Nonsentinel Axillary Lymph Node Status in Clinically Node-Negative Early Breast Cancer After Primary Systemic Therapy and Positive Sentinel Lymph Node: A Predictive Model Proposal

Abstract

Background: In clinically node-negative (cN0) early stage breast cancer (EBC) undergoing primary systemic treatment (PST), post-treatment positive sentinel lymph node (SLN+) directs axillary lymph node dissection (ALND), with uncertain impacts on outcomes and increased morbidities.

Patients and methods: We conducted an observational study on imaging-confirmed cN0 EBC, who underwent PST and breast surgery that resulted in SLN+ and underwent ALND. The association among baseline/postsurgical clinic-pathological factors and positive nonsentinel additional axillary lymph nodes (non-SLN+) was analyzed with logistic regression. LASSO regression (LR) identified variables to include in a predictive score of non-SLN+ (ALND-predict). The accuracy and calibration were assessed, an optimal cut-point was then identified, and in silico validation with bootstrap was undertaken.

Results: Non-SLN+ were detected in 22.2% cases after ALND. Only progesterone receptor (PR) levels and macrometastatic SLN+ were independently associated to non-SLN+. LR identified PR, Ki67, and type and number of SLN+ as the most efficient covariates. The ALND-predict score was built based on their LR coefficients, showing an area under the curve (AUC) of 0.83 and an optimal cut-off of 63, with a negative predictive value (NPV) of 0.925. Continuous and dichotomic scores had a good fit (p = 0.876 and p = 1.00, respectively) and were independently associated to non-SLN+ [adjusted odds ratio (aOR): 1.06, p = 0.002 and aOR: 23.77, p < 0.001, respectively]. After 5000 bootstrap-adjusted retesting, the estimated bias-corrected and accelerated 95%CI included the aOR.

Conclusions: In cN0 EBC with post-PST SLN+, non-SLN+ at ALND are infrequent (~22%) and independently associated to PR levels and macrometastatic SLN. ALND-predict multiparametric score accurately predicted absence of non-SLN involvement, identifying most patients who could be safely spared unnecessary ALND. Prospective validation is required.

Fig. 1

STROBE flow-chart. BC breast cancer, HCB hospital clinic of Barcelona, c clinical, T4d inflammatory BC, US ultrasound, MRI magnetic resonance imaging, SLN sentinel lymph node, SLNB sentinel lymph node biopsy, PST primary systemic treatment, ALND axillary lymph node dissection, NACT neoadjuvant chemotherapy, NET neoadjuvant endocrine therapy, + positive, FNAC fine needle biopsy, CNB core needle biopsy

Fig. 2

Patterns of SLN and non-SLN involvement. A Pattern of SLN involvement according to primary systemic therapy and in the overall population. B Number of non-SLN affected after ALND, according to SLN involvement type. C Type of non-SLN affected after ALND, according to SLN involvement within each IHC subtype. D Number of non-SLN affected after ALND, according to SLN involvement within each IHC subtype. IHC immunohistochemistry, SLN sentinel lymph node, ALND axillary lymph node dissection, NAC neoadjuvant chemotherapy, NET neoadjuvant endocrine therapy, ITC isolated tumor cells, Micro micrometastases, Macro macrometastases, HR hormone receptor, + positive, − negative, TNBC triple negative breast cancer. p values refer to Chi-squared tests

Fig. 3

Demographic and clinical feature selection using the LASSO binary logistic regression model and ROC curve of the final ALND-predict model. A and B Plots of the beta coefficient paths, representing the optimal parameter (λ) selection in the LASSO model. A cross-validation via minimum criteria was used. Each colored line represents the value taken by a different coefficient in the model. The partial likelihood deviance (binomial deviance) curve was plotted versus log(λ) in A, and the L1 Norm in B. λ is the weight given to the regularization term (the L1 norm) of the LASSO function. When λ is very small, the LASSO solution should be very close to the ordinary least square (OLS) solution, and all the coefficients are included in the model. In A, this is represented by smaller log(λ) values on the x axis being associated to higher number of variables entering the model. The x axis in B is the maximum permissible value the L1 Norm can take. Smaller L1 Norm values (left section of B) correspond to higher regularization, implying less variables with non-zero coefficients entering the model. C LASSO coefficient profiles of the variables. A coefficient profile plot was produced against the log(λ) sequence. Vertical line was drawn at the value selected using fivefold cross-validation, where optimal λ resulted in five features with nonzero coefficients. Each red dot is a λ value, with respective standard error (SE) depicted by the gray whiskers. The numbers on top are the number of nonzero regression coefficients in the model corresponding to each λ. From left to right along the x axis, with increasing fewer variables are included in the model, since the penalty for inclusion of features is weighted more heavily. The dashed lines are the log values corresponding to the λmin (left dashed line) and λ_1SE (right dashed line). D ROC curve of the ALND-predict model, with optimal cutoff point by the Youden Index. LASSO least absolute shrinkage and selection operator, TPR true positive rate, FPR false positive rate, ROC receiver operating characteristics