Numerical and deep learning algorithms for automated quality assurance in proton therapy

Abstract

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…