  Breast cancer is the most prevalent cancer among women and predicting
pathologic complete response (pCR) after anti-cancer treatment is crucial for
patient prognosis and treatment customization. Deep learning has shown promise
in medical imaging diagnosis, particularly when utilizing multiple imaging
modalities to enhance accuracy. This study presents a model that predicts pCR
in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic
resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps.
Radiomics features are established hand-crafted features of the tumor region
and thus could be useful in medical image analysis. Our approach extracts
features from both DCE MRI and ADC using an encoder with a self-attention
mechanism, leveraging radiomics to guide feature extraction from tumor-related
regions. Our experimental results demonstrate the superior performance of our
model in predicting pCR compared to other baseline methods.
