Sparse probabilistic evaluation for treatment planning: a feasibility study in IMPT head and neck patients

Objective Probabilistic evaluation improves the trade-off between target coverage and OAR sparing in IMPT but remains computationally demanding. This study proposes sparse probabilistic evaluation (SPE), a computationally efficient approach integrated into a clinical TPS. Materials and methods Clinical plans of 20 IMPT HNC patients treated in 2024 were included. SPE used a predefined setup and range error grid with Monte-Carlo computed dose distributions. Two grid settings were evaluated: the maximum error Emax (3σ or 4σ), with σ=√ ( (σᵣandomerror) ²+ (σₛystematicerror) ²), and the number of setup error points nsetup (7, 33, 123). Accuracy and duration of SPE with each grid were evaluated in the calibration group (5 patients). 1000 treatments with normally distributed random (σ=1 mm) and systematic (σ=0. 92 mm) setup and range (σ=1. 5%) errors were simulated. The dose distribution of the nearest error point in the grid was assigned to each fraction. Probability distributions derived from SPE were compared with those from a reference based on 35. 000 Monte-Carlo calculations. Agreement was quantified using the mean percentile error (MPE), the mean absolute difference across percentiles 0. 01, 0. 02, …, 1. The found optimal grid (Emax = 3σ, nsetup = 33) was applied to the validation group (15 patients). Results The median MPE in the calibration group decreased significantly as the number of error points increased from 7 (tavg = 2 minutes) to 33 (tavg = 9 minutes), with no further improvement between 33 and 123 (tavg = 27 minutes) error points. Increasing Emax from 3σ to 4σ only improved accuracy for values above the 98th percentile. Applying SPE to the validation group resulted in median errors of 0. 02 Gy RBE (range: -0. 11 to 0. 07) for the 10th percentile of the D99. 8%, CTV distribution and 0. 0 Gy RBE (range: -0. 14 to 0. 23) for the 95th percentile of the D0. 03cc, SpinalCord Core distribution. Conclusion Sparse probabilistic evaluation achieves sufficient accuracy while requiring clinically acceptable computation times, paving the way for probabilistic evaluation in clinical practice. .