Development and validation of a nomogram for false-negative results in fine-needle aspiration of axillary lymph nodes in breast cancer.

OBJECTIVE

To construct and validate a nomogram for predicting false-negative results of axillary lymph node (ALN) fine needle aspiration (FNA) in breast cancer (BC).

METHODS

Using the surgical pathological results of axillary lymph nodes (ALNs) in BC patients as the gold standard, we retrospectively analyzed the clinical, pathological, and ultrasonographic characteristics of patients with false-negative lymph FNA results and identified predictive factors. Based on the independent predictors screened, a nomogram prediction model was constructed and validated using the Hosmer-Lemeshow test, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The calibration curve was drawn by bootstrap method for internal verification.

RESULTS

Univariate analysis revealed that the following five factors were statistically significant predictors of false-negative results (P < 0.05): ultrasound features of ALN shape, corticomedullary boundary, hilum status, histological type of the primary tumor from core needle biopsy (CNB), and short-axis diameter of ALNs on ultrasound. Multivariate analysis identified three independent predictors of false-negative FNA results (P < 0.05): ultrasound features of ALN shape, corticomedullary boundary, and histological type of the primary tumor from CNB. A nomogram prediction model was successfully developed based on these independent predictors. The Hosmer-Lemeshow test yielded a P-value of 1, the area under the ROC curve (AUC) was 0.782, and the DCA threshold range for the nomogram was 0.03-0.95. The calibration curve was drawn by bootstrap method for internal verification, and the conclusion was that N = 247, Mean absolute error = 0.012, Mean squared error = 0.00035.

CONCLUSION

A nomogram model was constructed to predict false-negative FNA results in ALNs of BC patients, demonstrating good predictive performance.