发表机构
Fraunhofer-Chalmers Centre; Department of Electrical Engineering; Chalmers University of Technology（弗劳恩霍夫-查尔默斯中心; 电气工程系; 查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对纵向肿瘤测量等多源数据整合难的问题，扩展EB-VAE框架用于联合纵向和事件发生时间建模，比较不同解码器公式，纳入基因组协变量，实现肿瘤生长等联合预测，确定相关基因指标，提供了灵活概率框架。
AI 中文摘要
纵向肿瘤测量、缺失信息和基因协变量为治疗反应提供了补充信息，但在单一总体建模框架中整合这些数据源仍具有挑战性。我们将经验贝叶斯变分自编码器（EB-VAE）框架扩展到联合纵向和事件发生时间建模，并在肿瘤生长数据上进行评估。该框架使用由协变量条件经验贝叶斯先验正则化的潜在个体效应来表示个体间变异性，同时解码器将这些潜在效应映射到肿瘤体积轨迹。为了考虑信息缺失，解码器增加了一个风险模型，从而产生肿瘤生长和缺失时间的联合预测。我们进一步比较了全神经和解码器公式，并通过基因条件先验适应纳入基因组协变量。混合解码器恢复的治疗效果参数与先前报道的非线性混合效应估计大致一致，同时实现了与神经解码器相当先验预测性能。联合模型再现了保留个体中的肿瘤体积分布和缺失模式，基因条件在皮肤黑色素瘤和乳腺癌实验中改善了个体水平的先验预测。稳定性选择确定了几个生物学上合理的基因指标，包括BRAF、NRAS、NF1和MDM2的改变。这些结果表明，EB-VAE为在药代动力学应用中结合神经动力学、机械结构、事件发生时间建模和高维协变量提供了一个灵活的概率框架。
英文摘要
Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrating these data sources within a single population modeling framework remains challenging. We extend the empirical Bayes variational autoencoder (EB-VAE) framework to joint longitudinal and time-to-event modeling and evaluate it on tumor growth data. The framework represents inter-individual variability using latent individual effects regularized by a covariate-conditioned empirical Bayes prior, while a decoder maps these latent effects to tumor-volume trajectories. To account for informative dropout, the decoder was augmented with a hazard model, yielding joint predictions of tumor growth and time to dropout. We further compared fully neural and hybrid semi-mechanistic decoder formulations and incorporated genomic covariates through a genetics-conditioned prior adaptation. The hybrid decoder recovered treatment-effect parameters broadly consistent with previously reported nonlinear mixed-effects estimates, while achieving prior predictive performance comparable to the neural decoder. The joint model reproduced both tumor-volume distributions and dropout patterns in held-out individuals, and genetic conditioning improved individual-level prior predictions in both cutaneous melanoma and breast cancer experiments. Stability selection identified several biologically plausible genetic indicators, including alterations in BRAF, NRAS, NF1, and MDM2. These results demonstrate that EB-VAE provides a flexible probabilistic framework for combining neural dynamics, mechanistic structure, time-to-event modeling, and high-dimensional covariates in pharmacometric applications.
