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A theoretical framework for prescribing radiotherapy dose distributions using patient‐specific biological information
Author(s) -
South C. P.,
Partridge M.,
Evans P. M.
Publication year - 2008
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 - 10.1118/1.2975229
Subject(s) - radiation therapy , dosimetry , parametric statistics , formalism (music) , medical imaging , nuclear medicine , mathematics , medicine , mathematical optimization , statistics , radiology , art , musical , visual arts
We present a formalism for using functional imaging both to derive patient‐specific radiobiological properties and consequently to prescribe optimal nonuniform radiotherapy dose distributions. The ability to quantitatively assess the response to an initial course of radiotherapy would allow the derivation of radiobiological parameters for individual patients. Both an iterative optimization and an analytical approach to this problem were investigated and illustrated by application to the linear‐quadratic model of cell killing using simulated parametric data for a modeled tumor. Potential gains in local control were assessed by comparing uniform dose distributions with optimized dose distributions of equal integral dose. The effect on local prescribed dose of variations in effective radiosensitivity, tumor burden, and proliferation rate was investigated, with results suggesting that dose variations would be significant but clinically achievable. The sensitivity of derived parameters to image noise and the effect of varying the initial fractionation and imaging schedule were assessed. The analytical approach proved remarkably robust, with 10% image noise resulting in dose errors of approximately 1% for a clinically relevant set of parameters. Potential benefits were demonstrated by using this formalism to prescribe nonuniform dose distributions for model tumors using a range of literature‐derived parameters. The redistribution of dose improved tumor control probability by factors between 1.03 and 4.27 for a range of model tumors.