A machine learning approach to the accurate prediction of monitor units for a compact proton machine

Purpose Clinical treatment planning systems for proton therapy currently do not calculate monitor units ( MU s) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field‐specific output factors ( OF s) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning‐based approach was developed to predict output ( cG y/ MU ) and derive MU s, incorporating the dependencies on gantry angle and field size for a single‐room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient‐specific OF measurements. Method The OF s of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient‐specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGB oost and Cubist) included 1431 (~81%) OF s. Ten‐fold cross‐validation was used to prevent “overfitting” and to validate each model. The remaining 323 (~19%) OF s were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi‐empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OF s was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OF s was investigated for the measurements with and without patient‐specific apertures. Results All three machine learning methods showed higher accuracy than the semi‐empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist‐based solution outperformed all other models ( P  < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OF s. The OF s showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%–4% as the field radius was reduced to 2.5 cm. Conclusion Machine learning methods can be used to predict OF for double‐scatter proton machines with greater prediction accuracy than the most popular semi‐empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning‐based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time‐consuming patient‐specific OF measurements.