Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.

Accurate prediction of disease-free survival (DFS) is essential for tailoring adjuvant regimens and improving clinical outcomes in early-stage breast cancer (EBC). A multimodal deep learning model (Mu-model) based on deep canonical correlation analysis (DCCA) integrating multiparametric magnetic resonance imaging (MRI)-including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI)-with clinical text data is developed to predict DFS and identify patients likely to benefit from adjuvant therapy. This multicenter retrospective study includes 1,120 patients with EBC (training cohort, n = 459; external validation cohort, n = 661). The Mu-model achieves concordance index (C-index) values of 0.742 (95% confidence interval [CI]: 0.662-0.821) in the training cohort (TC) and 0.735 (95% CI: 0.667-0.803) in the external validation cohort (EVC) for DFS. The Mu-model score (MuS) remains an independent prognostic factor after adjustment for clinicopathologic variables (all P < 0.05). In human epidermal growth factor receptor 2 (HER2)-positive, hormone receptor (HR)-positive, and T2-stage subgroups, a significant survival benefit associated with adjuvant therapy was observed in patients with low MuS, whereas no statistically significant association was detected in patients with high MuS. Transcriptomic analysis in 19 patients indicates that high MuS is associated with immune activation and enrichment of cell-cycle and purine-metabolism pathways. The Mu-model provides non-invasive DFS prediction and recurrence risk stratification, while preliminarily exploring its potential to identify patients who may derive differential benefits from adjuvant therapy.