ESTRO 36 Abstract Book

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S433 ESTRO 36 2017
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minutes, a speed-up factor of 22. Plan quality was mostly
similar on average. For individual cases however, the
LRPM plans showed clinically more balanced trade-offs
between OAR doses. In comparison with the plan resulting
from the sequential method, relatively large dose
reductions were possible for some OAR(s) at the cost of
relatively small increases of dose for other OAR(s).
Conclusion
The LRPM features very fast automatic multi-criterial
generation of high-quality treatment plans, reducing
Erasmus-iCycle planning times to the order of minutes.
Further research focuses on clinical implementation.
PO-0817 Anatomical robust optimization to deal with
variation in nasal cavity filling during IMPT
S. Van de Water1, F. Albertini2, D.C. Weber2, B.J.M.
Heijmen1, M.S. Hoogeman1, A.J. Lomax2
1 Erasmus MC Cancer Institute, Radiation Oncology,
Rotterdam, The Netherlands
2 Paul Scherrer Institute, Center for Proton Therapy,
Villigen PSI, Switzerland
Purpose or Objective
Intensity-modulated proton therapy (IMPT) for tumors in
the sinonasal and skull-base regions can be seriously
affected by interfraction changes in nasal cavity filling,
resulting in underdosage of the tumor and/or overdosage
of organs-at-risk (OARs). The aim of this study was to
develop an anatomical robust optimization method that
accounts for variation in nasal cavity filling and to
compare it with the conventional single-field uniform dose
(SFUD) approach and with online plan adaptation.
Material and Methods
We included CT data of five patients with tumors in the
sinonasal region, for which the clinical target volume
(CTV) showed large overlap with the nasal cavity. Based
on the planning CT, we generated for each patient 25
‘artificial’ CTs with varying nasal cavity filling (Figure 1).
The minimax robust optimization method available in our
in-house developed treatment planning system was
extended to account for anatomical uncertainties by
including additional (artificial) CTs with varying patient
anatomy as error scenarios in the inverse optimization. For
each patient, we generated treatment plans using: 1) the
SFUD approach (with varying planning target volume (PTV)
margins of 0 mm, 3 mm or 5 mm), 2) anatomical robust
optimization (including two, three or four artificial CTs,
next to the planning CT), and 3) online plan adaptation
(generating a new treatment plan for each artificial CT).
We used the clinically applied 3- or 4-beam arrangements.
Treatment plans were evaluated by recalculating and
accumulating the dose for an entire fractionated 50-Gy
treatment, assuming each artificial CT to correspond to a
2-Gy fraction. We assessed CTV and OAR dose parameters
for the accumulated dose and individual fractions. A
treatment planning strategy was considered adequate
when V95%≥99% and V107% ≤2% in each fraction.
Results
Anatomical robust optimization resulted in adequate CTV
doses if at least three artificial CTs were included next to
the planning CT. Online plan adaptation also resulted in
adequate CTV irradiation, whereas this could not be
achieved using the SFUD approach, even with a PTV margin
of 5 mm (Figure 2). Anatomical robust optimization
provided considerable OAR sparing compared with the
SFUD approach (5 mm margin), with an average reduction
in max-dose and mean-dose parameters of 6.0 Gy (17%)
and 5.8 Gy (24%), respectively. The use of online plan
adaptation resulted in further OAR sparing compared with
anatomical robust optimization, reducing max-dose and
mean-dose parameters on average by 3.8 Gy (13%) and 3.4
Gy (23%), respectively.
Conclusion
We have developed an anatomical robust optimization
method that effectively dealt with the variation in nasal
cavity filling, providing substantially improved CTV
coverage and OAR sparing compared with the conventional
SFUD approach. Online plan adaptation allowed for further
OAR dose reduction and we therefore recommend this
planning strategy to be pursued for future application in
these patients.
PO-0818 Improving plan quality and efficiency by
automated rectum VMAT treatment planning
G. Wortel1, J. Trinks1, D. Eekhout1, P. De Ruiter1, R. De
Graaf1, L. Dewit 1, E. Damen1
1Netherlands Cancer Institute Antoni van Leeuwenhoek
Hospital, Department of Radiation Oncology,
Amsterdam, The Netherlands
Purpose or Objective
To develop, evaluate, and implement fully automated
VMAT plan generation for rectum patients that receive
either palliative 39 Gy (13×3 Gy), or curative 45 Gy (25×1.8
Gy, postoperative), 50 Gy (25×2 Gy, preoperative)
treatment.
Material and Methods
The automatic rectum VMAT plan generation is performed
by a combination of our in-house developed automation
framework FAST and the Pinnacle 3 Auto-Planner. The
automatic planning starts after the physician has
delineated the rectum target volume(s). FAST starts our
TPS Pinnacle3, creates a patient record, and imports the
CT. The patient’s skin and bladder are auto-segmented by
Pinnacle3’s module SPICE. In addition, the small bowel is
delineated using a custom-made FAST module. The