Prediction of axillary lymph node metastasis using a transformer model and multi-omics validation in breast cancer.

Authors

Xiaodong Liu, Fan Li, Ye Xiang, Ruishan Liu, Chen-Xi Wang, Lihua Zhuo, Hongwei Li, Hongchao Yao, Jie Zhang, Xingxiong Zhou, Pexi Hu, Lv Yue, Jin-Ming Cao, Xu Feng, Yu-Hong Huang, Ming Jie, Qian Wang, Chang-Cong Gu, Fei Wang, Haibo Qu, Pei Wang, Gang Ning

Published in

NPJ precision oncology. Jul 14, 2026. Epub Jul 14, 2026.

Abstract

Our study developed a multiomics-driven transformer model that combines mammography, MRI, transcriptomic and proteomic data to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. A total of 2105 patients from 10 institutions were included for model training and validation. The model achieved an AUC of 0.939 in the training cohort (n = 658) and 0.830-0.867 across three independent validation cohorts (n = 282, 971 and 194, respectively), outperforming conventional ultrasound examination. Grad-CAM visualizations highlighted the tumor edges and surrounding tissue, consistent with clinical and pathological findings. In a cohort of 194 patients, multiomics analyses linked the model output to gene and protein signatures involved in immune modulation, cytoskeletal remodeling, and epithelial-to-mesenchymal transition. Critically, the major enriched pathways identified through model-stratified analysis were independently replicated in a parallel non-model-driven analysis using ALN status, demonstrating that these signatures reflect tumor biology. Network analysis revealed gene clusters related to DNA replication and immune pathways, providing biological insights into the model's decisions. These findings suggest that the stacking model holds promise as a noninvasive decision-support tool that may complement, rather than replace, current clinical staging practices. However, integration into clinical workflows requires prospective validation.

PMID:
42449125
Bibliographic data and abstract were imported from PubMed on 15 Jul 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.