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Incorporating model parameter uncertainty into inverse treatment planning
Author(s) -
Lian Jun,
Xing Lei
Publication year - 2004
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1785451
Subject(s) - formalism (music) , radiation treatment planning , inverse , statistical model , inverse problem , computer science , mathematics , dosimetry , mathematical optimization , probability density function , algorithm , statistical physics , radiation therapy , statistics , physics , nuclear medicine , machine learning , medicine , art , musical , mathematical analysis , geometry , visual arts
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 frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue‐specific effect in the equivalent uniform dose (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 caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment.