ESTRO 2023 - Abstract Book

S1601

Digital Posters

ESTRO 2023

Conclusion Start-of-the-art technology is mature for an affordable and time-efficient workflow, mostly automatized, for repeated CT acquisition, contouring, and dose computation. Our data showed that anatomical changes, which required adaptive replanning, typically occur in only a small number of patients (8 % of cases in our sample of more than two hundred patients). The reasons for replanning are different, and it would be hard to model all of them. In light of the above, individual monitoring by repeated CT scans to select patients who require treatment replanning seems an optimal strategy in adaptive HN PT.

PO-1861 The effect of different optimization function templates on daily online adaptive proton planning

S. Kaushik 1,2 , I. Toma Dasu 2,3 , A. Fredriksson 1 , J. Oden 1

1 RaySearch Laboratories AB, Research and development, Stockholm, Sweden; 2 Stockholm University, Physics and Medical Radiation Physics, Stockholm, Sweden; 3 Karolinska Institutet, Medical Radiation Physics, Stockholm, Sweden Purpose or Objective As a time-saving approach in daily adaptive proton planning, one usually uses the same objective functions and weights each day which might be sub-optimal considering the anatomy and OAR volume changes. Combining multiple optimization function types for the same OAR may counteract the limitations of an individual function type over the treatment course. This study aims to compare the performance of three planning approaches, two involving single and one multiple optimization function types for the same OAR, in online adaptive proton therapy. Materials and Methods Three robustly optimized (3.5%,3mm) plans, aiming at delivering 68 Gy (RBE-weighted dose using RBE=1.1) in 25 fractions to the CTV (clinical goal V95%>99%), were made in RayStation v12A for a sample group consisting of five low-risk prostate cancer patients. The optimization function types for the OARs for the three plans were: (1) max EUD function (plan_1), (2) dose gradient function (plan_2), and (3) max EUD and dose gradient functions with the weights determined from plan_1 and plan_2 (plan_3), such that all three approaches have similar OAR mean doses. A fictive ring structure around the CTV was used to guide the optimization in all plans. The nominal plans optimized on the planning CT (pCT) were recalculated for each patient on a set of virtual CTs (vCTs) obtained from the daily CBCTs. For each patient, the treatment plans were examined separately, and the findings were presented as an average across all fractions without dose accumulation.