Physics-Guided Radiotherapy Treatment Planning with Deep Learning

TL;DR

This paper introduces a physics-guided deep learning pipeline for radiotherapy planning that automates treatment plan generation, closely matches clinical standards, and reduces radiation exposure to organs at risk.

Contribution

It presents a novel two-stage deep learning approach incorporating physics-based guidance for more accurate and efficient radiotherapy treatment planning.

Findings

Achieves mean D95% difference of 0.42 Gy from clinical plans

Reduces radiation dose to organs at risk

Consistently matches clinical treatment plans using deep learning

Abstract

Radiotherapy (RT) is a critical cancer treatment, with volumetric modulated arc therapy (VMAT) being a commonly used technique that enhances dose conformity by dynamically adjusting multileaf collimator (MLC) positions and monitor units (MU) throughout gantry rotation. Adaptive radiotherapy requires frequent modifications to treatment plans to account for anatomical variations, necessitating time-efficient solutions. Deep learning offers a promising solution to automate this process. To this end, we propose a two-stage, physics-guided deep learning pipeline for radiotherapy planning. In the first stage, our network is trained with direct supervision on treatment plan parameters, consisting of MLC and MU values. In the second stage, we incorporate an additional supervision signal derived from the predicted 3D dose distribution, integrating physics-based guidance into the training process. We train and evaluate our approach on 133 prostate cancer patients treated with a uniform 2-arc VMAT protocol delivering a dose of 62 Gy to the planning target volume (PTV). Our results demonstrate that the proposed approach, implemented using both 3D U-Net and UNETR architectures, consistently produces treatment plans that closely match clinical ground truths. Our method achieves a mean difference of D95% = 0.42 +/- 1.83 Gy and V95% = -0.22 +/- 1.87% at the PTV while generating dose distributions that reduce radiation exposure to organs at risk. These findings highlight the potential of physics-guided deep learning in RT planning.