Fast and precise dose estimation for very high energy electron radiotherapy with graph neural networks - Arsini, Lorenzo; Caccia, Barbara; Ciardiello, Andrea; De Gregorio, Angelica; Franciosini, Gaia; Giagu, Stefano; Guatelli, Susanna; Muscato, Annalisa; Nicolanti, Francesca; Paino, Jason; Schiavi, Angelo; Mancini-Terracciano, Carlo (Frontiers in Physics, 2024)
External beam radiotherapy (RT) is a one of the most used treatments against cancer, with photon-based RT and particle therapy (PT) being commonly employed modalities. Very highenergy electrons (VHEE) have emerged as promising candidates for novel treatments, particularly in exploiting the FLASH effect, offering potential advantages over traditional modalities. This paper introduces a Deep Learning (DL) model based on Graph Convolutional Networks to compute dose distributions of therapeutic VHEE beams in patient tissues. The model emulates Monte Carlo (MC) simulated dose within a cylindrical volume around the beam, enabling high spatial resolution dose calculation along the beamline while managing memory constraints. Trained on diverse beam orientations and energies, the model exhibits strong generalization to unseen configurations, achieving high accuracy metrics including a δ-index 3% passing rate of 99.8% and average relative error < 1% in integrated dose profiles compared to MC simulations. Notably, the model offers 3 to 6 orders of magnitude speedup over full MC simulations and fast MC codes, generating dose distributions in milliseconds on a single GPU. This speed could enable direct integration into treatment planning optimization algorithms, also leveraging the model's differentiability for exact gradient computation.