Abstract
Background
Pathological complete response (pCR) is a key endpoint following neoadjuvant chemotherapy (NAC) in breast cancer, with potential to guide treatment adaptation and surgical de-escalation. However, limited generalizability across institutions remains a major challenge due to heterogeneous imaging protocols (scanner vendors, field strengths, and acquisition parameters) and diverse patient populations, which can cause significant domain shifts in model performance. This study develops and externally validates a multimodal framework for post-neoadjuvant, preoperative prediction of pCR using multiparametric MRI (mpMRI) and routinely available clinical data.Methods
In a multicenter retrospective setting, 1291 patients from five institutions were enrolled and institutionally divided into a development cohort pooled from two centers (n=683) and three independent, single-center external validation cohorts (n=608). Model inputs comprised post-treatment mpMRI (dynamic contrast-enhanced and diffusion-weighted imaging) for deep feature extraction, clinicopathological variables, radiomics features from both pre- and post-treatment scans, and delta-radiomics features capturing longitudinal within-sequence changes (Δ = post - pre). A dual-tower attention network incorporating a Feature Quality Enhancer (FQE) module-which regularizes the latent space by promoting intra-class compactness, inter-class separability, and feature consistency-was designed to learn discriminative imaging representations. Deep, radiomic, and clinical features were then integrated through a hierarchical feature selection strategy, with robustness assessed via repeated experiments.. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) in both development and external validation cohorts.Results
The proposed AIFPS achieved an AUC of 0.882 (95% CI: 0.855-0.905) in the development cohort. In external validation, AIFPS achieved the highest numerical AUCs (0.853, 0.888, and 0.895) and demonstrated statistically significant improvements over all single-modality models in all cohorts. While its superiority over certain dual-modality models did not reach statistical significance after correction for multiple comparisons, the full multimodal approach consistently yielded the numerically highest discrimination.Conclusion
AIFPS integrates quality-enhanced deep imaging features with radiomics, including delta-radiomics, and clinicopathological variables to enable accurate and generalizable preoperative prediction of pathological complete response, particularly in HER2-positive disease, while further validation is required for triple-negative and luminal subtypes. Its application in luminal breast cancer requires further dedicated subtype-specific modeling. With prospective validation, this approach may support individualized treatment stratification following neoadjuvant chemotherapy.