面向肿瘤学纵向患者表示与可扩展洞察生成的多模态基础模型
A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology
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中文总结 AI 辅助
本研究推出多模态基础模型oFM，基于167万肿瘤患者数据整合多模态信息，在预后基准及治疗队列中表现优于基线特征，还开发了机制发现框架以解释模型，助力肿瘤学临床与药物开发应用。
中文摘要 AI 辅助
精准肿瘤学需要整合多模态观测数据的患者状态纵向模型，以捕捉癌症随时间的演化与治疗过程。我们推出oFM，这一基于167万癌症患者的真实世界肿瘤队列开发的基础模型，整合了临床轨迹与DNA、RNA及H&E病理数据。患者级别的划分被用于训练、验证和测试，其中超过100万患者用于训练。oFM对每日临床和分子事件进行编码，并结合病理图像随时间整合，生成患者状态嵌入。我们将冻结的oFM嵌入与专家 curated 的临床和分子基线特征进行对比评估。在预后基准测试中，oFM提升了治疗反应、无进展生存期和总生存期的AUC，总生存期的AUC为0.774，而基线特征为0.563。在11个对比治疗队列中，oFM嵌入的汇总和尺度归一化治疗获益AUTOC是基线特征的三倍，且在11个队列中的9个队列中改善了获益排名，并在两个治疗组内提供了更强的预后区分度。我们还评估了一个机制发现框架，该框架通过基于证据的时间图将基于oFM嵌入构建的下游模型的预测结果与临床和生物学基础的机制关联，从而解释这些模型，支持在临床和药物开发应用中进行评估。
英文摘要
Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal observations. We introduce the oFM, a foundation model developed on a real-world oncology cohort of 1.67 million cancer patients that integrates clinical trajectories with DNA, RNA, and H&E pathology. Patient-level partitions were reserved for training, validation, and testing, with over one million patients used for training. The oFM encodes daily clinical and molecular episodes and, along with pathology images, integrates them over time to produce a patient state embedding. We evaluate frozen oFM embeddings against expert-curated clinical and molecular baseline features. In prognostic benchmarks, the oFM improved AUC for treatment response, progression-free survival, and overall survival (0.774 vs. 0.563 for overall survival). Across 11 comparative-treatment cohorts, the oFM embeddings achieved a three-fold higher pooled and scale-normalized treatment-benefit AUTOC than baseline features with improved benefit ranking in 9 of 11 cohorts, and provided stronger prognostic discrimination within both treatment arms. We also evaluated a mechanism discovery framework that interprets downstream models built on oFM embeddings by linking their predicted outcomes to clinically and biologically grounded mechanisms through an evidence-grounded temporal graph, enabling evaluation in clinical and drug-development applications.
发表机构
- Tempus AI, Inc.（Tempus AI公司）
机构由 AI 辅助整理，请以论文原文为准。
