Deep learning-based prediction of axillary pathological complete response in patients with breast cancer using longitudinal multiregional ultrasound

作者：Yu Liu, Ying Wang, Jiaxin Huang, Shufang Pei, Yuxiang Wang, Yanfen Cui, Lifen Yan, Mengxia Yao, Yumeng Wang, Zejun Zhu, Chunwang Huang, Zaiyi Liu, Changhong Liang, Jiayao Shi, Zhenhui Li, Xiao‐Qing Pei, Lei Wu · 发表于：EBioMedicine · 年份：2025 · DOI：10.1016/j.ebiom.2025.105896 · 被引用次数：11 · 研究领域：Breast Cancer Treatment Studies、AI in cancer detection、Breast Lesions and Carcinomas

BACKGROUND: Noninvasive biomarkers that capture the longitudinal multiregional tumour burden in patients with breast cancer may improve the assessment of residual nodal disease and guide axillary surgery. Additionally, a significant barrier to the clinical translation of the current data-driven deep learning model is the lack of interpretability. This study aims to develop and validate an information shared-private (iShape) model to predict axillary pathological complete response in patients with axillary lymph node (ALN)-positive breast cancer receiving neoadjuvant therapy (NAT) by learning common and specific image representations from longitudinal primary tumour and ALN ultrasound images. METHODS: A total of 1135 patients with biopsy-proven ALN-positive breast cancer who received NAT were included in this multicentre, retrospective study. The iShape was trained on a dataset of 371 patients and validated on three external validation sets (EVS1-3), with 295, 244, and 225 patients, respectively. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The false-negative rates (FNRs) of iShape alone and in combination with sentinel lymph node biopsy (SLNB) were also evaluated. Imaging feature visualisation and RNA sequencing analysis were performed to explore the underlying basis of iShape. FINDINGS: The iShape achieved AUCs of 0.950-0.971 for EVS 1-3, which were better than those of the clinical model and the image signatures der...