Related Experiment Video
Updated: Aug 30, 2025
Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy
Published on: May 8, 2018
Machine learning-based treatment couch parameter prediction in support of surface guided radiation therapy
Geert De Kerf1, Michaël Claessens1,2, Isabelle Mollaert1
1Iridium Netwerk, Antwerp, Belgium.
Purpose:
A fully independent, machine learning-based automatic treatment couch parameters prediction was developed to support surface guided radiation therapy (SGRT)-based patient positioning protocols. Additionally, this approach also acts as a quality assurance tool for patient positioning.
Materials/Methods:
Setup data of 183 patients, divided into four different groups based on used setup devices, was used to calculate the difference between the predicted and the acquired treatment couch value.
Results:
Couch parameters can be predicted with high precision . A significant difference (p < 0.01) between the variances of Lung and Brain patients was found. Outliers were not related to the prediction accuracy, but are due to inconsistencies during initial patient setup.
Conclusion:
Couch parameters can be predicted with high accuracy and can be used as starting point for SGRT-based patient positioning. In case of large deviations (>1.5 cm), patient setup has to be verified to optimally use the surface scanning system.
