Nomograms for preoperative prediction of axillary nodal status in breast cancer

Abstract

BackgroundAxillary staging in patients with breast cancer and clinically node‐negative disease is performed by sentinel node biopsy (SLNB). The aim of this study was to integrate feasible preoperative variables into nomograms to guide clinicians in stratifying treatment options into no axillary staging for patients with non‐metastatic disease (N0), SLNB for those with one or two metastases, and axillary lymph node dissection (ALND) for patients with three or more metastases.MethodsPatients presenting to Skåne University Hospital, Lund, with breast cancer were included in a prospectively maintained registry between January 2009 and December 2012. Those with a preoperative diagnosis of nodal metastases were excluded. Patients with data on hormone receptor status, human epidermal growth factor receptor 2 and Ki‐67 expression were included to allow grouping into surrogate molecular subtypes. Based on logistic regression analyses, nomograms summarizing the strength of the associations between the predictors and each nodal status endpoint were developed. Predictive performance was assessed using the area under the receiver operating characteristic (ROC) curve. Bootstrap resampling was performed for internal validation.ResultsOf the 692 patients eligible for analysis, 248 were diagnosed with node‐positive disease. Molecular subtype, age, mode of detection, tumour size, multifocality and vascular invasion were identified as predictors of any nodal disease. Nomograms that included these predictors demonstrated good predictive abilities, and comparable performances in the internal validation; the area under the ROC curve was 0·74 for N0 versus any lymph node metastasis, 0·70 for one or two involved nodes versus N0, and 0·81 for at least three nodes versus two or fewer metastatic nodes.ConclusionThe nomograms presented facilitate preoperative decision‐making regarding the extent of axillary surgery.

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

“…alone and the presented SLNB reduction rate was higher for the MIXED predictor compared to the CLINICAL predictor. Notably, the AUCs attained in our models are similar to those of prediction models previously published for nodal metastasis (AUC 0.67-0.78), which included only clinicopathologic data (18)(19)(20)(21)30), but did not report on possible SLNB reduction rate. Although the addition of gene expression data to existing clinicopathologic variables did not show a clear superiority in predicting nodal status, it should be noted that machinelearning approaches identify the most dominant trait for each predictor type and capture different metastatic features.…”

“…One problem with developing mixed gene expression classifiers for the classification question at hand is the scarcity of larger cohorts including gene expression-and relevant clinicopathologic data that is commonly used in, for example, nomogram methods (such as lymphovascular invasion, mode of detection, multifocality and tumor location; refs. 18,30). To further validate the MIXED classifier for ER þ HER2 À tumors, we used the TCGA dataset (44) comprising 503 primary breast tumors.…”

“…The classifiers developed to date are not sufficient to support relevant clinical conclusions (29). A remaining question is thus whether gene expression profiling alone or in combination with clinical variables may surpass the performance of existing methods, for instance, nomogram methods that report AUCs between 0.70 and 0.75, in performance (18,30).…”

Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical‐related features for axillary lymph node metastasis in early‐stage invasive breast cancer

“…Recently, several studies are evaluating the patient's likelihood of ALN metastasis in BC, including estimated risks of SLN metastasis or non‐SLN metastasis. Dihge et al . presented a noninvasive predictive nomogram model, with an C‐index of 0.74 for N0 versus any lymph node metastasis.…”

“…9 Recently, several studies are evaluating the patient's likelihood of ALN metastasis in BC, including estimated risks of SLN metastasis or non-SLN metastasis. Dihge et al 26 presented a noninvasive predictive nomogram model, with an C-index of 0.74 for N0 versus any lymph node metastasis. The above predictive model includes clinicopathological factors such as tumor size, vascular invasion and molecular subtype, and so on but lacks AUS and molecular markers detection.…”