From plan to delivery: Machine learning based positional accuracy prediction of multi‐leaf collimator and estimation of delivery effect in volumetric modulated arc therapy

Abstract

AbstractPurposeThe positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery.MethodsA total of 744 “dynalog” files for 23 VMAT plans were extracted randomly from treatment database. Multi‐correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates.ResultsThe MLC positional deviation and effective impact factors show a strong multi‐correlation (R = 0.701, p‐value < 0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5‐fold cross‐validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235 cGy, while the relative volume deviation remained small at 0.470%–5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of −0.506 to −0.720 and p‐value < 0.05) but marginally with dosage deviations (with correlation coefficient < 0.498 and p‐value > 0.05).ConclusionsThe RF predictive model provides a prior tool for VMAT quality assurance.