BayesDose: Comprehensive proton dose prediction with model uncertainty using Bayesian LSTMs

TL;DR

BayesDose introduces a Bayesian LSTM framework for proton dose prediction that quantifies uncertainty, matching deterministic accuracy and supporting clinical decision-making.

Contribution

It presents a novel Bayesian approach using Gaussian mixture models for dose prediction with uncertainty quantification in proton therapy.

Findings

BayesDose performs comparably to deterministic models in accuracy.

Uncertainty predictions are conservative and spatially correlated with dose differences.

Model re-training improves performance on unseen patient data.

Abstract

We propose the BayesDose-Framework, a Bayesian approach for fast and accurate dose prediction in proton therapy. Our framework is based on a previously published deterministic LSTM model and is trained and evaluated on simulated beamlet doses from water phantoms and patient geometries. We parameterize the network's weights using 2D Gaussian mixture models and use ensemble predictions to quantify mean dose predictions and their standard deviation. The BayesDose model performs similarly to the deterministic variant. The uncertainty predictions are conservative but correlate well spatially and in magnitude with dose differences. This correlation is reduced when applied to patient data with unseen relative stopping power value ranges, which could be successfully addressed by re-training. We parallelize predictions and presample network weights to reduce runtime overhead. Bayesian models like BayesDose can provide fast predictions with quality equal to deterministic models and may support decision making and quality assurance in clinical settings in the future.