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Conclusion
Robust CTV-based VMAT optimization in head and neck
patients resulted in improved estimated actual given dose
distributions with lower normal tissue dose and equal
target coverage compared to non-robustly optimized
plans. This is the first study to compare robustly optimized
photon plans to non-robustly optimized photon plans in
terms of dose accumulation using daily CBCT images. The
differences in dose are deemed clinically relevant and are
expected to lead to an improved method of patient
selection for proton therapy.
OC-0444 Pareto-optimal plans as ground truth to
validate a commercial knowledge-based DVH-
prediction system
E. Cagni
1
, A. Botti
1
, Y. Wang
2
, M. Iori
1
, S.F. Petit
2
, B.J.
Heijmen
2
1
Arcispedale S. Maria Nuova - IRCCS, Medical Physics
Unit, Reggio Emilia, Italy
2
Erasmus MC Cancer Institute, Department of Radiation
Oncology, Rotterdam, The Netherlands
Purpose or Objective
The purpose of the current study was two fold. First, to
evaluate the DVH prediction accuracy of RapidPlan (Varian
Medical Systems, Palo Alto) using a large database of
Pareto optimal treatment plans. These were consistently
generated with automated prioritized multi-criterial
treatment plan optimization, independent of RapidPlan,
and therefore can be considered as an unbiased, ground
truth of achievable plan quality. Second, to determine the
importance of the size/variability of the plan database on
the accuracy of the RapidPlan DVH predictions. Using the
automatically generated plans, the prediction accuracy of
RapidPlan could be investigated without an impact of
unavoidable plan quality variations related to manual
planning.
Material and Methods
A previously published database of 115 Pareto-optimal
prostate VMAT plans, consistently generated with
automatic prioritized planning, was used in this study
[Wang et al, PMB, 2016]. Separate Rapidplan prediction
models were generated for training groups consisting of
20, 30, 45, 55, or 114 randomly selected plans, the latter
using a leave-one-out technique. In a second experiment,
Model-20 was also built for 4 other groups of randomly
selected training patients. Prediction accuracy of all
models was assessed using a fixed, independent validation
group of 60 plans and comparing predicted dose
parameters (rectum Dmean, V65, and V75, anus Dmean,
and bladder Dmean) with the achieved values of the
Pareto optimal plans.
Results
For Model-114, the absolute (relative) prediction errors
(mean±SD), for rectum Dmean, V65, and V75 were
1.8±1.4Gy (6.9±5.6%), 1.0±0.9% (8.9±13.4%), and 1.6±1.4%
(36.3±62.2%), respectively. For anus and bladder Dmean,
these errors were 2.2±1.7Gy (18.3±21.3%) and 1.8±1.3Gy
(4.9±4.2%), respectively. For 63.3% of the validation plans,
Model-114 predicted a lower rectum V65 than could
actually be achieved. Because of the prioritized
optimization used for generating the input Pareto-optimal
plans, this can only be realized by underdosing the target.
In 36.7% of plans, the predicted V65 was higher than
obtained in the input plan, possibly losing an opportunity
for lower rectum dose.
Table 1
demonstrates equal
prediction accuracies for model-114 and the smaller
models (only first Model-20 included).
Table 2
compares
Model-114 with all 5 investigated Models-20, showing
significant differences in performance of the Models-20.