Incorporating Model Parameter Uncertainty into Prostate IMRT Treatment Planning
Abstract
Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frame set developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter that describes tissue-specific effect in EUD model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect cause by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for use to maximally utilize the available radiobiology knowledge for better IMRT treatment.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2004
- Accession Number
- ADA427063
Entities
People
- David Y. Yang
- Jun Lian
Organizations
- Stanford University