A machine learning-based framework for delivery error prediction in proton pencil beam scanning using irradiation log-files. (October 2020)

• Record Type: Journal Article
• Authors: Maes, Dominic; Bowen, Stephen R.; Regmi, Rajesh; Bloch, Charles; Wong, Tony; Rosenfeld, Anatoly; Saini, Jatinder
• Is Part Of: Physica medica. Volume 78(2020)
• Page Start: 179
• Page End: 186
• Publication Date: 2020-10
• Subjects: Proton therapy -- Pencil beam scanning -- Machine learning -- Neural networks -- Log-files; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2020.09.008
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Delivery errors in proton pencil beam scanning (PBS) can introduce error in dose distributions for patient treatments. PBS delivery error can be assessed by analyzing data from irradiation log-files. Machine learning models can be trained to predict PBS delivery error using log-file data a training dataset. Abstract: Purpose: This study aims to investigate the use of machine learning models for delivery error prediction in proton pencil beam scanning (PBS) delivery. Methods: A dataset of planned and delivered PBS spot parameters was generated from a set of 20 prostate patient treatments. Planned spot parameters (spot position, MU and energy) were extracted from the treatment planning system (TPS) for each beam. Delivered spot parameters were extracted from irradiation log-files for each beam delivery following treatment. The dataset was used as a training dataset for three machine learning models which were trained to predict delivered spot parameters based on planned parameters. K-fold cross validation was employed for hyper-parameter tuning and model selection where the mean absolute error (MAE) was used as the model evaluation metric. The model with lowest MAE was then selected to generate a predicted dose distribution for a test prostate patient within a commercial TPS. Results: Analysis of the spot position delivery error between planned and delivered values resulted in standard deviations of 0.39 mm and 0.44 mm for x and y spot positions respectively. Prediction error standard deviation values of spot positions using the selected model were 0.22 mm and 0.11 mm for x and y spot positions respectively. Finally, a three-way comparison of dose distributions and DVH values for select OARs indicates that the random-forest-predicted dose distribution within the test prostate patient was in closer agreement to the delivered dose distribution than the planned distribution. Conclusions: PBS delivery error can be accurately predicted using machine learning techniques.