生成肿瘤治疗效果的高保真合成数据，提升精准医疗研究可靠性。
OncoSynth: Synthetic data generation for treatment effect estimation in oncology
- 基于扩散模型构建因果感知的合成数据生成框架
- 在肺癌和乳腺癌数据集上降低66%群体与58%个体治疗效应误差
- 适合数据受限场景下精准肿瘤学疗效评估
在肿瘤学领域，患者层面数据常受限制。合成数据可作为分析治疗效果的替代方案，但现有方法难以保持协变量、治疗与结果之间的因果关系，导致治疗效应估计偏差。本文提出OncoSynth，一种基于扩散的序列生成框架，用于建模协变量如何影响治疗分配及治疗对生存的影响，从而生成能准确估计群体与个体治疗效应的合成队列。我们在大规模肺癌（N=37,128）和乳腺癌（N=17,046）队列上评估该方法。结果表明，OncoSynth生成的合成患者队列能有效保留真实世界中的患者、治疗和结局分布。显著地，其相比现有方法将群体水平治疗效应误差降低最高达66%，个体水平误差降低最高达58%。因此，OncoSynth可在数据共享受限环境中支持可靠的精准肿瘤学证据生成。
原文摘要 · Abstract (English)
In oncology, access to patient-level data is often restricted. Synthetic data provides an alternative for analyzing treatment effectiveness, but existing methods for synthetic data generation fail to preserve the causal relationships between covariates, treatments, and outcomes, thereby leading to biased estimates of treatment effects. Here, we introduce OncoSynth, a generative, causally-aware machine learning framework designed to produce synthetic cohorts that enable accurate estimation of population- and patient-level treatment effects. OncoSynth uses a diffusion-based sequential approach to model how covariates influence treatment assignment and how treatment affects survival. We evaluate OncoSynth using large lung (N = 37,128) and breast cancer (N = 17,046) cohorts. Our results show that OncoSynth generates high-fidelity synthetic patient cohorts that preserve real-world patient, treatment, and outcome distributions. Notably, OncoSynth improves treatment effect estimation over existing approaches, by reducing population-level treatment effect error by up to 66%, and patient-level treatment effect error by up to 58%. Thereby, OncoSynth supports reliable evidence generation for precision oncology in settings where data sharing is restricted.
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。