A feasibility study on a machine-learning-based quality assurance tool for spot-scanning proton therapy using delivery log files and treatment plans

Background and purpose: Although pencil beam scanning provides superior dose conformity, delivery uncertainties remain in beam monitoring and steering systems. This study evaluates a machine learning model that predicts delivered spot positions from plan parameters as a complementary quality assurance support tool. Materials and methods: score, and Euclidean distance. Results: Gy, and a maximum absolute voxel-wise dose difference of 1.796 Gy, defined as the largest absolute difference between predicted and planned dose values at corresponding voxels. Conclusions: This feasibility study demonstrates that machine-learning-based prediction of delivered spot positions can achieve sub-millimeter accuracy, potentially enhancing the precision and reliability of quality assurance processes in proton therapy.