Predicting Pathologic ≥N2 Disease in Women With Breast Cancer

Authors

Kerollos Nashat Wanis
Wenli Dong
Yu Shen
Funda Meric-Bernstam
Taiwo Adesoye
Henry M Kuerer
Abigail S Caudle
Nina Tamirisa
Sarah M DeSnyder
Susie X Sun
Isabelle Bedrosian
Puneet Singh
Solange E Cox
Kelly K Hunt
Rosa F Hwang

Publication Date

5-22-2025

Journal

npj Breast Cancer

Abstract

The distinction between pN1 and ≥pN2 breast cancer impacts treatment decisions. Using data from a single institution on women with cN0 invasive breast cancer who were treated with upfront surgery, had 1-3 positive SLNs, and underwent completion ALND, we used gradient boosted trees (XGBoost) to develop a model for predicting ≥pN2 disease using clinicopathologic variables. Model performance was tested in a held-out subsample (20%) and validated using data from the National Cancer Database (NCDB). Of 3574 patients with cN0 breast cancer, 587 underwent upfront surgery and had 1-3 positive SLNs. Of these, 415 (70.7%) underwent completion ALND, with 64 (15.4%) having ≥pN2 disease. The trained algorithm had an AUC of 0.87 (95% CI: 0.74, 0.97) in the held-out test data, and 0.78 (95% CI: 0.76, 0.79) in recent NCDB data where completion ALND was much less commonly performed. The number of positive SLNs and the total number of SLNs removed had the greatest influence on model predictions in the held-out test data. The developed model effectively estimates the probability of ≥pN2 disease in cN0 patients with positive SLNs, providing guidance for the management of patients with breast cancer.

DOI

10.1038/s41523-025-00757-4

PMID

40404657

PMCID

PMC12098822

PubMedCentral® Posted Date

5-22-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes