Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning

Abstract

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1 × 10 6 delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.

An artificial neural network based approach for predicting the proton beam spot dosimetric characteristics of a pencil beam scanning technique

“…Newpower et al [10] developed a neural network model for predicting the spot position using measured and log file recorded spot position data. The MSE of the prediction model was 0.300 mm.…”

“…Another study by Li et al [9] used a feed-forward neural network model and recurrent neural network model to predict the proton therapy range and dose by the information of proton-induced positron emitters. An irradiation log file is a potential tool for ML techniques to predict the PBS spot position and MU per spot [10]. Another study by Dominic Maes et al [11] demonstrated ML techniques to accurately predict delivered PBS spot positions and MU using information from irradiation log files as part of the training data set.…”