ESTRO 2022 - Abstract Book

S705

Abstract book

ESTRO 2022

Conclusion The highest priority current training need was radiotherapy planning; this was also the highest current overall training need. The highest priority future training need was MRI acquisition; this was also the highest overall future training need. There was an increase from current to future training needs in all skills which indicates that the skills would be beneficial to all TRs and not just those undertaking MRIgRT when considering the future of the TR profession.

OC-0786 Surface imaging to track inter-fractional anatomical variation in paediatric abdominal radiotherapy.

S. Taylor 1 , P. Lim 2 , J. Cantwell 2 , D. D’Souza 2 , S. Moinuddin 2 , Y. Ching-Chang 2 , M. Gaze 2 , J. Gains 2 , C. Veiga 1

1 University College London, Centre for Medical Image Computing, London, United Kingdom; 2 University College London Hospitals NHS Foundation Trust, Radiotherapy, London, United Kingdom Purpose or Objective Proton therapy is promising in the treatment of abdominal paediatric malignancies due to its potential in reducing treatment side effects. However, inter-fractional variation in gastrointestinal (GI) gas volume and position may lead to under/over-shooting, compromising target coverage. We investigated the use of surface imaging in paediatric settings to monitor interfractional anatomical variation, by correlating internal variation in GI gas volume with simulated external metrics of surface change. Materials and Methods Data from 21 patients treated with IMAT for abdominal high-risk neuroblastoma (median age 4, range 2-19 y) were used in this study. The body and GI gas volumes were semi-automatically delineated on 21 CT and 77 weekly CBCT scans using ITK- Snap. The CBCTs were rigidly co-registered to the CT using NiftyReg, and both scans translated to treatment isocenter. CBCTs were considered as treatment position, while the CT was the reference position. Quantitative metrics of internal and surface changes were calculated for each pair of CT/CBCT scans within the common imaging field-of-view, as defined in Fig.1. Surface parameters were simulated from the body contour. The anterior surface was extracted from the body and converted to a point cloud. Point clouds were registered using the iterative closest point algorithm in MATLAB to estimate the translational and rotational corrections needed to align the CBCT surface to the reference. These metrics are surrogates to the correction that would have been obtained with surface imaging in treatment position. GI gas variation was then correlated with all metrics. Statistical analysis was performed in Stata 16.1 (5% significance level).