融合病理、基因、甲基化和文本描述，用链式推理提升癌症生存预测准确率
CoC: Chain-of-Cancer based on Cross-Modal Autoregressive Traction for Survival Prediction
- 构建链式癌变（CoC）框架，通过跨模态自回归牵引实现多模态协同学习
- 在五个公开癌症数据集上达到当前最优性能，显著优于仅使用临床或组学数据的方法
- 首次将医学文本描述引入生存预测，适合临床辅助决策与多模态模型研究者
生存预测旨在评估癌症患者的患病风险。现有方法主要依赖病理和基因组数据，单独或组合使用。从癌症发病机制看，表观遗传变化（如甲基化数据）也至关重要。此外，此前研究未利用文本描述辅助预测。为此，我们首次探索四种模态——三种临床模态与语言——用于生存预测。受思维链（CoT）启发，提出链式癌变（CoC）框架，聚焦内部学习与跨模态学习。将临床数据编码为原始特征，保留领域专有知识以支持内部学习；跨模态学习中，利用语言提示原始特征，并引入自回归互牵引模块以实现表示协同。该设计促进多模态联合学习。我们在五个公开癌症数据集上进行评估，大量实验验证了方法有效性与设计合理性，实现了当前最优结果。代码将公开。
原文摘要 · Abstract (English)
Survival prediction aims to evaluate the risk level of cancer patients. Existing methods primarily rely on pathology and genomics data, either individually or in combination. From the perspective of cancer pathogenesis, epigenetic changes, such as methylation data, could also be crucial for this task. Furthermore, no previous endeavors have utilized textual descriptions to guide the prediction. To this end, we are the first to explore the use of four modalities, including three clinical modalities and language, for conducting survival prediction. In detail, we are motivated by the Chain-of-Thought (CoT) to propose the Chain-of-Cancer (CoC) framework, focusing on intra-learning and inter-learning. We encode the clinical data as the raw features, which remain domain-specific knowledge for intra-learning. In terms of inter-learning, we use language to prompt the raw features and introduce an Autoregressive Mutual Traction module for synergistic representation. This tailored framework facilitates joint learning among multiple modalities. Our approach is evaluated across five public cancer datasets, and extensive experiments validate the effectiveness of our methods and proposed designs, leading to producing \sota results. Codes will be released.
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