Abstract Book

S110

ESTRO 37

Conclusion The DMR algorithm automatically optimized a robust proton plan using a photon reference dose. For the studied cases, the DMR plans qualified the same patients for proton therapy as the manually optimized proton plans. We developed a fully automated tool to select head and neck cancer patients for proton therapy by combining the DMR algorithm with the model-based approach. PV-0202 Towards a more robust 4D optimization for PBS-based moving tumor treatments Y. Zhang 1 , I. Huth 2 , D.C. Weber 1 , A.J. Lomax 1 1 Paul Scherrer Institut, Centre for Proton Therapy, Villigen-PSI, Switzerland 2 Varian Medical Systems – Particle Therapy GmbH, Particle Therapy GmbH, Troisdorf, Germany Purpose or Objective Complementary to classic motion mitigation approaches (rescanning, gating, breath hold, tracking or combinations of these), 4D optimization can inherently include motion into the process of proton pencil beam weight optimization, resulting in a ‘motion-robust’ plan. We aim here to evaluate this approach systematically using 4DCT-MRI datasets, and to investigate the possibility of increasing plan robustness by incorporating rescanning into the optimization. Material and Methods Using nine 4DCT(MRI) datasets of liver tumors (CTV:100- 400cc), 3D plans (Fig. 1) were generated on the end-of- exhalation CT phase using a geometric ITV (encapsulating CTVs at all 4DCT phases), and a 4D dose calculation (Fig. 1) was performed by considering regular and irregular motion patterns (range: 8-20mm; period: 3.3-6.3s) with and without rescanning (x5 layered). In addition, 4D, directly optimized plans, which accurately take into account beam delivery dynamics and organ motion, were calculated to the CTV (on reference CT) without expanded internal margins. The robustness of optimized 4D plans were assessed by recalculating the optimized 4D plans under variable motion scenarios with phase delays up to 1s (in 50ms intervals). All re-calculated 4D plans were quantified and compared using homogeneity index (HI) of D5-D95 in the CTV as well as V10%, V20% and V60% in the healthy liver (liver-CTV).

Results Independent on the motion pattern (regular or irregular), amplitudes (up to 20mm) or periods (either fast or slow breathers), interplay effects could be effectively mitigated using 4D optimization alone, with HI in the CTV being <15% (Figure 2). Combining 4D optimization with rescanning however can significantly improve optimized plan quality, with HI in the CTV to within 5% of a static 3D plan. For the ‘best-case’ rescanned 4D plans, 4D optimization could be able to provide 'zero-motion- margin” for 4D treatments (in Fig. 1), resulting in pronounced reduction of dose to the healthy liver (median 5% for V10/20/60% indexes). However, optimized 4D plans, especially those without rescanning, have been shown to be extremely sensitive to variations of the presumed (during the optimization) and actual motion conditions during delivery. Nevertheless, the robustness of 4D optimized plans could be substantially increased by combining optimization with rescanning, with comparable plan quality (within 5%), being achievable for rescanned 4D optimized plans even when allowing for phase shifts between optimization and delivery of up to 500ms, This is in contrast to the less than 200ms shifts that were observed to degrade plan quality for 4D plans optimized without rescanning.

Conclusion 4D plan optimization can substantially minimize ITV margins, therefore reducing dose to normal issues. Incorporating rescanning into 4D optimization is beneficial for achieving such goals, thanks to the significantly increased 4D plan robustness w.r.t. motion variations. PV-0203 Energy layer reduction strategies for single- and multi field optimization of proton lung plans M. Fuglsang Jensen 1 , L. Hoffmann 2 , J.B.B. Petersen 2 , D.S. Møller 2 , M.L. Alber 3 1 Aarhus University Hospital, Department of Oncology and Danish Centre for Particle Therapy, Aarhus C, Denmark 2 Aarhus University Hospital, Department of Medical Physics, Aarhus C, Denmark 3 Heidelberg University Hospital, Department of Radiation Oncology, Heidelberg, Germany Purpose or Objective For proton therapy treatments of lung cancer, robust delivery can be ensured by gating and rescanning techniques. This, however, increases the treatment time which translates into a higher risk of intra fractional changes. Treatment times can be reduced by limiting the number of energy layers, but this can challenge the target coverage and plan robustness. We investigate to what extend the number of layers can be reduced in single field optimization (SFO) and multi field optimization (MFO) plans using three different strategies.