面向鲁棒癌症风险估计的因子化上下文聚合：软重排序检索与分层锚框
Factorized Context Aggregation for Robust Cancer Risk Estimation via Soft Re-Ranked Retrieval and Hierarchical Anchors
提出以组织病理学为锚、其他模态仅用于训练的解耦框架，结合软重排序检索与分层锚框，在模态缺失下保持鲁棒性；其检索与分层锚框机制可迁移至其他多模态医学影像任务。
原文依据 leverages histopathology as a basis for outcome prediction, while using other data modalities when training the models
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论准确的癌症风险评估对于个性化治疗规划至关重要。尽管整合组织病理学与互补数据模态（如基因组学或临床报告）的多模态模型展现出更优的预后能力，但它们通常假设数据完全可用，这在真实临床环境中是不切实际的期望。相比之下，组织病理学切片被常规采集、普遍可获取且信息丰富，使其成为鲁棒生存预测的实用锚点。在本研究中，我们提出了一种新颖的框架，利用组织病理学作为结果预测的基础，同时在训练模型时使用其他数据模态。在八种癌症类型和场景（包括各种数据模态）上的大量实验表明，我们的模型优于所有基线，在训练时仅使用组织病理学的方法上提升高达8%，与使用所有数据模态的模型相比差距仅为1.4%。我们的模型还在67%的风险分层场景中将患者分为有意义的风险组（最佳SOTA为50%），在不同模态缺失情况下泛化良好，即使在训练期间缺失数据率高出40%时也能匹配最佳SOTA。它还在零样本设置中保持了语义对齐。这些结果凸显了我们的方法在资源有限或模态不完整的真实世界癌症风险预测中的实用性和鲁棒性。代码见：https://github.com/pazadimo/fca-robust-risk-estimation
英文原文摘要 1564 字符
Accurate cancer risk assessment is critical for personalized treatment planning. While multimodal models that integrate histopathology with complementary data modalities (e.g., genomics, or clinical reports) exhibit superior prognostic capability, they typically assume full data availability, an unrealistic expectation in real-world clinical settings. In contrast, histopathology slides are routinely collected, universally accessible, and information-rich, making them a practical anchor for robust survival prediction. In this study, we propose a novel framework that leverages histopathology as a basis for outcome prediction, while using other data modalities when training the models. Extensive experiments across eight cancer types and scenarios, including various data modalities, demonstrate that our model outperforms all baselines, with up to 8% gains over methods that solely use histopathology at training time, and a 1.4% gap compared to models that utilize all data modalities. Our model also stratifies patients into meaningful risk groups in 67% of risk stratification scenarios (vs. 50% for best SOTA), generalizes well under varying modality missingness, and matches the best SOTA even with 40% higher rate of missing data during training. It also preserves semantic alignment in zero-shot settings. These results highlight the practical utility and robustness of our approach for real-world cancer risk prediction in resource-limited or modality-incomplete settings. Code is available at: https://github.com/pazadimo/fca-robust-risk-estimation