It is recognized that the use of a single plan calculated on an image acquired some time before the treatment is generally insufficient to accurately represent the daily dose to the target and to the organs at risk. This is particularly true for protons, due to the physical finite range. Although this characteristic enables the generation of steep dose gradients, which is essential for highly conformal radiotherapy, it also tightens the dependency of the delivered dose to the range accuracy. In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution. A plan should be ideally adapted as soon as anatomical variations occur, ideally online. In this review, we describe in detail the different steps of the adaptive workflow and discuss the challenges and corresponding state-of-the art developments in particular for an online adaptive strategy.

In radiation therapy, a plan is robust if the calculated and the delivered dose are in agreement, even in the case of different uncertainties. The current practice is to use safety margins, expanding the clinical target volume sufficiently enough to account for treatment uncertainties. This, however, might not be ideal for proton therapy and in particular when using intensity modulated proton therapy (IMPT) plans as degradation in the dose conformity could also be found in the middle of the target resulting from misalignments of highly in-field dose gradients. Single field uniform dose (SFUD) and IMPT plans have been calculated for different anatomical sites and the need for margins has been assessed by analyzing plan robustness to set-up and range uncertainties. We found that the use of safety margins is a good way to improve plan robustness for SFUD and IMPT plans with low in-field dose gradients but not necessarily for highly modulated IMPT plans for which only a marginal improvement in plan robustness could be detected through the definition of a planning target volume.

Objective. This study presents the first clinical implementation of an efficient online daily adaptive proton therapy workflow (DAPT). Approach. The DAPT workflow includes a pre-treatment phase, where a template and a fallback plan are optimized on the planning computed tomography (CT). In the online phase , the adapted plan is re-optimized on daily images from an in-room CT. Daily structures are rigidly propagated from the planning CT. Automated Quality Assurance (QA) involves geometric, sanity checks and an independent dose calculation from the machine files. Differences from the template plan are analyzed field-by-field, and clinical plan is assessed by reviewing the achieved clinical goals using a traffic light protocol. If the daily adapted plan fails any QA or clinical goals, the fallback plan is used. In the offline phase the delivered dose is recalculated from log-files onto the daily CT, and a gamma analysis is performed (3%/3 mm). The DAPT workflow has been applied to selected adult patients treated in rigid anatomy for the last serie of the treatment between October 2023 and April 2024. Main Results. DAPT treatment sessions averaged around 23 min [range: 15–30 min] and did not exceed the typical 30 minute time slot. Treatment adaptation, including QA and clinical plan assessment, averaged just under 7 min [range: 3:30–16 min] per fraction. All plans passed the online QAs steps. In the offline phase a good agreement with the log-files reconstructed dose was achieved (minimum gamma pass rate of 97.5%). The online adapted plan was delivered for >85% of the fractions. In 92% of total fractions, adapted plans exhibited improved individual dose metrics to the targets and/or organs at risk. Significance. This study demonstrates the successful implementation of an online daily DAPT workflow. Notably, the duration of a DAPT session did not exceed the time slot typically allocated for non-DAPT treatment. As far as we are aware, this is a first clinical implementation of daily online adaptive proton therapy.

In this paper the influence of varying the starting conditions on intensity-modulated proton therapy (IMPT) plans has been studied. In particular IMPT plans have been optimized based on four different starting conditions of initial beamlet fluences: (a) all beamlets with an initial constant weight, delivering a gradient from the proximal to the distal edge of the target (forward wedge approach); (b) beamlet weights reduced from the distal to the proximal aspect of the target such as to deliver a flat 'spread-out-Bragg-peak' (SOBP approach); (c) beamlet weights calculated to deliver a gradient from the distal (maximal dose) to the proximal edge (inverse wedge); (d) beamlet weights set universally to zero except the most distal one, for each given lateral direction (i.e. distal-edge-tracking, DET). An analysis of robustness to range errors has been performed by recalculating plans, assuming a systematic 3% error in CT values. Results showed that IMPT plans optimized with the forward wedge approach were very sensitive to range errors, since organs-at-risk (OAR) were spared by patching single-field lateral and distal fall-offs, the last ones being strongly sensitive to range errors. In addition a plan robust to range errors can be achieved by starting the optimization process in the case of low-dose constraints to OAR, with the initial flat SOBP approach, and with either the DET or the inverse wedge approaches, in the case of stringent dose-volume constraints to OAR. 'Starting condition-based optimization' as proposed here can therefore provide a tool to transparently 'steer' the optimization outcome to solutions more robust to uncertainties.