ESTRO 2022 - Abstract Book

S342

Abstract book

ESTRO 2022

Conclusion A comprehensive assessment framework for geometric and dosimetric DIR uncertainties was developed and validated for HN patients. DIR induced dosimetric uncertainties for dose accumulation of PT for HN cases are substantial and potentially of clinical relevance. However, the model-based estimation provides a good estimate of these uncertainties, and more accurately than previous validations for the lung case [Amstutz et al. 2021].

PD-0401 Assessment of CBCT based synthetic CT generation accuracy for adaptive radiotherapy planning

C. O'Hara 1 , D. Bird 1 , R. Speight 1 , S. Andersson 2 , R. Nilsson 2 , B. Al-Qaisieh 1

1 Leeds Teaching Hospitals NHS Trust, Leeds Cancer Centre, Leeds, United Kingdom; 2 RaySearch Laboratories, Research, Stockholm, Sweden Purpose or Objective The ability to calculate dose on CBCTs, using synthetic CTs (sCTs), has the potential to make the adaptive radiotherapy (ART) pathway more efficient and remove subjectivity from the process. Implementing sCTs generated from CBCTs into the ART pathway would also reduce CT scanner workload and allow adaptive treatment plans to be delivered more quickly. This study assessed the dosimetric and Hounsfield units (HU) similarity of CBCT-based sCTs compared to CTs as well as the sCT generation time. sCTs were generated using a commercially available treatment planning system. Materials and Methods Fifteen head and neck rescan patients were used to assess four methods of sCT generation using RayStation Research version 9B. Each patient’s planning CT (pCT), rescan CT (rCT), and the first CBCT after the rCT were obtained, using the rCT as the comparator. The CBCT was deformed to the rCT geometry (dCBCT) and used as the input for sCT generation. Method 1 deformably registered the pCT to the dCBCT. Method 2 assigned the range of dCBCT intensity values to six mass density values. Method 3 iteratively removed low-frequency artefacts and assigned a HU function to the dCBCT values. Method 4 used a cycle generative adversarial network (cycleGAN) machine learning model (independently trained using 45 head and neck patient dCBCTs and pCTs) to generate an sCT. Methods 1, 3, and 4 are currently RayStation Research only scripted methods. A treatment plan conforming to the local clinical protocol was created on each rCT and recalculated on each sCT. Planning target volume (PTV) and organ at risk (OAR) structures were contoured by clinicians on the rCT to allow assessment of dose- volume histogram (DVH) statistics. The mean absolute error (MAE) of the HU, dose differences of PTV and OAR structures (high-dose PTV, low-dose PTV, spinal canal, larynx, brainstem, and parotids) at clinically relevant DVH points, and global gamma index analysis (2%/2 mm) were used to assess the differences between the sCT and rCT. sCT generation time, including validation, was also recorded. Results For methods 1, 2, 3, and 4 the MAE, gamma index analysis, and generation time were: 59.7 HU, 100.0%, and 143 s; 164.2 HU, 95.2%, and 232 s; 75.7 HU, 99.9%, and 153 s; and 79.4 HU, 99.8%, and 112 s respectively. All assessed dose differences were <0.3 Gy except for method 2 (<0.5 Gy). An example of the dose differences between the rCT and sCTs are shown in Figure 1.