From Diagnostic CT to Treatment: Evaluating the Feasibility of a Simulation-Free Pipeline In a Regional Healthcare Network
Abstract
Purpose
In a typical radiation therapy treatment planning workflow, a CT simulation scan is acquired. This scan is an anatomical mapping of the patient, allowing for the delineation of both the tumor and organs that should be avoided during the treatment. Simulation free radiation therapy (SFRT) is an emerging treatment modality that removes the necessity of the planning CT(pCT) in favor of using a previously acquired diagnostic CT(dCT) scan instead. Removing pCT allows for a more efficient workflow while maintaining accuracy and precision. This study evaluates the feasibility of implementing SFRT for palliative 3DCRT treatments in our institutes.
Methods
A cohort of 120 cancer patients was retrospectively selected based on tumor location, palliative intent, and 3D-CRT delivery technique. dCT scans were acquired internally or externally prior to pCT. A deformable registration was performed and the delineated structures from pCT were transferred to the diagnostic image. To evaluate the feasibility of treatment planning directly on dCT, the Hounsfield Units (HU) of the PTVs and OARs were compared, and the original pCT-based treatment plans were recalculated on the dCT to determine dosimetric effectiveness.
Results
The median Hounsfield unit difference between pCT and dCT images was 4.34% in the PTV and 10% in organs at risk. The preliminary dose difference was 0.05% in the PTV and 0% in OARs, remaining within clinically acceptable limits for palliative treatments. Variability in deformable registration performance was identified as a limiting factor, suggestions that improved standardization could further reduce dose discrepancies.
Conclusion
The insignificant dose differences between pCT and dCT suggest that dCT is a viable method for making palliative plans. SFRT demonstrates the potential to reduce delays and improve efficiency without sacrificing dosimetric accuracy. Future efforts will focus on standardizing the SFRT workflow and integrating autoplanning to maximize clinical speed and treatment efficiency.