Multi-institution investigations of online daily adaptive proton strategies for head and neck cancer patients.

Abstract

Objective: Fast computation of daily reoptimization is key for an efficient online adaptive proton therapy workflow. Various approaches aim to expedite this process, often compromising daily dose. This study compares MGH's online dose reoptimization approach, PSI's online replanning workflow and a full reoptimization adaptive workflow for head and neck cancer (H&N) patients. Approach:10 H&N patients (PSI:5, MGH:5) with daily CBCTs were included. Synthetic CTs were created by deforming the planning CT to each CBCT. Targets and OARs were deformed on daily images. Three adaptive approaches were investigated: i) an online dose reoptimization approach modifying the fluence of a subset of beamlets, ii) full reoptimization adaptive workflow modifying the fluence of all beamlets, and iii) a full online replanning approach, allowing the optimizer to modify both fluence and position of all beamlets. Two non-adapted (NA) scenarios were simulated by recalculating the original plan on the daily image using: Monte Carlo for NAMGH and raycasting algorithm for NAPSI. Main results:All adaptive scenarios from both institutions achieved the prescribed daily target dose, with further improvements from online replanning. For all patients, low-dose CTV D98% shows mean daily deviations of -2.2%, -1.1%, and 0.4% for workflows i, ii, and iii, respectively. For the online adaptive scenarios, plan optimization averages 2.2 minutes for iii) and 2.4 for i) while the full dose reoptimization requires 72 minutes. The OAMGH20% dose reoptimization approach produced results comparable to online replanning for most patients and fractions. However, for one patient, differences up to 11% in low-dose CTV D98% occurred. Significance:Despite significant anatomical changes, all three adaptive approaches ensure target coverage without compromising OAR sparing. Our data suggests 20% dose reoptimization suffices, for most cases, yielding comparable results to online replanning with a marginal time increase due to Monte Carlo. For optimal daily adaptation, a rapid online replanning is preferable. .