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

J Appl Clin Med Phys. 2023 May;24(5):e13911. doi: 10.1002/acm2.13911. Epub 2023 Feb 7.


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 × $ \times \ $ 106 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.

Keywords: machine learning; proton therapy; robustness.

MeSH terms

  • Carcinoma, Non-Small-Cell Lung*
  • Humans
  • Lung Neoplasms*
  • Proton Therapy* / methods
  • Radiotherapy Dosage
  • Radiotherapy Planning, Computer-Assisted / methods
  • Radiotherapy, Intensity-Modulated* / methods