External beam radiotherapy (EBRT) is a method for treating cancer in which the tumor is targeted by beams of radiation originating from the patient’s exterior. The two main particles employed for EBRT are photons and protons, with electrons and carbon ions also being in use. Both photons and protons are capable of achieving adequate tumor coverage, but protons can theoretically achieve lower doses in the surrounding tissues (at the expense of increased economical costs). Regardless of the chosen modality, the radiotherapy (RT) workflow is similar. It consists of determining the patient anatomy via imaging, usually via computed tomography (CT) scans, contouring (delineating) the organs at risk (OARs) and the target, creating a treatment plan, performing quality assurance (QA) and delivering the plan safely. In classical (also called non-adaptive) RT this workflow is performed once and the treatment is delivered over several (around 30) daily sessions (also called fractions).

Theoretically, the best radiotherapy treatment is the one in which the tumor is completely eradicated, while the surrounding tissue is not irradiated at all. Given that this is physically impossible, due to the nature of photon and proton propagation and interaction with matter, the next best result is maximal tumor coverage and minimal radiation damage to OARs. As the patient anatomy changes on different time scales ranging from weeks (e.g., weight loss, tumor shrinkage) to days (e.g., day to day variations of cavity fillings or neck pose changes) to seconds (due to for example breathing and slight movements) it becomes apparent that the offline approach to RT is suboptimal. To improve on this, the radiotherapy workflow must be adjusted such that imaging, delineation and treatment planning are performed several times over the course of the treatment, resulting in adaptive radiotherapy (ART). ART results in better targeting of the tumor and lower OAR doses. If adaptation is performed without the patient on the treatment table, the process is called offline adaptation. The next time-scale is online, which refers to a daily adaptation regime where the patient remains online (on the treatment table) after imaging. In such a workflow, on a given day the patient is imaged and within a short time (from tens of seconds to several minutes) the complete offline workflow (contouring, treatment planning, quality assurance, safe delivery) is performed. The time between imaging and delivery should be as short as possible, in order to minimize inter-fractional and patient set-up errors and to maximize clinical output. The ideal scenario would be real-time adaptation, in which all the steps of the radiotherapy workflow (including imaging and irradiation adaptations) are performed in real-time…

Objective. To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients. Approach. A probabilistic daily anatomy model (DAM) for head and neck patients DAM (DAMHN) is built on the variational autoencoder architecture. The model approximates the generative joint conditional probability distribution of the repeat computed tomography (rCT) images and their corresponding masks on the planning CT images (pCT) and their masks. The model outputs deformation vector fields, which are used to produce possible rCTs and associated masks. The dataset is composed of 93 patients (i.e. 315 pCT–rCT pairs), 9 (i.e. 27 pairs) of which were set aside for final testing. The performance of the model is assessed based on the reconstruction accuracy and the generative performance for the set aside patients. Main results. The model achieves a DICE score of 0.83 and an image similarity score normalized cross-correlation of 0.60 on the test set. The generated parotid glands, spinal cord and constrictor muscle volume change distributions and center of mass shift distributions were also assessed. For all organs, the medians of the distributions are close to the true ones, and the distributions are broad enough to encompass the real observed changes. Moreover, the generated images display anatomical changes in line with the literature reported ones, such as the medial shifts of the parotids glands. Significance. DAMHN is capable of generating realistic anatomies observed during the course of the treatment and has applications in anatomical robust optimization, treatment planning based on plan library approaches and robustness evaluation against inter-fractional changes.

Objective. To assess the viability of a physics-based, deterministic and adjoint-capable algorithm for performing treatment planning system independent dose calculations and for computing dosimetric differences caused by anatomical changes. Approach. A semi-numerical approach is employed to solve two partial differential equations for the proton phase-space density which determines the deposited dose. Lateral hetereogeneities are accounted for by an optimized (Gaussian) beam splitting scheme. Adjoint theory is applied to approximate the change in the deposited dose caused by a new underlying patient anatomy. Main results. The dose engine’s accuracy was benchmarked through three-dimensional gamma index comparisons against Monte Carlo simulations done in TOPAS. For a lung test case, the worst passing rate with (1 mm, 1%, 10% dose cut-off) criteria is 94.55%. The effect of delivering treatment plans on repeat CTs was also tested. For non-robustly optimized plans the adjoint component was accurate to 5.7% while for a robustly optimized plan it was accurate to 4.8%. Significance. Yet anOther Dose Algorithm is capable of accurate dose computations in both single and multi spot irradiations when compared to TOPAS. Moreover, it is able to compute dosimetric differences due to anatomical changes with small to moderate errors thereby facilitating its use for patient-specific quality assurance in online adaptive proton therapy.

In this paper we propose a solution to the need for a fast particle transport algorithm in Online Adaptive Proton Therapy capable of cheaply, but accurately computing the changes in patient dose metrics as a result of changes in the system parameters. We obtain the proton phase-space density through the product of the numerical solution to the one-dimensional Fokker-Planck equation and the analytical solution to the Fermi-Eyges equation. Moreover, a corresponding adjoint system was derived and solved for the adjoint flux. The proton phase-space density together with the adjoint flux and the metric (chosen as the energy deposited by the beam in a variable region of interest) allowed assessing the accuracy of our algorithm to different perturbation ranges in the system parameters and regions of interest. The algorithm achieved negligible errors ((Formula presented.)) for small Hounsfield unit (HU) perturbation ranges (–40 HU to 40 HU) and small to moderate errors (3% to 17%)–in line with the well-known limitation of adjoint approaches–for large perturbation ranges (–400 HU to 400 HU) in the case of most clinical interest where the region of interest surrounds the Bragg peak. Given these results coupled with the capability of further improving the timing performance it can be concluded that our algorithm presents a viable solution for the specific purpose of Online Adaptive Proton Therapy.