Deep learning-based prediction of axillary pathological complete response in patients with breast cancer using
Yu Liu1, Ying Wang2, Jiaxin Huang3
1Department of Ultrasound, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
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 derived from the primary tumour, longitudinal primary tumour, or ALN (P < 0.05, as per the DeLong test). The performance of iShape remained satisfactory in subgroup analyses stratified by age, menstrual status, T stage, molecular subtype, treatment regimens, and machine type (AUCs of 0.812-1.000). More importantly, the FNR of iShape was 7.7%-8.1% in the EVSs, and the FNR of SLNB decreased from 13.4% to 3.6% with the aid of iShape in patients receiving SLNB and ALN dissection. The decision-making process of iShape was explained by feature visualisation. Additionally, RNA sequencing analysis revealed that a lower deep learning score was associated with immune infiltration and tumour proliferation pathways.
Interpretation:
The iShape model demonstrated good performance for the precise quantification of ALN status in patients with ALN-positive breast cancer receiving NAT, potentially benefiting individualised decision-making, and avoiding unnecessary axillary lymph node dissection.
Funding:
This study was supported by (1) Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2024ZD0531100); (2) Key-Area Research and Development Program of Guangdong Province (No. 2021B0101420006); (3) National Natural Science Foundation of China (No. 82472051, 82471947, 82271941, 82272088); (4) National Science Foundation for Young Scientists of China (No. 82402270, 82202095, 82302190); (5) Guangzhou Municipal Science and Technology Planning Project (No. 2025A04J4773, 2025A04J4774); (6) the Natural Science Foundation of Guangdong Province of China (No. 2025A1515011607); (7) Medical Scientific Research Foundation of Guangdong Province of China (No. A2024403); (8) Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); (9) Outstanding Youth Science Foundation of Yunnan Basic Research Project (No. 202401AY070001-316); (10) Innovative Research Team of Yunnan Province (No. 202505AS350013).
