Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated study

TL;DR

This study develops a hierarchical Bayesian neural network model to predict cancer dynamics under treatment, effectively capturing nonlinear covariate effects and interactions, demonstrated through simulated data in multiple myeloma.

Contribution

The paper introduces a novel hierarchical Bayesian neural network approach for modeling cancer dynamics, improving prediction accuracy over linear models by capturing complex covariate interactions.

Findings

Bayesian neural network model outperforms linear models in predicting cancer trajectories.

The approach effectively captures nonlinear covariate effects and interactions.

Framework applicable to various cancer types and time series prediction problems.

Abstract

Predicting cancer dynamics under treatment is challenging due to high inter-patient heterogeneity, lack of predictive biomarkers, and sparse and noisy longitudinal data. Mathematical models can summarize cancer dynamics by a few interpretable parameters per patient. Machine learning methods can then be trained to predict the model parameters from baseline covariates, but do not account for uncertainty in the parameter estimates. Instead, hierarchical Bayesian modeling can model the relationship between baseline covariates to longitudinal measurements via mechanistic parameters while accounting for uncertainty in every part of the model. The mapping from baseline covariates to model parameters can be modeled in several ways. A linear mapping simplifies inference but fails to capture nonlinear covariate effects and scale poorly for interaction modeling when the number of covariates is large. In contrast, Bayesian neural networks can potentially discover interactions between covariates automatically, but at a substantial cost in computational complexity. In this work, we develop a hierarchical Bayesian model of subpopulation dynamics that uses baseline covariate information to predict cancer dynamics under treatment, inspired by cancer dynamics in multiple myeloma (MM), where serum M protein is a well-known proxy of tumor burden. As a working example, we apply the model to a simulated dataset and compare its ability to predict M protein trajectories to a model with linear covariate effects. Our results show that the Bayesian neural network covariate effect model predicts cancer dynamics more accurately than a linear covariate effect model when covariate interactions are present. The framework can also be applied to other types of cancer or other time series prediction problems that can be described with a parametric model.