用扩散模型生成肺癌治疗后影像，提升免疫治疗响应预测准确率
ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer
- 基于扩散模型合成治疗后CT，融合解剖结构先验增强真实性
- 引入多模态条件模块，使影像与临床数据协同生成，响应预测准确率提升21.24%
- 适合肿瘤影像与临床研究者，尤其关注免疫治疗预后的团队
非小细胞肺癌（NSCLC）的免疫治疗响应精准预测仍是未满足的临床需求。现有基于影像组学和深度学习的模型主要依赖治疗前影像预测分类结果，难以捕捉免疫治疗引发的复杂形态与纹理变化。本研究提出ImmunoDiff，一种面向解剖结构的扩散模型，可从基线影像生成治疗后CT，并融入临床约束。该框架整合肺段与血管结构等解剖先验以提升生成质量；引入新型cbi-Adapter模块，实现影像与临床数据嵌入的成对一致性融合；同时加入临床变量条件机制，利用人口统计、血液生物标志物及PD-L1表达优化生成过程。在自有NSCLC队列（接受免疫检查点抑制剂治疗）上的评估显示，响应预测的平衡准确率提升21.24%，生存预测的c-index提高0.03。代码即将开源。
原文摘要 · Abstract (English)
Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learning-based predictive models rely primarily on pre-treatment imaging to predict categorical response outcomes, limiting their ability to capture the complex morphological and textural transformations induced by immunotherapy. This study introduces ImmunoDiff, an anatomy-aware diffusion model designed to synthesize post-treatment CT scans from baseline imaging while incorporating clinically relevant constraints. The proposed framework integrates anatomical priors, specifically lobar and vascular structures, to enhance fidelity in CT synthesis. Additionally, we introduce a novel cbi-Adapter, a conditioning module that ensures pairwise-consistent multimodal integration of imaging and clinical data embeddings, to refine the generative process. Additionally, a clinical variable conditioning mechanism is introduced, leveraging demographic data, blood-based biomarkers, and PD-L1 expression to refine the generative process. Evaluations on an in-house NSCLC cohort treated with immune checkpoint inhibitors demonstrate a 21.24% improvement in balanced accuracy for response prediction and a 0.03 increase in c-index for survival prediction. Code will be released soon.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。