108 / 1082
S95
ESTRO 36 2017
_______________________________________________________________________________________________
Netherlands
3
Elekta, Veenendaal, The Netherlands
Purpose or Objective
In PDR and HDR prostate brachytherapy (BT), treatment
plans have to be created in a reasonably short time. In our
clinic, an initial p lan is automatically generated with an
optimization algorithm using a standard parameter set,
called a class solution (CS). Next, the plan is fine-tuned
manually using graphical optimization. The better the CS,
the less fine-tuning is required. We developed a method
to automatically find a CS such that the plans resulting
from the use of this CS match given reference plans as
good as possible, regardless of how these reference plans
were created.
Material and Methods
Twenty patients consecutively treated with PDR BT for
intermediate/high-risk prostate cancer were included.
Clinically acceptable reference plans were created in
Oncentra Brachy using manual graphical optimization
according to our clinical protocol.
To demonstrate our method, we learn CSs for Inverse
Planning Simulated Annealing (IPSA). Per organ, the IPSA
parameter set consists of an acceptable dose range and a
penalty value for violating this range. The ranges follow
from our clinical protocol, and the penalty values are
automatically learned for each patient individually
(IPSA-
I)
by minimizing the difference between the reference and
IPSA-generated plan using the evolutionary algorithm
known as AMaLGaM. Then, three CSs are compared:
- (CS-C)
is the current clinical CS,
- (CS-M)
results from a frequently used strategy for IPSA
by computing the mean of the IPSA-I parameters found
for the individual patients,
-
(CS-S)
is learned by using AMaLGaM again, but this time
aimed at minimizing the sum of plan differences for
multiple patients simultaneously.
Plan difference was measured by the root mean square of
the differences in selected DVH indices (Table). To
prevent overfitting, the data was randomly split into two
sets of 10 patients so that both CS-M and CS-S could be
learned twice: once on each half and validated on the
other half (2-fold cross validation).
Results
Our method is highly accurate when determining IPSA
parameters for individual patients (IPSA-I; dark purple
bars, Figure), with DVH indices of the reproduced plans
differing on average less than 2% of the reference plans
(Table). CS-S performs best for 13 of the patients, and has
the lowest average plan difference. CS-M has a larger plan
difference on average, but outperforms the current
clinical CS-C as well.
Conclusion
Our method for automatically determining class solutions
was found to be advantageous for our patient group,
outperforming the commonly used approach of taking the
mean of IPSA parameters. For individual patients, IPSA
parameters could automatically be found such that the
corresponding plans were very similar to the reference
plans. The performance gap between the latter and the
use of class solutions shows that there is still much room
for improvement by moving toward a patient-tailored
approach for automated BT planning. Our work achieves a
first step in that direction.
PV-0189 Ring applicator source path determination
using a high resolution ionisation chamber array
M. Gainey
1,2
, M. Kollefrath
1
, D. Baltas
1
1
University Medical Centre, Division of Medical Physics-
Department of Radiation Oncology, Freiburg, Germany
2
German Cancer Consortium DKTK, Partner Site Freiburg,
Freiburg, Germany
Purpose or Objective
Commissioning brachytherapy
applicators can be very time consuming. Brachytherapy
has recently seen efforts to perform array based QA
(Espinoza et al. 2013, Espinoza et al. 2015, Kollefrath
2015, Gainey 2015). Previously we described a technique
for determining one source dwell position per
measurement using the OD1000 (PTW-Freiburg) analogous
to film measurements (Kollefrath 2015). In this work we
employ a time resolved high spatial resolution dose
measurement with OD1000 to determine the entire source
path for each interstitial ring applicator (Elekta AB,
Sweden), available in three diameters (R26, R30, R34),
within a single measurement.
Material and Methods
Two microSelectron (Elekta AB, Sweden) v2 afterloaders
(AL1, AL2) were employed to perform all measurements
with 192Ir. A special PMMA jig consisting of a base plate
and a central insert was constructed to mount onto the
OD1000 array. A time resolved (100ms per frame) dose
measurement of the entire source path within the
respective ring applicator was contrived: a single plan for
each ring diameter consisting of 5.0 s dwell time for each
position (associated source strength 42000U). The
resulting data was analysed using an in-house MATLAB
script (version 8.4.0, The Mathworks NA). Typically three
measurements were repeated for both (blue and green)
clinically commissioned rings and for a number of source
exchanges.
Results