Evaluation of dose prediction error and optimization convergence error in four‐dimensional inverse planning of robotic stereotactic lung radiotherapy

Inverse optimization of robotic stereotactic lung radiotherapy is typically performed using relatively simple dose calculation algorithm on a single instance of breathing geometry. Variations of patient geometry and tissue density during respiration could reduce the dose accuracy of these 3D optimized plans. To quantify the potential benefits of direct four‐dimensional (4D) optimization in robotic lung radiosurgery, 4D optimizations using 1) ray‐tracing algorithm with equivalent path‐length heterogeneity correction ([Formula: see text]), and 2) Monte Carlo (MC) algorithm ([Formula: see text]), were performed in 25 patients. The [Formula: see text] plans were recalculated using MC algorithm ([Formula: see text]) to quantify the dose prediction errors (DPEs). Optimization convergence errors (OCEs) were evaluated by comparing the [Formula: see text] and [Formula: see text] dose results. The results were analyzed by dose‐volume histogram indices for selected organs. Statistical equivalence tests were performed to determine the clinical significance of the DPEs and OCEs, compared with a 3% tolerance. Statistical equivalence tests indicated that the DPE and the OCE are significant predominately in [Formula: see text]. The DPEs in [Formula: see text] of lung, and [Formula: see text] of cord, trachea, and esophagus are within 1.2%, while the OCEs are within 10.4% in lung [Formula: see text] and within 3.5% in trachea [Formula: see text]. The marked DPE and OCE suggest that 4D MC optimization is important to improve the dosimetric accuracy in robotic‐based stereotactic body radiotherapy, despite the longer computation time. PACS numbers: 87.53.Ly, 87.55.km