Attention-based multimodal fusion transformer for predicting the efficacy of neoadjuvant therapy in breast cancer: a cross-institutional retrospective study.

ABSTRACT:

Background

Neoadjuvant therapy (NAC) is a standard treatment for breast cancer, yet only some patients gain significant benefit. Identifying those most likely to benefit from NAC is crucial. Single-modality data often overlook patient heterogeneity, so we developed an interpretable, attention-based multimodal full information feature fusion transformer, MuFi, to predict NAC responses by integrating whole slide images (WSI) and magnetic resonance imaging (MRI).

Methods

Data from 567 biopsy-confirmed breast cancer patients from two institutions were retrospectively analyzed, with a training cohort (n = 290), validation cohort (n = 73), and external test cohort (n = 204). Multimodal data included pre-treatment pathology slides, MRI scans, and clinical information. A memory-effi

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12797410 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature