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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. | LitMetric

AI Article Synopsis

  • - The study focuses on early-stage breast cancer patients without clinically positive lymph nodes (cN0) who undergo primary systemic treatment (PST) and later have positive sentinel lymph nodes (SLN+), leading to axillary lymph node dissection (ALND) which can result in complications.
  • - Researchers analyzed clinic-pathological factors to find predictors of additional malignant nodes (non-SLN+) after ALND, identifying progesterone receptor levels and macrometastatic SLN+ as significant factors, ultimately developing the ALND-predict score for assessing these risks.
  • - Results indicated that only about 22% of patients had non-SLN+ after ALND, and the ALND-predict score demonstrated good accuracy,

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319670PMC
http://dx.doi.org/10.1245/s10434-023-13231-xDOI Listing

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