Abstract
BackgroundTo evaluate in-silico the performance of a model-based optimization process for volumetric modulated arc therapy (RapidArc) applied to hepatocellular cancer treatments.Patients and methods45 clinically accepted RA plans were selected to train a knowledge-based engine for the prediction of individualized dose-volume constraints. The model was validated on the same plans used for training (closed-loop) and on a set of other 25 plans not used for the training (open-loop). Dose prescription, target size, localization in the liver and arc configuration were highly variable in both sets to appraise the power of generalization of the engine. Quantitative dose volume histogram analysis was performed as well as a pass-fail analysis against a set of 8 clinical dose-volume objectives to appraise the quality of the new plans.ResultsQualitative and quantitative equivalence was observed between the clinical and the test plans. The use of model-based optimization lead to a net improvement in the pass-rate of the clinical objectives compared to the plans originally optimized with standard methods (this pass-rate is the frequency of cases where the objectives are respected vs. the cases where constraints are not fulfilled). The increase in the pass-rate resulted of 2.0%, 0.9% and 0.5% in a closed-loop and two different open-loop validation experiments.ConclusionsA knowledge-based engine for the optimization of RapidArc plans was tested and lead to clinically acceptable plans in the case of hepatocellular cancer radiotherapy. More studies are needed before a broad clinical use.
Highlights
To evaluate in-silico the performance of a model-based optimization process for volumetric modulated arc therapy (RapidArc) applied to hepatocellular cancer treatments
More studies are needed before a broad clinical use
This is made of three main components: a model building and training engine; a model-based dose-volume histogram (DVH) and automated constraints prediction tool; a new volumetric modulated arc therapy (VMAT) and IMRT optimization algorithm to manage the above
Summary
To evaluate in-silico the performance of a model-based optimization process for volumetric modulated arc therapy (RapidArc) applied to hepatocellular cancer treatments. The radiation-oncology treatment planning process, is a step where many information shall merge leading to the most appropriate technique and dose distribution for all individual patients. Different levels of “knowledge” contribute to decision making. The determination of the appropriate dose-volume constraints is a problem that could be “modeled” if the “features” causing inter-patient variability could be converted into mathematical methods. These features include the geometric, anatomical and dosimetric characteristics of the treatment plans. The group of the Duke University developed so-called knowledge-based
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