Radiomics-Guided Multi-Modal Learning for Pathological Complete Response Prediction from Breast MRI Under Incomplete Modality Settings

Pathological complete response (pCR) after neoadjuvant therapy is an important indicator of treatment response and prognosis in breast cancer. Multi-modal breast MRI provides complementary information for pCR prediction, but existing methods often assume complete modality availability and do not fully exploit the complementary value of radiomics and deep features. To address these limitations, we propose a radiomics-guided multi-modal learning framework for pCR prediction from breast MRI under incomplete modality settings. The model employs a multi-branch 2.5D encoder to extract modality-specific features, a radiomics-guided gating module to enhance deep representations with handcrafted priors, and a masked fusion strategy to adaptively integrate available modalities while excluding missing ones. Experiments on the I-SPY1 Trial dataset show that the proposed method achieves promising performance and maintains robustness under incomplete modality settings. These results suggest that the proposed framework can effectively integrate multi-modal MRI and radiomics information for pCR prediction and shows potential under incomplete modality settings.