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S825
ESTRO 36
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Material and
Methods
Atlas Based Segmentation (ABS) was utilized for automatic
volume delineation. Volume metrics and DICE coefficient
scores were compared between multiple manual
delineation, ABS and ABS with post processing.
Automatic planning was achieved by python code in the
Raystation treatment planning environment. Initial
optimization objectives were determined by a min-
difference optimization of database entries from previous
clinical plans.
Plan evaluation checks against both standard guidelines
and previous plan quality scores are produced through
python code and inbuilt look up tables. Non-linear scoring
systems are incorporated for total plan scores that provide
score weighting to crucial structures.
Adaptive planning and dose tracking is achieved in a
Varian-Mosaiq-Raystation
environment.
In all case time measurements were use to provide
comparisons between manual and automated processing
of typical radiotherapy planning tasks.
Results
Contouring of DICE scores showed strong agreement (over
0.90) for the vast majority of regions of interest, with an
average DICE coefficient of 77.7 for breast patients and
81.8 for prostate. Results were improved with post
processing.
Breast and prostate plans show comparable plan quality
with manual planning for both simple single phase and
advanced 3-4 target volume techniques with dose volume
histogram differences consistently within 5% TD
Point to point comparisons between automatic
deformation matches and manual user deformations
showed varying results highlighting the current need for
visual QA of deformable
registrations.
Time improvements over manual processes are recoreded
for both breast and prostate patients in all areas of testing
Conclusion
The possibility of automation to provide efficiency and
consistency on a departmental and larger scale is
demonstrated. The work represents a step in the correct
direction rather than a finished produce to radiotherapy
automation and the current limitations and problems are
opened to the audience for responses and questions.
EP-1535 knowledge based planning for lung cancer
patients with stereotactic ablative radiotherapy
S. Smith
1
, P. Houston
1
, G. Currie
1
1
NHS Greater Glasgow & Clyde, Radiotherapy Physics,
Glasgow, United Kingdom
Purpose or Objective
To determine whether a knowledge based treatment
planning system can efficiently produce VMAT plans for
lung cancer patients treated with SABR and to assess plan
quality in relation to different training plan data and
model parameters.
Material and Methods
Three Lung SABR models were developed using the Varian
RapidPlan™ DVH estimation algorithm.
1.
Model A was trained using 60 plans calculated
with both standard and HD MLCs, 6MV flattened
/10 MV flattening filter free (FFF) utilising one
algorithm (AAA version 10.0.28) with no plans
excluded.
2.
Model B included data from model A and a
further 40 plans; 22 plans with statistical
outliers were excluded.
3.
Model C included the data from model B. A
further 78 plans calculated with a new algorithm
version (AAA version 13.6.23) were added to the
model. Statistical outliers were excluded.
The resulting models were then used to
generate plans for ten patients who had not
been included in the model training process.
Comparisons of plans generated by each
RapidPlan model with corresponding clinical
plans were performed against clinical
objectives. Clinical plans were generated by an
experienced physicist using 10MV FFF, HD MLC
and AAA (version 13.6.23).
Results
All of the plans generated by each model met the clinical
objectives. The PTV V99 (Volume of the PTV receiving at
least 99% of the prescription) was comparable between
all three model plans and the plan generated by the
experience physicist (p>>0.05). The R50 (Ratio of the 50%
prescription isodose volume to the PTV) and D2cm (the
maximum dose at 2cm from the PTV) values were
significantly reduced when using the RapidPlan (p<0.05)
for all three models. Model B gave the best results and
was statistically better than model A (p<0.05). Model B
also gave better results for the R100 (Ratio of the 100%
prescription isodose volume to the PTV) than the
experienced planner (P<0.05). This may be due to the
differences in the out of field dose calculation between
versions of AAA. OAR doses were comparable between all
models and the experienced planner (p>0.05)
Conclusion
The RapidPlan™ system was able to generate clinically
acceptable VMAT treatment plans for lung SABR patients,
in a single optimisation, with comparable OAR sparing and
equal or better plan conformity than the original clinically
acceptable plans. allowing for improved consistency and
efficiency in the treatment planning process.
EP-1536 The advantages of Collimator Optimization for
Intensity Modulated Radiation Therapy
S. Pella
1
, B. Brian Doozan
2
1
21st Century Radiation Oncology- Florida Atlantic-
Univeristy and Advanced Radiation Physics Inc.,
Radiation Oncology/Physics, Boca Raton, USA
2
Florida Atlantic University, Physics/Medical Physics,
Boca Raton, USA
Purpose or Objective
The goal of this study was to improve dosimetry for
pelvic, head and neck and other cancer sites with
aspherical planning target volumes (PTV) using a new
algorithm for collimator angles optimization for intensity
modulated radiation therapy (IMRT).
Material and Methods
A retroactive study on the effects of the collimator
optimization for 20 patients was performed by comparing
the dosimetric effects, number of monitor units (MU), and
the treatment delivery time from optimized plans in
Eclipse version 11.0. Keeping all parameters equal,
multiple treatment plans were created using four
collimator angle optimization techniques: CA
0
, all fields
have collimators set to 0°, CA
E
, using the Eclipse
collimator angle optimization, CA
A,
minimizing the area of
the jaws around the PTV, and CA
X
, minimizing the x-jaw
gap. The minimum area and the minimum x-jaw angles
were found by evaluating each field beam’s eye view of
the PTV with
ImageJ
and finding the desired parameters
with a custom script. The evaluation of the plans included
the monitor units (MU), the maximum dose of the plan,
the maximum dose to organs at risk (OAR), the conformity
index (CI) and the number of split fields.
Results
Compared to the CA
0
plans, the monitor units decreased
on average by 6% for the CA
X
with a p-value of 0.01 from
an ANOVA test. The average maximum dose stayed within
1.1% between all four methods with the lowest being CA
X
.
The maximum dose to the most at risk organ was best