Prediction of Residual Axillary Lymph Node Metastasis Following Neoadjuvant Therapy for Breast Cancer: Habitat Radiomics Analysis Based on Breast MRI.

Authors

Junjie Zhang, Yi Dai, Ruxin Xu, Dilinuer Aishanjiang, Zhi Yin, Yanfen Cui, Xiaotang Yang

Published in

Academic radiology. Aug 26, 2026. Epub Aug 26, 2026.

Abstract

To develop and validate a habitat radiomics model based on pretreatment breast magnetic resonance imaging (MRI) in predicting residual axillary lymph node metastasis (RALNM) after neoadjuvant therapy (NAT) in patients with clinically node-positive (cN+) breast cancer.
This retrospective study included patients with cN+ breast cancer who underwent NAT at two centers between March 2018 and December 2022. Tumor region on pretreatment dynamic contrast-enhanced (DCE) MRI was segmented into distinct habitats using K-means clustering, and radiomics features were extracted from each habitat to build a habitat radiomics model. The conventional radiomics model was developed for comparison. Furthermore, a combined model integrating clinicopathological features with habitat radiomics signature was built. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
Among 686 included women (mean age ± standard deviation, 49.38 ± 10.37 years), RALNM was present in 351 patients (51.17%) after NAT. The habitat radiomics model achieved AUCs of 0.805 (95% confidence interval [CI]: 0.739-0.872) in the internal validation set and 0.802 (95% CI: 0.734-0.869) in the external test set, significantly outperforming the whole-tumor radiomics model in all cohorts (all p < 0.05). Integration of habitat radiomics with independent clinicopathological predictors yielded a combined model with further improved AUCs of 0.871 (95% CI: 0.816-0.926) and 0.857 (95% CI: 0.800-0.915) in the validation and external test sets, respectively.
Habitat radiomics analysis of pretreatment breast DCE-MRI demonstrates promising performance for predicting RALNM after NAT. The combined model incorporating habitat radiomics and clinicopathological factors further improves predictive accuracy.

PMID:
42648920
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.