Value of Memorial Sloan-Kettering Cancer Center nomogram in clinical decision making for sentinel lymph node-positive breast cancer

Nomograms for Predicting Axillary Lymph Node Status Reconciled With Preoperative Breast Ultrasound Images

Introduction

The axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status.

Methods

A total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models.

Results

The AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set.

Conclusion

The ALNFM could effectively predict ALN status based on US images especially for different US tumor size.

Nomograms for predicting the likelihood of non-sentinel lymph node metastases in breast cancer patients with a positive sentinel node biopsy

BACKGROUND

Breast cancer patients with sentinel lymph node (SLN) metastases may have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the non-SLN metastases can avoid many complications mentioned the axillary lymph node dissection (ALND). This study aims to develop a new model based on Chinese real-world patients to ascertain the likelihood of non-SLN metastases in a breast cancer patient with disease-positive SLN, enabling the surgeons to make a better choice of surgical procedures.

METHODS

Out of the 470 patients from CSCO Breast Cancer Database collaborated Group, a proportion of 3 (347 cases): 1 (123 cases) was considered for assigning patients to training and validation groups, respectively. Two training models were created to predict the likelihood of having additional, non-SLN metastases in an individual patient. Training model 1 was created with pathological size of the tumor, pathological type, lymphovascular invasion, the number of positive SLNs/number of total SLNs ratio, and the Her-2 status based on multivariable logistic regression (P < .05). Training model 2 was based on the variables in model 1 and age, estrogen receptor status, progesterone receptor status, Ki-67 count, menopause status.

RESULTS

The area under the receiver operating characteristic (ROC) curve of the training model 1 was 0.754, while the area of training model 2 was 0.766. There was no difference between model 1 and model 2 regarding the ROC curve, P = .243. Next, the validation cohort (n = 123) was developed to confirm the model 1's performance and the ROC curve was 0.703. The nomogram achieved good concordance indexes of 0.754 (95% CI, 0.702-0.807) and 0.703 (95% CI, 0.609-0.796) in predicting the non-SLN metastases in the training and validation cohorts, respectively, with well-fitted calibration curves. The positive and negative predictive values of the nomogram were calculated, resulting in positive values of 59.3% and 48.6% and negative predictive values of 79.7% and 83.0% for the training and validation cohorts, respectively.

CONCLUSION

We developed 2 models that used information commonly available to the surgeon to calculate the likelihood of having non-SLN metastases in an individual patient. The numbers of variables in model 1 were less than in model 2, while model 1 had similar results as model 2 in calculating the likelihood of having non-SLN metastases in an individual patient. Model 1 was more user-friendly nomogram than model 2. Using model 1, the risk for an individual patient having ALND could be determined, which would lead to a rational therapeutic choice.

Risk factors and a predictive nomogram for non-sentinel lymph node metastases in Chinese breast cancer patients with one or two sentinel lymph node macrometastases and mastectomy

Background

Two ongoing prospective randomized trials are evaluating whether omitting axillary lymph node dissection (alnd) in patients with breast cancer (bca) and sentinel lymph node (sln) macrometastases undergoing mastectomy is safe. Determining predictive risk factors for non-sln metastases and developing a model to predict the probability of those patients having non-sln metastases is also important.

Methods

This retrospective study enrolled 396 patients with bca and 1-2 slns with macrometastases who underwent alnd and mastectomy between January 2012 and December 2016. Factors influencing the non-sln metastases were determined, and a predictive nomogram was formulated. Performance of the nomogram was evaluated by its area under the curve (auc).

Results

We developed a predictive nomogram with an auc of 0.81 (cross-validation 95% confidence interval: 0.75 to 0.86) that included 4 factors (tumour size, histologic grade, and number of negative slns and axillary lymph nodes on imaging).

Conclusions

Our predictive nomogram assesses the risk of non-sln metastases in patients with bca and 1-2 sln macrometastases undergoing mastectomy.

Sentinel Lymph Nodes for Breast Carcinoma: A Paradigm Shift

CONTEXT

-Sentinel lymph node biopsy has been established as the new standard of care for axillary staging in most patients with invasive breast carcinoma. Historically, all patients with a positive sentinel lymph node biopsy result underwent axillary lymph node dissection. Recent trials show that axillary lymph node dissection can be safely omitted in women with clinically node negative, T1 or T2 invasive breast cancer treated with breast-conserving surgery and whole-breast radiotherapy. This change in practice also has implications on the pathologic examination and reporting of sentinel lymph nodes.

OBJECTIVE

-To review recent clinical and pathologic studies of sentinel lymph nodes and explore how these findings influence the pathologic evaluation of sentinel lymph nodes.

DATA SOURCES

-Sources were published articles from peer-reviewed journals in PubMed (US National Library of Medicine) and published guidelines from the American Joint Committee on Cancer, the Union for International Cancer Control, the American Society of Clinical Oncology, and the National Comprehensive Cancer Network.

CONCLUSIONS

-The main goal of sentinel lymph node examination should be to detect all macrometastases (>2 mm). Grossly sectioning sentinel lymph nodes at 2-mm intervals and evaluation of one hematoxylin-eosin-stained section from each block is the preferred method of pathologic evaluation. Axillary lymph node dissection can be safely omitted in clinically node-negative patients with negative sentinel lymph nodes, as well as in a selected group of patients with limited sentinel lymph node involvement. The pathologic features of the primary carcinoma and its sentinel lymph node metastases contribute to estimate the extent of non-sentinel lymph node involvement. This information is important to decide on further axillary treatment.

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.

A head to head comparison of nine tools predicting non-sentinel lymph node status in sentinel node positive breast cancer women

BACKGROUND AND OBJECTIVES

This study was conducted to evaluate the performance of available tools predicting non-sentinel lymph node (non-SLN) status in women with SLN positive breast cancer and to see if they can be safely used in everyday clinical practice.

METHODS

Data of 220 women with breast cancer who underwent a SLN biopsy at the Máxima Medical Centre between 2000-2008 were analysed. Tools evaluated were: the models from Memorial Sloan Kettering Cancer Centre, Stanford, Mayo, Cambridge, Gur, and MOU, and the scores from Saidi, Tenon, and MDA. Model performance was assessed using calibration, discrimination and Nagelkerke's explained variation.

RESULTS

The MSKCC nomogram showed best overall performance with best discrimination (AUC 0.69), second best calibration, and highest explained variation (31%). The 10% low risk threshold led to defining only 22% (38/176) of the women as being low risk while in fact 66% (116/176) were non-SLN negative. The false negative rate was 13% (5/38).

CONCLUSIONS

Current models for predicting non-SLN metastases in SLN positive breast cancer are not yet ready for implementation in general practice. Further research efforts should improve model performance in selecting patients or perhaps find a role in support in the paradigm shift to a "treat none unless" approach.

A Paradigm Shift in Axillary Breast Cancer Treatment; From "Treat All-Except," Toward "Treat None-Unless"

The various pitfalls that exist in using predictive systems for sentinel node-positive breast cancer are described, to give insight into the background and building stones of these systems. The aim of this article was to make clinicians aware of these pitfalls before using them in the clinical decision-making process in individual patients with positive sentinel nodes. Besides these pitfalls, the implications concerning the results of the Z0011 trial as presented by Giuliano et al (Giuliano AE, et al. Locoregional recurrence after sentinel lymph node dissection with or without axillary dissection in patients with sentinel lymph node metastases: The American College of Surgeons Oncology Group Z0011 randomized trial. Ann Surg 2010; 252:426-32, discussion 432-3) are addressed, and we point out some important issues for debate, before implementation of the conclusions of that practice-changing trial into daily clinical breast cancer practice.

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.

An independent assessment of the 7 nomograms for predicting the probability of additional axillary nodal metastases after positive sentinel lymph node biopsy in a cohort of British patients with breast cancer

INTRODUCTION

Axillary lymph node dissection (ALND) is currently the recommended procedure in patients with tumor-positive sentinel lymph node biopsy (SLNB). A significant proportion of patients with positive SLNs will not have any additional metastases in nonsentinel lymph nodes (NSLNs). Predictive nomograms could identify a subgroup of patients with low or high risk of further disease in whom completion ALND can be avoided or recommended. The aim of this study was to assess the accuracy of the currently available 7 nomograms in a cohort of British patients with breast cancer.

PATIENTS AND METHODS

A total of 138 patients with positive SLNs who underwent completion ALND were identified. Data were then used to calculate the probability of further metastases in NSLNs predicted by the 7 nomograms that are currently in use: the MSKCC (Memorial Sloan Kettering Cancer Center), Cambridge, Turkish, Stanford, MDACC (University of Texas MD Anderson Cancer Center), Tenon, and MOU (Masarykuv onkologický ústav, Masaryk Memorial Cancer Institute) models. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for each nomogram.

RESULTS

Of the 138 patients, 54 (41%) had additional metastases in NSLNs. AUC values for the MSKCC, Cambridge, Turkish, Stanford, MDACC, Tenon, and MOU models are 0.68, 0.68, 0.70, 0.69, 0.56, 0.63, and 0.74, respectively.

CONCLUSION

The MOU nomogram was more predictive than the other nomograms, with a better AUC value and false-negative rate. None of the models were able to achieve AUC value ≥ 0.80 in a cohort of British patients with breast cancer.

Prognostic factors in elderly patients with breast cancer

BACKGROUND

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

METHODS

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

RESULTS AND DISCUSSION

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

CONCLUSIONS

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

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.