موجز عن البحث:
comparing relative induced second cancer risk, to inform choice of
radiotherapy treatment plan, are becoming increasingly necessary as the
availability of new treatment modalities expands. Uncertainties, in both
radiobiological models and model parameters, limit the confidence of such
calculations. The aim of this study was to develop and demonstrate a software
tool to produce a malignant induction probability (MIP) calculation which
incorporates patient-specific dose and allows for the varying responses of
different tissue types to radiation.
The tool has been
used to calculate relative MIPs for four different treatment plans targeting
a subtotally resected meningioma: 3D conformal radiotherapy (3DCFRT), volumetric
modulated arc therapy (VMAT), intensity-modulated x-ray therapy (IMRT), and
Two plausible MIP
models, with considerably different dose–response relationships, were
considered. A fractionated linear–quadratic induction and cell-kill model
gave a mean relative cancer risk (normalized to 3DCFRT) of 113% for VMAT, 16%
for protons, and 52% for IMRT. For a linear no-threshold model, these figures
were 105%, 42%, and 78%, respectively. The relative MIP between plans was
shown to be significantly more robust to radiobiological parameter
uncertainties compared to absolute MIP. Both models resulted in the same
ranking of modalities, in terms of MIP, for this clinical case.
results demonstrate that relative MIP is a useful metric with which treatment
plans can be ranked, regardless of parameter- and model-based uncertainties.
With further validation, this metric could be used to discriminate between
plans that are equivalent with respect to other planning priorities.
than half of cancer patients receive radiotherapy for radical or palliative
purposes. Increasing survival rates in cancer patients make it important to
study late side-effects, including secondary radiation-induced cancers.
Although a number of predictive models exist, the absolute accuracy of these
models in the radiotherapy dose range is limited partly due to scarcity of
data and partly by extrapolation beyond historical data bounds. The aim of
this work is to investigate transforming modelled absolute malignant
induction probabilities into life time excess relative risk estimates for
cancer related death (ERR) to allow comparison of the results with the
relevant risk estimates in Life Span Study (LSS) report no.14.