ESTRO 36 Abstract Book

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S865 ESTRO 36
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larynx (6), paranasal sinus (4) and two unknown primary
cases. Half of the cohort had been diagnosed with ORN of
the mandible within a median follow-up time of 3.2 years
(range 1.3-5.3) from the end of RT. The toxicity endpoint
considered included ORN complication of any grading. The
other 40 patients were control cases (no ORN observed)
prospectively matched according to primary site and
treatment option (PORT or primary RT and chemotherapy
type).
For given volume vj and dose di levels, the numerical
fraction in a cell of an ACI is composed of the number of
patients (denominator) and the number of patients with
ORN (numerator) that have a mandible percentage volume
between vj and vj+1 exposed to a dose level between di and
di+1. Atlases were created for sub-sets of the entire cohort
to investigate the effects of pre-RT surgery,
chemotherapy, smoking or dental extractions. These risk
factors were also tested with univariate statistical
analysis. Dosimetric variables including dmax and dmean were
tested with ROC analysis.
Results
A dosimetric correlation with ORN incidence was observed
in cases where treatment modality was primary RT (as
opposed to post-operative RT). An increased ORN
incidence was observed towards the large percentage
volume and high doses region of the ACI, where a large
percentage of the mandible volume had received doses of
above 40Gy as well as smaller volume percentages
receiving doses above 64Gy. A similar incidence pattern
was observed for the ACI that included smoking patients
only. The ACI for the sub-set of patients that had
undergone dental extractions pre- or post-RT also showed
a very similar incidence pattern; however, this sub-set
included very few cases.
Conclusion
The ACI analysis carried out so far has shown a dose
response in patients who received primary RT, patients
who smoked at the time of diagnosis and patients who had
dental extractions. The limited number of cases did not
allow for any conclusive statistical significance of the
logistic regression and ROC analysis results. This pilot
study will be expanded to include cases from other centres
to increase the cohort size.
EP-1604 Ion induced complex DNA damage: In silico
modelling of damage and repair using Geant4-DNA.
J.W. Warmenhoven1, N.T. Henthorn1, M. Sotiropoulos1,
R.I. Mackay2, K.J. Kirkby1,3, M.J. Merchant 1,3
1 University of Manchester, Division of Molecular and
Clinical Cancer Sciences, Manchester, United Kingdom
2 The Christie NHS Foundation Trust, Christie Medical
Physics and Engineering, Manchester, United Kingdom
3 The Christie NHS Foundation Trust, Manchester, United
Kingdom
Purpose or Objective
This work uses Monte Carlo simulation to assess and
understand the differences in biological response to
various radiation qualities in the context of hadron
therapy. The current clinical estimator for this is Relative
Biological Effectiveness (RBE), offering a biological dose
conversion between radiation qualities. A large variability
in reported RBE measurements implies that this parameter
does not give the full picture. This variability in RBE is a
major source of uncertainty in ion therapy treatment
planning. Recently, LET based biological effect models
have been proposed, however, among those reviewed
there are uncertainties in the behaviour of key biological
parameters. We approach the problem on a mechanistic
level, linking nanoscale energy deposition to cellular
repair.
Material and Methods
We present a stochastic model to predict ion induced DNA
damage and subsequent repair. DNA damage patterns are
predicted using nanodosimetric principles applied to track
structure simulations within the Monte Carlo based
Geant4-DNA toolkit. A section of detailed DNA geometry is
irradiated to study specific DNA double strand break
structures; building up a library of break models for a
given radiation quality. These patterns are then fed into a
modified Geant4-DNA simulation where the DNA double
strand break ends are explicitly modelled within a
simplified cell nucleus. Double strand break ends then
progress along the predefined Non-Homologous End
Joining repair pathway according to stochastic, time
constant based state changes. This allows the prediction
of differences in DNA repair for a range of radiation
qualities.
Results
We show that break complexity and repair kinetics are
dependent on the particle LET and particle type, with
more complex breaks becoming more probable for higher
LET (fig 1.). Our simulations predict a greater number of
residual DSBs after 24h when higher LET particles are used
(fig 2.), which is in good agreement with the literature.
We also observe a difference in break complexity for
protons and alpha particles at the same LET due to
differences in radiation track structure.
Conclusion
Monte Carlo track structure simulation coupled to a
mechanistic DNA damage repair simulation is a useful tool
for modelling biologically relevant endpoints to cellular
radiation injury. We have modelled DSB damage and repair
with respect to several beam delivery parameters. The
complexity of the biological response caused by different
ions of the same LET was found to differ due to the