Simulating Post-Neoadjuvant Chemotherapy Breast Cancer MRI via Diffusion Model with Prompt Tuning
This work addresses the need for accurate treatment planning in breast cancer patients, though it is incremental as it applies an existing diffusion model with modifications to a specific medical imaging task.
The paper tackled the problem of predicting breast cancer response to neoadjuvant chemotherapy by generating post-treatment MRI images from pre-treatment ones using a diffusion model with prompt tuning, achieving better image quality and tumor size reflection compared to other generative models.
Neoadjuvant chemotherapy (NAC) is a common therapy option before the main surgery for breast cancer. Response to NAC is monitored using follow-up dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Accurate prediction of NAC response helps with treatment planning. Here, we adopt maximum intensity projection images from DCE-MRI to generate post-treatment images (i.e., 3 or 12 weeks after NAC) from pre-treatment images leveraging the emerging diffusion model. We introduce prompt tuning to account for the known clinical factors affecting response to NAC. Our model performed better than other generative models in image quality metrics. Our model was better at generating images that reflected changes in tumor size according to pCR compared to other models. Ablation study confirmed the design choices of our method. Our study has the potential to help with precision medicine.