Patient‐specific quality assurance prediction models based on machine learning for novel dual‐layered MLC linac

摘要

Abstract Background Patient‐specific quality assurance (PSQA) is an indispensable and essential procedure in radiotherapy workflow, and several studies have been done to develop prediction models based on the conventional C‐arm linac of single‐layered multileaf collimator (MLC) with machine learning (ML) and deep learning techniques to predict PSQA results and improve efficiency. Recently, a newly designed O‐ring gantry linac “Halcyon” equipped with unique jawless stacked‐and‐staggered dual‐layered MLC was released. However, few studies have focused on developing PSQA prediction models for this novel dual‐layered MLC system. Purpose To evaluate the performance of ML to predict PSQA results of fixed field intensity‐modulated radiation therapy (FF‐IMRT) plans for linac equipped with dual‐layered MLC. Methods and materials A total of 213 FF‐IMRT treatment plans, including 1383 beams from various treatment sites, were selected and delivered with portal dosimetry verification on Halcyon linac. Gamma analysis was performed using 1/1, 2/2, and 3/2?mm criteria with a 10 threshold. The training set (TS) of ML models consisted of 1106 beams, and an independent evaluation set (ES) consisted of 277 beams. For each beam, 33 complexity metrics were extracted as input data for training models. Three ML algorithms (gradient boosting decision tree/GBDT, random forest/RF, and Poisson Lasso/PL) were utilized to build the models and predict gamma passing rates (GPRs). To improve the prediction accuracy in the rare region, a method of reweighting for TS has been performed and compared to the unweighted results. The importance of complexity metrics was studied by permuted interesting features. Results The GBDT model had the best performance in this study. In ES, the minimal mean prediction error for unweighted results was 1.93, 1.16, 0.78 under 1/1, 2/2, and 3/2?mm criteria, respectively, from GBDT model. Comparing to the unweighted results, the models after reweighting gained up to 30 improvement in the rare region, whereas the overall prediction error was slightly worse depending on the kind of models. For feature importance, 2 tree‐based models (GBDT and RF) had in common the top 10 most important metrics as well as the same metric with the largest impact. Conclusion For linac equipped with novel dual‐layered MLC, the ML model based on GBDT algorithm shows a certain degree of accuracy for GPRs prediction. The specific ML model for dual‐layered MLC configuration could be a useful tool for physicists detecting PSQA measurement failures.