Analysis and Development of Phenomenological Models for the Relative Biological Effectiveness in Proton Therapy
Type
Doctoral thesisNot peer reviewed
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Date
2019-08-23Author
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Proton therapy is undergoing a rapid development making it increasingly popular as a
treatment of cancer. Protons interact differently with the tissue compared to
conventional radiation therapy with photons, resulting in a more beneficial dose
distribution with greater dose conformation. The radiation quality is also different for
protons and photons, as the ionisation density, often quantified by the linear energy
transfer (LET), is higher for protons than for photons. Irradiation experiments on cells
and animal models have shown that protons are slightly more effective in producing
biological damage than photons. This difference in biological response is quantified by
the relative biological effectiveness (RBE), which aid the comparison of the dose
deposition from the two modalities and enables transferal of established clinical
protocols from photon therapy to proton therapy. A conservative and constant RBE of
1.1 is used in proton therapy clinics, even though experiments have shown that the RBE
can be both higher and lower, varying with different biological and physical quantities,
including the LET value.
Phenomenological RBE models try to determine the various RBE dependencies from
large experimental databases of cell irradiation experiments. In this work, existing
phenomenological models were analysed and explored in a coherent manner: All
models were parameterised and described by functions of the maximum RBE (RBEmax)
and minimum RBE (RBEmin), the two model functions that make every model unique.
The models were implemented in the FLUKA Monte Carlo code and used in estimation
of the RBE and RBE-weighted dose for multiple patient plans and relevant dose
parameters. The models were also analysed and compared regarding the underlying
similarities and differences, which forms the basis for the unique definitions of RBEmax
and RBEmin of each model. A new phenomenological RBE model was proposed,
introducing the full LET spectrum as an input parameter for phenomenological models.
Statistical methods were used to test whether a non-linear LET dependency of RBEmax
would give a superior description of the experimental data compared to using the
established linear dependency of the dose-averaged LET (LETd). Further, we analysed
the LETd dependency of RBEmin in a two-step regression analysis, as the RBEmin function is most commonly assumed to be constant for all LETd values. Specifically,
we analysed how restriction on the minimum dose of the underlying experimental
database influenced RBEmin.
The estimation of the RBE and the RBE-weighted dose from the various models
differed significantly. The largest deviations were seen for organs at risk (OAR) with
low fractionation sensitivity ((α/β)x) and high LET. These variations are a result of the
distributions of (α/β)x values and LETd values in the experimental databases, the
assumptions for RBEmax and RBEmin and regression analysis method. The full LET
spectrum was found to give a better representation of the experimental database
included in our analysis. Regression weighted to the reported experimental
uncertainties showed that a non-linear function (quartic function) gave a better fit to
the data than a linear function. The RBEmin function was found to vary with the LETd
value if dose constraints were added to the experimental database. By restricting the
minimum dose in the database to be 1 Gy or lower, the analysis gave a non-negligible
linear LETd dependency, while higher minimum doses indicated that the dependency
is constant.
The deviations in the estimated RBE from the models can be traced back to the model
differences in the database construction, the model assumptions and the regression
techniques. Various methods were used in this thesis to develop novel models by
reanalysing published data, such as construction of model databases with strict
constraints, using the pure dose-survival data instead of only α and β values, statistical
analysis of model assumptions, applying multiple regression techniques and
recognition of the LET spectrum as a relevant input parameter. Together, these
techniques could minimise the researcher bias and make more accurate RBE models,
resulting in better dose predictions for clinically relevant scenarios.
Has part(s)
Paper I: Rørvik E, Fjæra LF, Dahle TJ, Dale JE, Engeseth GM, Stokkevåg CH, Thörnqvist S, Ytre-Hauge KS. (2018) Exploration and application of phenomenological RBE models for proton therapy. Physics in Medicine & Biology 63(18), p185013. The article is not available in BORA due to publisher restrictions. The published version is available at: http://doi.org/10.1088/1361-6560/aad9db Paper II: Rørvik E, Thörnqvist S, Stokkevåg CH, Dahle TJ, Fjæra LF, Ytre-Hauge KS. (2017) A phenomenological biological dose model for proton therapy based on linear energy transfer spectra. Medical Physics 44(6), p2586-2594. The article is not available in BORA due to publisher restrictions. The published version is available at: http://doi.org/10.1002/mp.12216 Paper III: Rørvik E, Thörnqvist S, Ytre-Hauge KS. The experimental dose ranges influence the LETd dependency of the proton minimum RBE (RBEmin). Physics in Medicine & Biology 64(19), p195001. The article is not available in BORA due to publisher restrictions. The published version is available at: https://doi.org/10.1088/1361-6560/ab369a
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The University of BergenCollections
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