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 In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution.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.

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Non-small cell lung cancer (NSCLC) patients show typically large anatomical changes during treatment, making recalculation or adaption necessary. For report and review, the applied treatment dose can be accumulated on the reference planning CT using deformable image registration (DIR). We investigated the dosimetric impact of using six different clinically available DIR algorithms for dose accumulation in presence of inter-fractional anatomy variations. For seven NSCLC patients, proton treatment plans with 66 Gy-RBE to the planning target volume (PTV) were optimised. Nine repeated CTs were registered to the planning CT using six DIR algorithms each. All CTs were acquired in visually guided deep-inspiration breath-hold. The plans were recalculated on the repeated CTs and warped back to the planning CT using the corresponding DIRs. Fraction doses warped with the same DIR were summed up to six different accumulated dose distributions per patient, and compared to the initial dose. The PTV-V95 of accumulated doses decreased by 16% on average over all patients, with variations due to DIR selection of 8.7%. A separation of the dose effects caused by anatomical changes and DIR uncertainty showed a good agreement between the dose degradation caused by anatomical changes and the dose predicted from the average of all DIRs (differences of only 1.6%). The dose degradation caused by anatomical changes was more pronounced than the uncertainty of employing different DIRs for dose accumulation, with averaged results from several DIRs providing a good representation of dose degradation caused by anatomy. However, accumulated dose variations between DIRs can be substantial, leading to an additional dose uncertainty.

Proton therapy with active scanning beam delivery has significant advantages compared to conventional radiotherapy. However, so far only static targets have been treated in this way, since moving targets potentially lead to interplay effects. For 4D treatment planning, information on the target motion is needed to calculate time-resolved dose distributions. In this study, respiratory liver motion has been extracted from 4D CT data using two deformable image registration algorithms. In moderately moving patient cases (mean motion range around 6 mm), the registration error was no more than 3 mm, while it reached 7 mm for larger motions (range around 13 mm). The obtained deformation fields have then been used to calculate different time-resolved 4D treatment plans. Averaged over both motion estimations, interplay effects can increase the D₅-D₉₅ value for the clinical target volume (CTV) from 8.8% in a static plan to 23.4% when motion is considered. It has also been found that the different deformable registration algorithms can provide different motion estimations despite performing similarly for the selected landmarks, which in turn can lead to differing 4D dose distributions. Especially for single-field treatments where no motion mitigation is used, a maximum (mean) dose difference (averaged over three cases) of 32.8% (2.9%) can be observed. However, this registration ambiguity-induced uncertainty can be reduced if rescanning is applied or if the treatment plan consists of multiple fields, where the maximum (mean) difference can decrease to 15.2% (0.57%). Our results indicate the necessity to interpret 4D dose distributions for scanned proton therapy with some caution or with error bars to reflect the uncertainties resulting from the motion estimation. On the other hand, rescanning has been found to be an appropriate motion mitigation technique and, furthermore, has been shown to be a robust approach to also deal with these motion estimation uncertainties.