（示例：仅用于演示输出格式与长度校验，不代表真实数据/结论）

标题建议：
[TITLE]
推荐标题：跨尺度可解释特征驱动的肿瘤免疫治疗反应预测与机制溯源
Recommended Title: Cross-scale Interpretable Feature-driven Prediction and Mechanism Tracing of Tumor Immunotherapy Response
1) 多模态影像与循环标志物联合特征用于免疫治疗应答预测及机制解析 —— 理由：对象与判据清楚，强调可验证终点 / EN: Multimodal Imaging and Circulating Biomarker Combined Features for Immunotherapy Response Prediction and Mechanism Analysis
2) 面向免疫治疗分层的可解释表征模型构建与关键通路验证 —— 理由：突出方法创新与验证闭环 / EN: Construction of Interpretable Representation Models and Key Pathway Validation for Immunotherapy Stratification
3) 肿瘤微环境时空异质性表征及其与免疫治疗反应的因果链研究 —— 理由：机制向，贴合生医评审口味 / EN: Characterization of Tumor Microenvironment Spatiotemporal Heterogeneity and Its Causal Links to Immunotherapy Response
4) 跨中心多模态数据标准化与免疫治疗反应评估工具开发 —— 理由：强调可行性与工程交付 / EN: Cross-center Multimodal Data Standardization and Development of Immunotherapy Response Assessment Tools
5) 可解释联合特征揭示免疫抑制通路动态变化并指导分层干预 —— 理由：整合机制+应用，但仍保留”可验证”语义 / EN: Interpretable Combined Features Reveal Dynamic Immunosuppressive Pathway Changes to Guide Stratified Intervention
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中文摘要：
[ZH]
肿瘤免疫治疗显著改善部分患者预后，但对多数实体瘤仍存在应答率低与耐药等瓶颈。当前缺乏能够同时表征肿瘤微环境时空异质性与治疗反应的可解释指标体系，导致机制判断与个体化决策受限。我们前期在多中心队列中发现，基于多模态影像与循环分子标志物的联合特征可稳定区分应答/非应答人群，并提示关键免疫抑制通路的动态变化，据此提出“跨尺度可解释特征驱动的反应预测与机制溯源”假说。本项目拟：（1）构建跨中心的多模态数据规范与质控流程；（2）建立可解释表征模型，解析与免疫反应相关的关键特征；（3）在动物模型与前瞻性队列中验证关键通路与预测口径；（4）形成可复用的评估工具与干预靶点线索。预期阐明影响免疫治疗反应的关键机制并提供可落地的分层评估策略，为提升实体瘤治疗获益提供新思路。
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English Abstract:
[EN]
Immunotherapy has improved outcomes for some patients, yet most solid tumors still suffer from low response rates and drug resistance. A major limitation is the lack of interpretable metrics that jointly capture the spatiotemporal heterogeneity of the tumor microenvironment and treatment responses, which hampers mechanistic understanding and personalized decision-making. In our multicenter pilot cohorts, we found that combined features from multimodal imaging and circulating molecular biomarkers can robustly distinguish responders from non-responders and indicate dynamic changes in key immunosuppressive pathways; therefore, we hypothesize that cross-scale interpretable features can drive response prediction and mechanism tracing. This project will (1) build cross-center standards and quality control for multimodal data, (2) develop interpretable representation models to identify key features associated with immune responses, (3) validate critical pathways and prediction criteria in animal models and prospective cohorts, and (4) deliver reusable assessment tools and actionable clues for intervention targets. We expect to elucidate key mechanisms underlying immunotherapy responses and provide practical stratification strategies to improve clinical benefits for solid tumors.
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