A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes

Abstract

We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676-0.785) for the training, 0.701 (95% CI 0.630-0.773) for internal validation, and 0.813 (95% CI 0.734-0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.

Figure 1

Flowchart of patient selection and nomogram construction. ALND: axillary lymph node dissection.

Figure 2

Nomogram predicting the probability of NSLN metastasis. The five variables were located in rows 2–6 of the nomogram. The corresponding scores were obtained by inputting each of the patient's characteristics into the nomogram and drawing an upward line to row 1 (Points). The scores were then summed and a line was drawn downwards to row 7 (Total points) to obtain the total score. Put the total score in a downward line to row 8 (Risk of NSLN metastasis) to determine the risk of NSLN metastasis.

Figure 3

Nomogram verification for training cohort (a–c), internal validation cohort (d–f), and external validation cohort (g-i). a, d, g: ROC curves. b, e, h: calibration curves. The "ideal" line as the reference standard. The "apparent" line shows the agreement between the observed and predicted probabilities. The "bias-corrected" line shows the agreement between the corrected predicted and the observed probabilities. A closer proximity of the apparent or bias-corrected line to the ideal line indicates better consistency between predicted values and actual values. c, f, i: DCA curves. The "treat all" line represents the assumption that all non-sentinel lymph nodes of the patients were positive. The "treat none" line represents the assumption that none non-sentinel lymph nodes of the patients were positive. The "model" line represents the nomogram. The DCA curve lies above the "none" and "all" baselines in the threshold probability range of 0.1 to 0.7, indicating acceptable model performance in this range. ROC: receiver operating characteristics; AUC: areas under the ROC curve; CI: confidence interval; DCA: decision curve analysis.