Abstract
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.
Abstract (translated)
新辅助化疗（NAC）是乳腺癌主要手术前的常见治疗选项。通过随访的动态对比增强磁共振成像（DCE-MRI）来监测对NAC的反应。准确预测NAC的反应有助于制定治疗计划。在这里，我们采用来自DCE-MRI的最大强度投影图像，从预处理图像生成术后图像（即，NAC后的3周或12周），利用新兴的扩散模型实现这一点。我们引入提示调优来考虑已知影响NAC反应的临床因素。我们的模型在图像质量指标上优于其他生成模型，并且根据病理完全缓解（pCR）产生的肿瘤大小变化的图像比其他模型更为准确。消融研究证实了我们方法的设计选择。本研究有可能帮助实现精准医疗。
URL
https://arxiv.org/abs/2509.24185
