Implementing a simple dose prediction tool to improve plan quality in cervical brachytherapy
David Thwaites,
United Kingdom
PO-2152
Abstract
Implementing a simple dose prediction tool to improve plan quality in cervical brachytherapy
Authors: Emily Flower1, Emma Sullivan2, Gemma Busuttil2, Niluja Thiruthaneeswaran2, Jonathan Sykes2, Alison Salkeld2, David Thwaites3, Jennifer Chard2
1Crown Princess Mary Cancer Centre , Radiation Oncology, Westmead, Australia; 2Crown Princess Mary Cancer Centre, Radiation Oncology, Westmead, Australia; 3University of Sydney, Institute of Medical Physics, Camperdown, Australia
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Purpose or Objective
Purpose: Toxicity from cervical brachytherapy has been demonstrated to mostly relate to the D2cc. Therefore a simplified version of knowledge based planning may be possible, only investigating the relationship of the overlap distance for 2cc and the D2cc from planning. This study aims to investigate if such a simple relationship could be used to predict the D2cc, and if this can be used to detect sub-optimal plans and improve plan quality.
Material and Methods
Methods: The overlap volume histogram (OVH) method was used to determine the distance for 2cc of overlap between the OAR and CTVHR. Linear plots were used to model the OAR D2cc and OVH predicted 2cc distance. The model was used to predict D2cc, and this value entered as the maximum constraint in the inverse planning algorithm. Two datasets of 20 patients (plans from 43 insertions in each dataset) each were used to create 2 models, and the performance of each model was compared. Doses were scaled to ensure consistent CTVHR D90 doses.
Results
Results: Mean bladder doses decreased by 2.9% for both models, mean rectal dose decreased 14.9% for model 1 and 6.0% for model 2, mean sigmoid dose decreased 10.7% for model 1 and 6.1% for model 2, mean bowel dose decreased 4.1% for model but no statistically significant difference observed for model 2.
Conclusion
Conclusion: A simplified knowledge based planning method was used to predict D2cc and was able to automate optimization of brachytherapy plans for locally advanced cervical cancer.