TU-C-17A-08: Improving IMRT Planning and Reducing Inter-Planner Variability Using the Stochastic Frontier Method: Validation Based On Clinical and Simulated Data

Authors

  • Gagne MC,

    1. Departement de Physique, Genie Physique et Optique, Universite Laval, Quebec, Quebec, Canada
    2. CHU de Quebec, Quebec, Quebec, Canada
    3. Centre de recherche sur le cancer, Quebec, Quebec, Canada
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  • Tremblay D,

    1. Departement de Physique, Genie Physique et Optique, Universite Laval, Quebec, Quebec, Canada
    2. CHU de Quebec, Quebec, Quebec, Canada
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  • Varfalvy N,

    1. Departement de Physique, Genie Physique et Optique, Universite Laval, Quebec, Quebec, Canada
    2. CHU de Quebec, Quebec, Quebec, Canada
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  • Archambault L

    1. Departement de Physique, Genie Physique et Optique, Universite Laval, Quebec, Quebec, Canada
    2. CHU de Quebec, Quebec, Quebec, Canada
    3. Centre de recherche sur le cancer, Quebec, Quebec, Canada
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Abstract

Purpose:

Intensity modulated radiation therapy always requires compromises between PTV coverage and organs at risk (OAR) sparing. We previously developed metrics that correlate doses to OAR to specific patients’ morphology using stochastic frontier analysis (SFA). Here, we aim to examine the validity of this approach using a large set of realistically simulated dosimetric and geometric data.

Methods:

SFA describes a set of treatment plans as an asymmetric distribution with respect to a frontier defining optimal plans. Eighty head and neck IMRT plans were used to establish a metric predicting the mean dose to parotids as a function of simple geometric parameters. A database of 140 parotids was used as a basis distribution to simulate physically plausible data of geometry and dose. Distributions comprising between 20 and 5000 were simulated and the SFA was applied to obtain new frontiers, which were compared to the original frontier.

Results:

It was possible to simulate distributions consistent with the original dataset. Below 160 organs, the SFA could not always describe distributions as asymmetric: a few cases showed a Gaussian or half-Gaussian distribution. In order to converge to a stable solution, the number of organs in a distribution must ideally be above 100, but in many cases stable parameters could be achieved with as low as 60 samples of organ data. Mean RMS value of the error of new frontiers was significantly reduced when additional organs are used.

Conclusion:

The number of organs in a distribution showed to have an impact on the effectiveness of the model. It is always possible to obtain a frontier, but if the number of organs in the distribution is small (< 160), it may not represent de lowest dose achievable. These results will be used to determine number of cases necessary to adapt the model to other organs.

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