---
title: "多模态经验贝叶斯变分自编码器实现纵向肿瘤测量与脱落时间的联合建模"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.13984v1"
permalink: "https://feed.astralor.com/items/itm_f73496581e4feef655b4999a"
published: "2026-07-15T16:13:14.000Z"
score: 61
channel: "tech"
tags: ["EB-VAE","变分自编码器","时间-事件建模","癌症基因组学","经验贝叶斯","药理学"]
---

# 多模态经验贝叶斯变分自编码器实现纵向肿瘤测量与脱落时间的联合建模

## 速览

本研究提出了一种多模态经验贝叶斯变分自编码器（EB-VAE）框架，用于联合建模纵向肿瘤测量、脱落时间及基因协变量。该框架通过潜在个体效应正则化和协变量条件先验，将神经网络动力学与半机械化解码器结合，能够同时预测肿瘤生长轨迹和脱落风险。在黑色素瘤和乳腺癌实验中，该模型在个体水平预测和基因选择上表现优于传统方法，识别出BRAF、NRAS等生物学可信基因指标。

## 关键点

- 提出EB-VAE框架，联合纵向数据与时间-事件数据建模，支持信息性脱落。
- 半机械化解码器恢复治疗效应参数，与非线性混合效应模型一致。
- 基因条件先验提升个体预测性能，稳定性选择识别BRAF、NRAS等基因。
- 在黑色素瘤和乳腺癌数据集上验证模型有效性。

## 全文

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.

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