用扩散模型生成化疗后乳腺MRI，提升治疗反应预测精度。
Simulating Post-Neoadjuvant Chemotherapy Breast Cancer MRI via Diffusion Model with Prompt Tuning
- 结合提示调优，利用化疗前MRI生成化疗后图像。
- 生成图像更准确反映肿瘤缩小程度，尤其在病理完全缓解者中。
- 适合临床研究与精准医疗领域，辅助治疗方案制定。
新辅助化疗（NAC）是乳腺癌术前常见疗法，其疗效通过随访动态对比增强磁共振成像（DCE-MRI）监测。准确预测NAC反应有助于优化治疗方案。本文采用DCE-MRI的最大强度投影图像，基于新兴的扩散模型，从化疗前图像生成化疗后图像（即治疗3周或12周后）。引入提示调优（prompt tuning）以建模影响NAC反应的已知临床因素。实验表明，本模型在图像质量指标上优于其他生成模型，且生成图像更真实反映肿瘤大小变化，尤其在病理完全缓解（pCR）患者中表现更佳。消融实验证实了方法设计的有效性。研究为精准医疗提供了潜在支持。
原文摘要 · Abstract (English)
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.
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