Prediction of negative axillary node clearance by sentinel node-positive to total node ratio: a retrospective cohort study

Introduction

Increasing evidence suggests that de-escalation of axillary surgery is safe, without significantly impacting patient outcome. Obtaining positive lymph nodes at a sentinel lymph node biopsy (SNB) can guide decisions toward the requirement of axillary nodal clearance (ANC). However, methods to predict how many further nodes will be positive are not available. This study investigates the feasibility of predicting the likelihood of a negative ANC based on the ratio between positive nodes and the total number of lymph nodes excised at SNB.

Methods

Retrospective data from January 2017 to March 2022 was collected from electronic medical records. Patients with oestrogen receptor (ER) positive and HER2 negative receptor disease were included in the study. ER-negative and HER2-positive disease was excluded, alongside patients who had chemotherapy before ANC.

Results

Of 102 patients, 58.8% (n=60) had no macrometastasis at ANC. On average, 2.76 lymph nodes were removed at SNB. A higher SNB ratio of positive to total nodes [OR 11.09 (CI 95% 2.33-52.72), P=0.002] had a significant association with positive nodes during ANC. SNB ratio less than or equal to 0.33 (1/3) had a specificity of 79.2% in identifying cases that later had a negative completion ANC, with a 95.8% specificity of no further upgrade of nodal staging.

Conclusion

A low SNB ratio of less than 0.33 (1/3) has a high specificity in excluding the upgradation of nodal staging on completion of ANC, with a false-negative rate of less than 5%. This may be used to identify patients with a low risk of axillary metastasis, who can avoid ANC.

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.

Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients with 1-2 positive sentinel lymph node macro-metastases: a retrospective analysis of two independent cohorts

BACKGROUND

It is reported that appropriately 50% of early breast cancer patients with 1-2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1-2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1-2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs.

METHODS

We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients.

RESULTS

The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682-0.771) in the primary cohort and 0.722 (95% CI: 0.653-0.792) in the validation cohort. The addition of CA 15-3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732-0.815) vs. 0.727 (0.682-0.771), P < 0.001) and validation cohort (AUC: (0.777 (0.713-0.840) vs. 0.722 (0.653-0.792), P < 0.001). Decision curve analysis demonstrated that the nomograms were clinically useful.

CONCLUSION

The nomograms based on pathological features can be used to identify axillary non-SLN metastases in breast cancer patients with 1-2 positive SLN. In addition, the combination of STMs and pathological features can identify patients with patients with axillary non-SLN metastases more accurately than pathological characteristics alone.

Sentinel lymph node biopsy with one-step nucleic acid assay relegates the need for preoperative ultrasound-guided biopsy staging of the axilla in patients with early stage breast cancer

Avoiding axillary node clearance in patients with early stage breast cancer and low-burden node-positive axillary disease is an emerging practice. Informing the decision to adopt axillary conservation is examined by comparing routine preoperative axillary staging using ultrasound (AUS) ± AUS biopsy (AUSB) with intraoperative staging using sentinel lymph node biopsy (SLNB) and a one-step nucleic acid cytokeratin-19 amplification assay (OSNA). A single-centre, retrospective cohort study of 1,315 consecutive new diagnoses of breast cancer in 1,306 patients was undertaken in the present study. An AUS ± AUSB was performed on all patients as part of their initial assessment. Patients who had a normal ultrasound (AUS-) or negative biopsy (AUSB-) followed by SLNB with OSNA ± axillary lymph node dissection (ALND), and those with a positive AUSB (AUSB+), were assessed. Tests for association were determined using a χ² and Fisher's Exact test. A total of 266 (20.4%) patients with cT1-3 cN0 staging received 271 AUSBs. Of these, 205 biopsies were positive and 66 were negative. The 684 patients with an AUS-/AUSB-assessment proceeded to SLNB with OSNA. AUS sensitivity and negative predictive value (NPV) were 0.53 [0.44-0.62; 95% confidence interval (CI)] and 0.58 (0.53-0.64, 95% CI), respectively. Using a total tumour load cut-off of 15,000 copies/µl to predict ≥2 macro-metastases, the sensitivity and NPV for OSNA were 0.82 (0.71-0.92, 95% CI) and 0.98 (0.97-0.99, 95% CI) (OSNA vs. AUS P<0.0001). Of the AUSB+ patients, 51% had ≤2 positive nodes following ALND and were potentially over-treated. Where available, SLNB with OSNA should replace AUSB for axillary assessment in cT1-2 cN0 patients with ≤2 indeterminate nodes seen on AUS.

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer

Background

Lymph node metastasis of triple-negative breast cancer (TNBC) is essential in treatment strategy formulation. This study aimed to build a nomogram that predicts lymph node metastasis in patients with TNBC.

Materials and Methods

A total of 28,966 TNBC patients diagnosed from 2010 to 2017 in the Surveillance, Epidemiology and End Results (SEER) database were enrolled, and randomized 1:1 into the training and validation sets, respectively. Univariate and multivariate logistic regression analysis were applied to identify the predictive factors, which composed the nomogram. The receiver operating characteristic curves showed the efficacy of the nomogram.

Result

Multivariate logistic regression analyses revealed that age, race, tumor size, tumor primary site, and pathological grade were independent predictive factors of lymph node status. Integrating these independent predictive factors, a nomogram was successfully developed for predicting lymph node status, and further validated in the validation set. The areas under the receiver operating characteristic curves of the nomogram in the training and validation sets were 0.684 and 0.689 respectively, showing a satisfactory performance.

Conclusion

We constructed a nomogram to predict the lymph node status in TNBC patients. After further validation in additional large cohorts, the nomogram developed here would do better in predicting, providing more information for staging and treatment, and enabling tailored treatment in TNBC patients.

Nomogram-based estimate of axillary nodal involvement in ACOSOG Z0011 (Alliance): validation and association with radiation protocol variations

PURPOSE

A substantial proportion of patients enrolled on ACOSOG Z0011 received protocol-deviant radiation treatment. It is currently unknown whether these deviations involved the use of more extensive fields in patients at higher nomogram-predicted risk.

METHODS

We used the M.D. Anderson (MDA) and Memorial Sloan-Kettering (MSK) nomograms to estimate risk of additional positive axillary nodes using surgical pathology information. In the control arm, we compared axillary dissection (AD) findings to nomogram-predicted estimates for validation. We used logistic regression to evaluate whether nomogram-estimated higher risk of nodal involvement was associated with high tangent (HT) or supraclavicular (SCV) radiation fields for patients with known radiation field design.

RESULTS

552/856 (64.5%) had complete details for the MDA nomogram. Mean MDA risk estimate in both treatment arms was 23.8%. Estimated risk for patients on the AD arm with positive nodes was 25.9%. Higher risk estimate was associated with additional positive nodes in the AD arm (OR 1.04, 95% CI 1.02-1.06, p < 0.0001). We observed significant association with higher MDA nomogram-estimated risk and SCV radiation (OR 1.07, 95% CI 1.04-1.10, p < 0.0001) but not HT (OR 0.99, 95% CI 0.96-1.02, p = 0.52) The MSK nomogram had similar associations.

CONCLUSION

MDA and MSK nomogram risk estimates were associated with lymph node risk in ACOSOG Z0011. Radiation oncologists' use of differing radiation fields were associated with treating higher risk patients. ClinicalTrials.gov id: NCT00003854.

A Novel Nomogram Predicting Distant Metastasis in T1 and T2 Gallbladder Cancer: A SEER-based Study

Background: Gallbladder cancer (GBC) is the most common malignancy of the biliary system. Early T stage GBC patients with distant metastasis are proven to have a worse prognosis. In this study, our aim was to construct and validate a novel nomogram for predicting distant metastasis in T1 and T2 GBC. Methods: Between 2004 and 2014, patients with T1 and T2 GBC were identified in the Surveillance, Epidemiology, and End Results (SEER) database. All of the eligible patients were randomly divided into training and validation cohorts. Univariate and multivariate analyses were used to assess significant predictive factors associated with distant metastasis. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve (ROC) analysis. Results: According to the inclusion and exclusion criteria, 3013 patients with historically confirmed AJCC stage T1 and T2 GBC were enrolled. Younger age, high pathological grade, nonadenocarcinoma, T1, N1 and larger tumor size correlated positively with the risk of distant metastasis. A novel nomogram was established to predict distant metastasis in early T stage GBC patients. Internal validation with a calibration plot in the training cohort showed that this nomogram was well calibrated. Through ROC curve analysis, the areas under the ROC curves in the training and validation cohorts were 0.723 and 0.679, respectively. Conclusions: Although some limitations exist in this predictive model, the nomogram revealed the relationship between the clinicopathological characteristics of T1 and T2 GBC patients and the risk of distant metastasis. The novel nomogram will assist in patient counseling and guide treatment decision making for T1 and T2 GBC patients.

Imaging Predictors for Nonsentinel Lymph Node Metastases in Breast Cancer Patients

Background

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

Objectives

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

Method

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

Results

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

Conclusions

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

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program

Background: Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer.

Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and clinicopathological characteristics. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve analysis.

Result: Age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status were independent predictive factors of positive lymph node metastasis in T1 breast cancer. Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. We developed a nomogram to predict lymph node metastasis and further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 and 0.741 in the training and validation sets, respectively.

Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients might important for assessing their lymph node status. The nomogram developed here, if further validated in other large cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis.

Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients

Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.

Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC).

Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%).

Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.

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.

Intraoperative prediction of the two axillary lymph node macrometastases threshold in patients with breast cancer using a one-step nucleic acid cytokeratin-19 amplification assay

The aim of the present study was to assess the sensitivity, specificity and practicality of using a one-step nucleic acid amplification (OSNA) assay during breast cancer staging surgery to predict and discriminate between at least 2 involved nodes and more than 2 involved nodes and facilitate the decision to provide axillary conservation in the presence of a low total axillary node tumour burden. A total of 700 consecutive patients, not treated with neo-adjuvant chemotherapy, received intraoperative sentinel lymph node (SLN) analysis using OSNA for cT1-T3 cN0 invasive breast cancer. Patients with at least one macrometastasis on whole-node SLN analysis underwent axillary lymph node dissection (ALND). The total tumour load (TTL) of the macrometastatic SLN sample was compared with the non-sentinel lymph node (NSLN) status of the ALND specimen using routine histological assessment. In total, 122/683 patients (17.9%) were found to have an OSNA TTL indicative of macrometastasis. In addition, 45/122 (37%) patients had NSLN metastases on ALND with a total positive lymph node burden exceeding the American College of Surgeons Oncology Group Z0011 trial threshold of two macrometastatic nodes. The TTL negative predictive value was 0.975 [95% confidence interval (CI), 0.962-0.988]. The area under the curve for the receiver operating characteristic curve was 0.86 (95% CI, 0.81-0.91), indicating that SLN TTL was associated with the prediction (and partitioning) of total axillary disease burden. OSNA identifies a TTL threshold value where, in the presence of involved SLNs, ALND may be avoided. This technique offers objective confidence in adopting conservative management of the axilla in patients with SLN macrometastases.

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.

The Pre-Treatment Neutrophil-Lymphocyte Ratio: a Useful Tool in Predicting Non-Sentinel Lymph Node Metastasis in Breast Cancer Cases

Background:

The sentinel lymph node (SLN) biopsy is a highly accurate predictor of overall axillary nodal status in early breast cancer patients. There is however, still a debate on which patients with a positive SLN can benefit from axillary lymph node dissection (ALND). Numerous studies have been designed to identify variables that are predictive of non-SLN metastasis to avoid a complete ALND. The aim of this study was to determine whether the pre-treatment neutrophil-lymphocyte ratio (NLR) can be a predictive factor of non-SLN metastasis in early breast cancer patients.

Materials and Methods:

The records of 214 consecutive patients with cT1-3N0 invasive breast cancer who had undergone intraoperative SLN evaluation at Songklanagarind Hospital between the 1st of March 2011 and the 30th of May 2016 were examined. Data on patient demographics, tumor variables and NLR were collected and factors for non-SLN metastasis were analyzed using multivariate logistic regression. The power of the NLR was quantified with receiver operating characteristics (ROC) curves as measured by the areas under curves (AUC).

Results:

Multivariate analysis established presence of lymphovascular invasion (OR 8.4, 95%CI 2.3-31.3, p=0.002), macrometastasis (OR 6.6, 95%CI 1.8-24.7, p=0.005), and NLR (OR 2.3, 95%CI 1.1-4.8, p=0.033) as predictive factors of non-SLN metastasis with statistical significance. The AUC for NLR was 0.7 (95%CI 0.6-0.8) with an optimal cut-off of 2.6 giving a sensitivity of 62%, a specificity of 83.8%, a positive predictive value of 77.3% and a negative predictive value of 70.5%.

Conclusion:

Pre-treatment NLR is a useful diagnostic aid for predicting additional non-SLN metastasis.

Elaboration of a nomogram to predict nonsentinel node status in breast cancer patients with positive sentinel node, intraoperatively assessed with one step nucleic amplification: Retrospective and validation phase

BACKGROUND

Tumor-positive sentinel lymph node (SLN) biopsy results in a risk of non sentinel node metastases in micro- and macro-metastases ranging from 20 to 50%, respectively. Therefore, most patients underwent unnecessary axillary lymph node dissections. We have previously developed a mathematical model for predicting patient-specific risk of non sentinel node (NSN) metastases based on 2460 patients. The study reports the results of the validation phase where a total of 1945 patients were enrolled, aimed at identifying a tool that gives the possibility to the surgeon to choose intraoperatively whether to perform or not axillary lymph node dissection (ALND).

METHODS

The following parameters were recorded: Clinical: hospital, age, medical record number; Bio pathological: Tumor (T) size stratified in quartiles, grading (G), histologic type, lymphatic/vascular invasion (LVI), ER-PR status, Ki 67, molecular classification (Luminal A, Luminal B, HER-2 Like, Triple negative); Sentinel and non-sentinel node related: Number of NSNs removed, number of positive NSNs, cytokeratin 19 (CK19) mRNA copy number of positive sentinel nodes stratified in quartiles. A total of 1945 patients were included in the database. All patient data were provided by the authors of this paper.

RESULTS

The discrimination of the model quantified with the area under the receiver operating characteristics (ROC) curve (AUC), was 0.65 and 0.71 in the validation and retrospective phase, respectively. The calibration determines the distance between predicted outcome and actual outcome. The mean difference between predicted/observed was 2.3 and 6.3% in the retrospective and in the validation phase, respectively. The two values are quite similar and as a result we can conclude that the nomogram effectiveness was validated. Moreover, the ROC curve identified in the risk category of 31% of positive NSNs, the best compromise between false negative and positive rates i.e. when ALND is unnecessary (<31%) or recommended (>31%).

CONCLUSIONS

The results of the study confirm that OSNA nomogram may help surgeons make an intraoperative decision on whether to perform ALND or not in case of positive sentinel nodes, and the patient to accept this decision based on a reliable estimation on the true percentage of NSN involvement. The use of this nomogram achieves two main gools: 1) the choice of the right treatment during the operation, 2) to avoid for the patient a second surgery procedure.

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.

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

BACKGROUNDS

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

METHODS

The following parameters were recorded:

CLINICAL

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

RESULTS

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

CONCLUSIONS

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

Clinicopathologic features predicting involvement of non- sentinel axillary lymph nodes in Iranian women with breast cancer

BACKGROUND

Almost half of the breast cancer patients with positive sentinel lymph nodes have no additional disease in the remaining axillary lymph nodes. This group of patients do not benefit from complete axillary lymph node dissection. This study was designed to assess the clinicopathologic factors that predict non-sentinel lymph node metastasis in Iranian breast cancer patients with positive sentinel lymph nodes.

MATERIALS AND METHODS

The records of patients who underwent sentinel lymph node biopsy, between 2003 and 2012, were reviewed. Patients with at least one positive sentinel lymph node who underwent completion axillary lymph node dissection were enrolled in the present study. Demographic and clinicopathologic characteristics including age, primary tumor size, histological and nuclear grade, lymphovascular invasion, perineural invasion, extracapsular invasion, and number of harvested lymph nodes, were evaluated.

RESULTS

The data of 167 patients were analyzed. A total of 92 (55.1%) had non-sentinel lymph node metastasis. Univariate analysis of data revealed that age, primary tumor size, histological grade, lymphovascular invasion, perineural invasion, extracapsular invasion, and the number of positive sentinel lymph nodes to the total number of harvested sentinel lymph nodes ratio, were associated with non-sentinel lymph node metastasis. After logistic regression analysis, age (OR=0.13; 95% CI, 0.02-0.8), primary tumor size (OR=7.7; 95% CI, 1.4-42.2), lymphovascular invasion (OR=19.4; 95% CI, 1.4- 268.6), extracapsular invasion (OR=13.3; 95% CI, 2.3-76), and the number of positive sentinel lymph nodes to the total number of harvested sentinel lymph nodes ratio (OR=20.2; 95% CI, 3.4-121.9), were significantly associated with non-sentinel lymph node metastasis.

CONCLUSIONS

According to this study, age, primary tumor size, lymphovascular invasion, extracapsular invasion, and the ratio of positive sentinel lymph nodes to the total number of harvested sentinel lymph nodes, were found to be independent predictors of non-sentinel lymph node metastasis.

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.

p53 expression in breast cancer predicts tumors with low probability of non-sentinel nodes infiltration

AIM

Several predictive tools of non-sentinel lymph nodes neoplastic involvement when a positive sentinel lymph node is found have been described. However, molecular factors have been rarely evaluated to build these tools. The aim of this study was to establish which factors predicted non-sentinel lymph nodes infiltration in our setting, including some molecular factors.

MATERIAL AND METHODS

We carried out a retrospective review of 161 patients with breast cancer and a positive sentinel lymph node who had undergone axillary lymph node dissection, none of whom had received neoadjuvant treatment. Features evaluated as predictive factors for non-sentinel node positivity were: menopausal status, tumor size, histological subtype, histological grade, lymphovascular invasion, extracapsular invasion, Ki67 index, hormonal receptors, CerbB2 and p53 expression, size of sentinel lymph node metastases and number of sentinel lymph nodes affected.

RESULTS

Tumor size (P = 0.001), size of sentinel lymph node metastases (P = 0.001), lobular invasive carcinoma (P = 0.05) and lymphovascular invasion (P = 0.006) were significantly associated with non-sentinel lymph node positivity. Tumor p53 positive expression was strongly associated with non-sentinel lymph node negativity (P = 0.000). In multivariate analysis, all these factors but tumor size maintained their significance. The discrimination power of the model calculated by the area under the receiver-operator curve was 0.811 (95% confidence interval, 0.741-0.880).

CONCLUSION

p53 expression in breast cancer was highly predictive of non-sentinel lymph node negativity in our study. New studies should evaluate if it would be useful to add p53 expression to other existing predictive tools.