Validation of Memorial Sloan–Kettering Cancer Center nomogram for prediction of non-sentinel lymph node metastasis in sentinel lymph node positive breast cancer patients an international comparison

Non-sentinel lymph node metastases risk factors in patients with breast cancer with one or two sentinel lymph node macro-metastases

Approximately 59 % of patients with breast cancer with one or two sentinel lymph nodes (1-2 SLN) macrometastases do not benefit from axillary lymph node dissection (ALND), which may also incur morbidities. It is necessary to evaluate the association between various clinicopathological characteristics and non-sentinel lymph node metastases (non-SLNM) in patients with breast cancer with 1-2 SLN macrometastases, and determine whether they 1-2 should avoid ALND. Eight electronic literature databases (PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal, Wanfang, and Chinese Biomedical Literature) were searched from their inception to June 30, 2023, and two reviewers independently extracted the data and assessed the risk of bias. Association strength was summarized using odds ratios (OR) and 95 % confidence intervals (CI). Heterogeneity was accounted for using a subgroup analysis. Publication bias was evaluated using funnel plots and Egger's test. There were 25 studies with 8021 participants, and 27 potential risk factors were evaluated. The risk factors for non-SLNM in patients with 1-2 SLN macrometastatic breast cancer include the following: factors of primary tumor: multifocality (OR (95 % CI (2.63 (1.96, 3.54))), tumor size ≥ T2 (2.64 (2.22, 3.14)), tumor localization (upper outer quad) (2.06 (1.23, 3.43)), histopathological grade (G3) (2.45 (1.70, 3.52)), vascular invasion (VI) (2.60 (1.35, 4.98)), lymphovascular invasion (LVI) (2.87 (1.80, 4.56)), perineural invasion (PNI) (3.16 (1.18,8.43)). Factors of lymph nodes: method of SLNs detected (blue dye) (3.85 (1.54, 9.60)), SLN metastasis ratio ≥0.5 (2.79 (2.24, 3.48)), two positive SLNs (3.55, (2.08, 6.07)), zero negative SLN (3.72 (CI 2.50, 4.29)), extranodal extension (ENE) (4.69 (2.16, 10.18)). Molecular typing: Her-2 positive (2.08 (1.26, 3.43)), Her-2 over-expressing subtype (1.83 (1.22, 2.73)). Factors of examination/inspection: axillary lymph nodes (ALNs) positive on imaging (3.18 (1.68, 6.00)), cancer antigen 15-3 (CA15-3) (4.01 (2.33,6.89)), carcinoembryonic antigen (CEA) (2.13 (1.32-3.43)). This review identified the risk factors for non-SLNM in patients with 1-2 SLN macrometastatic breast cancer. However, additional studies are needed to confirm the above findings owing to the limited number and types of studies included.

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

BACKGROUND

Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.

METHODS

From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared.

RESULTS

NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706).

CONCLUSIONS

The ML model XGBoost can well predict NSLNM in breast cancer patients.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis

Objective

Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.

Methods

Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.

Results

A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively.

Conclusion

ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

Axillary Lymph Nodes in Breast Cancer Patients After COVID-19 Vaccine

One of the side effects of vaccines used to end the COVID-19 epidemic is non-specifically enlarged axillary lymph nodes. Such lymphadenopathy detected during clinical examination of breast cancer patients may require additional imaging or interventional procedures that should not normally be performed. This study has been designed to estimate the incidence of palpable enlarged axillary lymph node in breast cancer patients who had received COVID-19 vaccination in the past 3 months in the same arm as compared to those without vaccination. Breast cancer patients admitted to M.U. Medical Faculty Breast polyclinic between January 2021 and March 2022 were screened, and clinical staging was performed after thorough clinical examination. Among these patients with suspected enlarged axillary lymph node and those undergoing sentinel lymph node biopsy (SLNB), they were divided into two groups as vaccinated and unvaccinated. Age, menopausal status, tumor size, tumor location, surgery, pathology results, hormonal receptor status, and SLNB results were statistically compared with groups. There was no significant difference between groups in terms of age, menopause, tumor size, tumor location, surgery, pathological results, and hormone receptor status. The SLNB being reported as reactive only was 89.1% in the vaccinated group and 73.2% in the non-vaccinated group which was statistically significant different. Reactive lymph nodes were commonly found with an excess of 16% in patients who had received COVID-19 vaccination in the past 3 months. This required caution and additional examination of the axillary lymph nodes in this period.

Predicting the difficult laparoscopic cholecystectomy based on a preoperative scale

It is important to establish the difficulty of a cholecystectomy preoperatively to improve the outcomes. There are multiple risk factors for a difficult cholecystectomy that may depend on the patient, the disease, or extrinsic factors. The aim of this study is to evaluate the predictive capacity of a difficult cholecystectomy with a preoperative scale. A diagnostic trial study was designed to evaluate the performance of a scale to predict the difficulty of laparoscopic cholecystectomy, considering as a reference standard the intraoperative findings evaluated according to an intraoperative difficulty scale. A ROC curve was performed and used to estimate predictive value of the preoperative score to predict the difficulty of a cholecystectomy preoperatively. The ROC curve shows an area of 0.88 under the curve. The calculated ideal cutoff was 8, with a sensitivity, specificity, positive predictive value and negative predictive value of 75.15%, 88.31%, 87.32 and 76.83%, respectively. It was demonstrated that, as the difficulty predicted by the preoperative scale increases, the rate of conversion to open procedure, the rate of subtotal cholecystectomies, the rate of complication and the rate of a critical view of safety failed increase. We suggest implementing the preoperative scale in all patients who are planning laparoscopic cholecystectomy, considering it a simple and easy tool to perform. This to inform the patient, organize the surgery schedule, select personnel, request support and have adequate pre-operative planning.

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.

Predictors of nonsentinel lymph node metastasis in patients with breast cancer with metastasis in the sentinel node

To predict the factors related to axillary nonsentinel lymph node (NSLN) metastasis in patients with positive sentinel lymph node (SLN) of early breast cancer.The retrospective data are collected from the patients with positive SLN who received further completion axillary lymph node dissection (cALND) in Peking Union Medical Hospital between March 2016 and December 2017. Univariate analysis was conducted on data with various clinicopathologic factors at first. Those factors with statistic significance (P < .05) in univariate analysis were then used to implement multivariate analysis and logistic regression.There were total of 734 patients who received SLN biopsy , among whom 153 cases were included in our study. About 39.22% (60/153) of 153 paitents with positive SLN had no NSLN metastasisted to SLN. Univariate analysis showed that 3 variables were significantly correlated with NSLN involvement: tumor size (X = 10.384, P = .001), SLN metastasis ratio (number of positive SLNs/number of SLNs removed × 100%) (X = 10.365, P = .001) and the number of negative sentinel nodes (X = 10.384, P = .006). In multivariate analysis and logistic regression, tumor size (odds ratio [OR] = 3.392, 95% confidence interval [CI]: 1.409-8.166, P = .006) and SLN metastasis ratio (OR = 3.514, 95% CI: 1.416-8.72, P = .007) were the independent risk factors. While the number of negative sentinel nodes (OR = 0.211, 95% CI: 0.063-0.709, P = .014) was the independent protective factor. The calculated risk resulted in an area under the curve of 0.746 (95% CI: 0.644-0.848), suggesting stable discriminative capability in Chinese population.For those patients with positive SLN, larger tumor burden and SLN metastasis ratio are independent risk factors for NSLN metastasis. However, the more of the detected negative SLN, the less possibility with NSLN involvement.

Impact of Size of the Tumour, Persistence of Estrogen Receptors, Progesterone Receptors, HER2Neu Receptors and Ki67 Values on Positivity of Axillary Lymph Nodes in Patients with Early Breast Cancer with Clinically Negative Axillary Examination

AIM:

The study aimed to identify factors that influence the positivity of axillary lymph nodes in patients with early breast cancer and clinically negative axillary lymph nodes, who were subjected for modified radical mastectomy and axillary lymphadenectomy.

MATERIAL AND METHODS:

This study included 81 surgically treated, early breast cancer patients during the period from 08-2015 to 05-2017. All the cases have been analysed by standard histological analysis including macroscopic and microscopic examination by routine H&E staining. For determination of molecular receptors, immunostaining by PT LINK immunoperoxidase has been done for HER2neu, ER, PR, p53 and Ki67.

RESULTS:

Patients age ranged between 31-73 years, an average of 56.86 years. The mean size of a primary tumour in the surgically treated patient was 20.33 ± 6.0 mm. Axillary dissection revealed from 5 to 32 lymph nodes, with an average of 14. Metastases have been found in 1 to 7 lymph nodes, with an average 0.7. Only 26 (32.1%) of the patients showed metastases in the axillary lymph nodes. The univariant regression analysis showed that the size of a tumour and presence of HER2neu receptors on cancer cells influence the positivity of the axillary lymph nodes. The presence of the estrogen receptors, progesterone receptors have no influence on the positivity for metastatic deposits of lymph nodes. Multivariant model and logistic regression analysis as significant independent factors or predictors of positivity of the axillary lymph nodes are influenced by the tumour size only.

CONCLUSION:

Our study showed that the metastatic involvement of the axillary lymph nodes is mainly influenced by the size of a tumour and presence of HER2neu receptors in the univariant analysis. This point to the important influence of positivity of the axillary lymph nodes but, in multi-variant regressive analysis the lymph node status correlates with the tumour size only.

Prognostic Factors on the Positivity for Metastases of the Axillary Lymph Nodes from Primary Breast Cancer

AIM

The aim of the study was to identify the impact of T stage, the presence of estrogen, progesterone, HER2neu receptors and the values of the Ki67 on the positivity for metastases of the axillary lymph nodes, from primary breast cancer.

MATERIAL AND METHODS

290 surgically treated patients for breast cancer were included in the study. All cases have been analyzed by standard histological analysis including microscopic analysis on standard H&E staining. For determining the molecular receptors - HER2neu, ER, PR, p53 and Ki67, immunostaining by PT LINK immunoperoxidase has been done.

RESULTS

Patients age was ranged between 18-90 years, average of 57.6+11.9. The mean size of the primary tumor in the surgically treated patient was 30.27 + 18.3 mm. On dissection from the axillary pits 8 to 39 lymph nodes were taken out, an average of 13.81+5.56. Metastases have been found in 1 to 23 lymph nodes, an average 3.14+4.71. In 59% of the patients there have been found metastases in the axillary lymph nodes. The univariate regression analysis showed that the location, size of tumor, differentiation of the tumor, stage, the value of the Ki67 and presence of lymphovascular invasion influence on the positivity of the axillary lymph nodes. The presence of the estrogen receptors, progesterone receptors and HER2neu receptors showed that they do not have influence on the positivity for metastatic deposits in axillary lymph nodes. The multivariate model and the logistic regression analysis as independent significant factors or predictors of positivity of the axillary lymph nodes are influenced by the tumor size and the positive lymphovascular invasion.

CONCLUSION

Our study showed that the involving of the axillary lymph nodes is mainly influenced by the size of the tumor and the presence of lymphovascular invasion in the tumor. Ki67 determined proliferative index in the univariate analysis points the important influence of positivity in the axillary lymph nodes, but not in the multivariate regressive analysis.

Comparison of clinicopathological characteristics of lymph node positive and lymph node negative breast cancer

OBJECTIVE

To record various clinicopathological characteristics of breast cancer (BC) in our population and to find an association between these characteristics and axillary nodal metastasis.

METHODS

This cross-sectional study included 150 BC patients from two tertiary care centers in Karachi from 15(th) February, 2013 to 31(st) March, 2015. Frequencies, percentages, and odds ratio were estimated to find out an association between various clinicopathological characteristics and lymph node status using SPSS version 20.

RESULTS

Approximately 75.4% patients had axillary lymph node metastasis ('1-3' LN = 34.4% and '>3' LN = 44%). Menopausal status (p <0.013), tumor grades ('II' p <0.03; 'III' p <0.01), and stages ('III' p <0.002; 'IV' p <0.0001), tumor sizes ('T2' p <0.014; 'T3' p <0.002), perineural invasion (PNI) (p <0.007), lymphovascular invasion (LVI) (p <0.0001), and skin and nipple invasion (p <0.024) were significant predictors for '>3' LN metastasis. Association of these variables with '1-3' LN involvement was insignificant.

CONCLUSION

Clinical spectrum of BC remains unchanged in 2016 with most of the patients presenting with high-grade, late-stage advanced disease. Moreover, clinicopathological variables, especially primary tumor size, tumor stage and lymphovascular invasion were significant predictors of >3 lymph node metastasis with high accuracy.

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.

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

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

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

AIMS AND BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Evaluation of the Probability of Non-sentinel Lymph Node Metastasis in Breast Cancer Patients with Sentinel Lymph Node Metastasis using Two Different Methods

OBJECTIVE

The aim of this retrospective clinical study was to evaluate the accuracy and feasibility of two different clinical scales, namely the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Tenon's axillary scoring system, which were developed for predicting the non-sentinel lymph node (NSLN) status in our breast cancer patients.

MATERIAL AND METHODS

The medical records of patients who were diagnosed with breast cancer between January 2010 and November 2013 were reviewed. Those who underwent sentinel lymph node biopsy (SLNB) for axillary staging were recruited for the study, and patients who were found to have positive SLNB and thus were subsequently subjected to axillary lymph node dissection (ALND) were also included. Patients who had neoadjuvant therapy, who had clinically positive axilla, and who had stage 4 disease were excluded. Patients were divided into two groups. Group 1 included those who had negative NSLNs, whereas Group 2 included those who had positive NSLNs. The following data were collected: age, tumor size, histopathological characteristics of the tumor, presence of lymphovascular invasion, presence of multifocality, number of negative and positive NSLNs, size of metastases, histopathological method used to define metastases, and receptor status of the tumor. The score of each patient was calculated according to the MSKCC nomogram and Tenon's axillary scoring system. Statistical analysis was conducted to investigate the correlation between the scores and the involvement of NSLNs.

RESULTS

The medical records of patients who were diagnosed with breast cancer and found to have SLNB for axillary staging was reviewed. Finally, 50 patients who had positive SLNB and thus were subsequently subjected to ALND were included in the study. There were 17 and 33 patients in Groups 1 and 2, respectively. Both the MSKCC nomogram and Tenon's axillary scoring system were demonstrated to be significantly accurate in the prediction of the involvement of NSLNs (p<0.05 for each). Among all the parameters, the only one that was found to be correlated with the risk of NSLN involvement was the presence of lymphovascular invasion.

CONCLUSION

The MSKCC nomogram and Tenon's axillary scoring system both seem to be reliable tools for the assessment of NSLN status in SLNB-positive breast cancer in our breast cancer population. Nevertheless, the omission of ALNB in SLNB-positive breast cancer cannot be yet recommended because of the lack of long-term results of current nomograms and scoring systems.

Added value of blue dye injection in sentinel node biopsy of breast cancer patients: do all patients need blue dye?

BACKGROUND

In the current study, we evaluated the incremental value of blue dye injection in sentinel node mapping of early breast cancer patients. We specially considered the experience of the surgeons and lymphoscintigraphy results in this regard.

METHODS

605 patients with early stage breast cancer were retrospectively evaluated in the study. Patients underwent sentinel node mapping using combined radiotracer and blue dye techniques. Lymphoscintiraphy was also performed for 590 patients. Blue dye, radioisotope, and overall success rates in identifying the sentinel lymph node were evaluated in different patient groups. The benefit of blue dye and radioisotope in identifying the sentinel lymph nodes was also evaluated.

RESULTS

Marginal benefits of both blue dye and isotope for overall sentinel node detection as well as pathologically involved sentinel nodes were statistically higher in inexperienced surgeons and in patients with sentinel node visualization failure. In the patients with sentinel node visualization on lymphoscintigraphy, 6 sentinel nodes were detected by blue dye only. All these six nodes were harvested by inexperienced surgeons. On the other hand 8 sentinel nodes were detected by dye only in the patients with sentinel node non-visualization. All these nodes were harvested by experienced surgeons.

CONCLUSIONS

The use of blue dye should be reserved for inexperienced surgeons during their learning phase and for those patients in whom lymphoscintigraphy failed to show any uptake in the axilla.