ESTRO 36 Abstract Book

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