ESTRO 36 Abstract Book

S96 ESTRO 36 _______________________________________________________________________________________________

Netherlands 3 Elekta, Veenendaal, The Netherlands

Fig. 1 demonstrated the capability of this QA system by showing a source transit process and Fig. 2 indicated that measured dwell time was affected by source separation. Table 1(a) tabulated the measured dwell time for 3 different assigned dwell times with 5 mm separation between source dwell positions. In all three scenarios, the dwell time at starting position was close to the assigned value. Dwell time at next dwell position experienced a larger discrepancy up to 40% for 0.1 s dwell time. This discrepancy in dwell time was due to the transit time for which control computer could not fully account. Hence, dwell time would be shorter than the assigned value except at the starting position. Table 1(b) tabulated measured dwell times at 3 different source separations with 0.5 s assigned dwell time to assess the compensation method stated. Discrepancy could be up to 0.33 s in 6 cm separation. Transit time occupied a larger portion of the dwell time for longer source separation.

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.

Conclusion Dwell time and transit time could be measured using the fluorescent QA system with uncertainty down to 2 ms. High temporal resolution in this system helped measure the transit time accurately which could hardly be achieved in commonly used QA systems. The effect of transit time on actual source dwell time could be significant and was not fully accounted for by treatment computer. Clinically possible combinations, like 0.5 s dwell time and 5 mm separation, could have a dosimetric error of 8%. PV-0188 Improved class solutions for prostate brachytherapy planning via evolutionary machine learning S.C. Maree 1 , P.A.N. Bosman 2 , Y. Niatsetski 3 , C. Koedood er 1 , N. Van Wieringen 1 , A. Bel 1 , B.R. Pieters 1 , T. Alderliesten 1 1 A cademic Medical Center, Radiation oncology, Amsterdam, The Netherlands 2 Centrum Wiskunde & Informatica, Amsterdam, The