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prostate patients by acquiring the conversion algorithms
within and outside an automatically contoured bone
outline. The quality of the produced sCT images was
quantified by HU and dose distribution comparisons
against standard CT images. The treatment planning was
conducted with VMAT.
Results
Figure 1 shows examples of the constructed sCTs with the
original MR images of each scanner. The mean HU
difference for the sCTs was 11 HUs and 90 HUs in the soft
and bone tissue volumes, respectively (n=9). The target
volume dose differences compared to the CTs were within
0.8% in all cases (0.2±0.5% [average±SD, n=9]). Table 1
presents the HU and dose distribution differe nces
between the sCTs and the actual CT images.
Conclusion
This study revealed the feasibility of generating high
quality sCTs directly from intensity values of standard T2-
weighted MR images. The applied sCT generation method
is adjustable for images applied by multiple
manufacturers’ scanners with different clinical settings.
This work can further contribute to wider clinical
implementation of MRI-only based radiotherapy treatment
planning.
Proffered Papers: Optimatisation algorithms for
treatment planning
OC-0443 Robust optimization of VMAT in head and
neck patients
D. Wagenaar
1
, R.G.J. Kierkels
1
, J. Free
1
, J.A.
Langendijk
1
, E.W. Korevaar
1
1
UMCG University Medical Center Groningen,
Department of Radiation Oncology, Groningen, The
Netherlands
Purpose or Objective
In the Netherlands, a subgroup of patients eligible for
proton therapy will be selected using the model-based
approach. So far, patients were selected by comparing
robustly optimized proton plans to non-robustly optimized
photon plans. However, using non-robust optimization for
photon plans may not explore the full potential of this
technique, possibly leading to an unfair comparison. The
main objective of this study was to investigate the benefit
of robust optimization for head and neck using photon
techniques.
Material and Methods
A cohort of nine head and neck cancer patients clinically
treated with VMAT were included. Their non-robustly
optimized (i.e. PTV-based) VMAT plans were copied and
their objectives were altered to create robustly optimized
(i.e. CTV-based) VMAT plans. Plans were re-optimized
with worst case robust objectives for the targets. Hence,
the actual given dose was estimated by calculating the
dose on 35 daily cone beam CT (CBCT) scans after position
correction, deforming the dose distributions to the
planning CT and summing doses from all fractions. All
estimated actual given dose distributions were inspected
and approved by a physician with experience in head and
neck radiotherapy. Treatment plan quality was evaluated
using dosimetric parameters and normal tissue
complication probability (NTCP) values using previously
published models for tube feeding dependence, grade 2-4
dysphagia, xerostomia and sticky saliva, 6 months after
treatment.
Results
Robustly optimised plans resulted in a lower actual given
dose estimation for all organs at risk (figure 1). The
difference in dose distribution and dose volume
histograms of a typical case are displayed in figure 2. The
average NTCP values were lower for the robustly
optimized plans by 0.5% (p = 0.07) for tube feeding
dependence, 1.8% (p = 0.04) for dysphagia, 3.0% (p = 0.01)
for xerostomia and 1.1% (p = 0.02) for sticky saliva.
Moreover, target dose conformity slightly improved using
robustly optimized VMAT optimization.