitivity and specificity.

RESULTS

AUC for the model for patients with micrometastases was comparable to AUC in the original cohort: 0.63 and 0.64, respectively. The sensitivity and specificity for predicting risk of non-sentinel node metastases over 30% was 0.36 and 0.81, respectively, in the validation cohort. AUC for the model for patients with isolated tumor cells decreased from 0.73 in the original cohort to 0.60 in the validation cohort. When dividing patients with isolated tumor cells into high and low risk of non-sentinel node metastases according to number of risk factors present, 37% in the high-risk group had non-sentinel node metastases. Specificity and sensitivity was 0.48 and 0.88, respectively, in the validation cohort when using this cut-point.

CONCLUSION

In this independent dataset, the model for patients with micrometastases was robust with accuracy similar to the original cohort, while the model for patients with isolated tumor cells was less accurate. The models may be used to identify patients where axillary lymph node dissection should still be considered.

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.