Prediction of axillary lymph node metastasis using a transformer model and multi-omics validation in breast cancer.
Authors
Affiliations (17)
Affiliations (17)
- Department of Radiology, Panzhihua Central Hospital, Panzhihua, China.
- Department of Radiology, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
- Department of Radiology, Leshan Hospital, Chengdu University of Traditional Chinese Medicine, Leshan, China.
- Department of Breast Cancer, Yunnan Cancer Hospital, Kunming, Yunnan, China.
- Department of Hepatobiliary Oncology and Liver Transplantation, Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
- Department of Radiology, Nanchong Central Hospital/The Second Clinical Medical College of North Sichuan Medical College, Nanchong, China.
- Department of Interventional Medicine Center, The Second People's Hospital of YiBin, Yibin, China.
- Department of Breast Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Medical Imaging Center, Afliated Cancer Hospital of Xinjiang Medical University, Urumqi, China.
- Department of Radiology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaiyin, China.
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Haigang District, Qinhuangdao, China.
- Department of Radiology, Luzhou Hospital of Traditional Chinese Medicine, Luzhou, China. [email protected].
- Department of Radiology, West China Second Hospital, Sichuan University, Chengdu, China. [email protected].
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China. [email protected].
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China. [email protected].
- Department of Radiology, West China Second Hospital, Sichuan University, Chengdu, China. [email protected].
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China. [email protected].
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.