Technologies for Delivery of Proton and Ion Beams for Radiotherapy

Recent developments for the delivery of proton and ion beam therapy have been significant, and a number of technological solutions now exist for the creation and utilisation of these particles for the treatment of cancer. In this paper we review the historical development of particle accelerators used for external beam radiotherapy and discuss the more recent progress towards more capable and cost-effective sources of particles.Comment: 53 pages, 13 figures. Submitted to International Journal of Modern Physics

Spatiotemporal Fractionation in Radiotherapy

In current clinical practice, radiotherapy treatments are often fractionated, i.e. the total radiation dose is equally divided into small fractions to be delivered daily over a period of few days or weeks. It has recently been shown in silico that spatiotemporal fractionation schemes, i.e. delivering distinct dose distributions in different fractions, can potentially improve the treatment. This is possible if these dose distributions are designed such that different fractions deliver similar doses to normal tissues (i.e. exploit the fractionation effect), but each fraction delivers high single-fraction doses to alternating parts of the tumor (i.e. achieve partial hypofractionation in the tumor). Thereby, the ratio of biological dose in the tumor versus the normal tissue can be improved. In this project, we further developed this innovative and novel concept. In particular, we focused on: 1. Developing new treatment planning algorithms for spatiotemporal fractionation 2. Identifying potential clinical applications of spatiotemporal fractionation with the aim of bringing spatiotemporal fractionation towards the design and implementation of a phase I clinical trial. Spatiotemporal fractionation is associated with higher complexity in treatment planning and delivery. Different plans with distinct dose distributions for different fractions must be designed such that all fractions together deliver the prescribed biological dose to the tumor. To that end, novel mathematical optimization methods for treatment planning have been developed, which are based on the cumulative biological dose rather than the physical dose. In particular, we developed robust treatment planning methods to account for geometric uncertainty in the patient setup and biological uncertainty in the fractionation sensitivity, which may lead to a degradation of the resulting treatment if not accounted for. It was shown that spatiotemporally fractionated treatments can be obtained which are robust against setup errors and uncertainty in the fractionation sensitivity. At the same time, these robust plans maintain most of their dosimetric benefit over uniformly fractionated plans. Besides liver cancer patients and patients with large arteriovenous malformations, patients with multiple brain metastases were identified to be especially well suited for spatiotemporal fractionation, because of the high accuracy in patient positioning. For theses patients, delivering high doses to different metastases in different fractions allows for fractionation of the normal brain dose in between the metastases while increasing the biological dose within the metastases. In addition, novel extensions of spatiotemporal fractionation were investigated. Spatiotemporal fractionation has been combined with other degrees of freedom that can be exploited in fractionated radiotherapy treatments, i.e. the combination of different particle types and treatment techniques, and the use of different beam orientations in different fractions. We showed that in the context of combined proton-photon therapy, spatiotemporal fractionation can be used to determine the optimal dose contribution of the proton and photon fractions to the tumor, thereby improving on simple proportional combination of intensity modulated radiotherapy and intensity modulated proton therapy plans. Also, we demonstrated that the quality of spatiotemporally fractionated treatments can be boosted by selecting fraction-specific beam orientations that are beneficial to treat specific regions of the tumor. To that end, a treatment planning algorithm was developed that allows for simultaneous optimization of multiple non-coplanar arc treatments. Finally, the simultaneous optimization of multiple dose distributions based on the cumulative biological dose is not supported by any commercial treatment planning system. To this end, we implemented a method which allows to import treatment plans optimized using our in-house research treatment planning system into a commercial treatment planning system. Thereby, it is possible to deliver spatiotemporally fractionated treatments in the clinics

On Quality in Radiotherapy Treatment Plan Optimisation

Radiotherapy is one of the essential treatments used in the fight against cancer. The goal of radiotherapy is to deliver a high dose of ionising radiation to the tumour volume and at the same time minimise the effect on healthy tissue by reducing the radiation to critical organs. This contradiction is challenging and has been driving the research and development of the treatments.Over the last two decades, there has been tremendous technical development inradiotherapy. The rapid increase in computational power introduced treatment plan optimisation and intensity-modulated radiotherapy (IMRT). IMRT made it possible to shape the radiation dose distribution closely around the target volume avoiding critical organs to a greater extent. Rotational implementation of IMRT, e.g. Volumetric Modulated Arc Therapy (VMAT) further improved this “dose shaping” ability. With these techniques increasing the ability to produce better treatment plans, there was a need for evaluation tools to compare the treatment plan quality. A plan can be judged by how well it fulfils the prescription and dose-volume constraints, ideally based on treatment outcome. In this work, this is denoted Required Plan Quality, the minimum quality to accept a plan for clinical treatment. If a plan does not fulfil all the dose-volume constraints, there should be a clear priority of which constraints are crucial to achieve. On the other hand, if the constraints are easily fulfilled, there might be a plan of better quality only limited by the treatment systems ability to find and deliver it. This is denoted Attainable Plan Quality in this work– the quality possible to achieve with a given treatment system for a specific patient group.In work described in this thesis, the so-called Pareto front method was used to search for the attainable plan quality to compare different treatment planning systems and optimisation strategies. More specifically, a fall-back planning system for backup planning and an optimiser to find the best possible beam angles. The Pareto method utilises a set of plans to explore the trade-off between target and nearby risk organs.The Pareto plan generation is time-consuming if done manually. The Pareto method was then used in a software that automated the plan generation allowing for a more accurate representation of the trade-off. The software was used to investigate the attainable plan quality for prostate cancer treatments. In the last two publications in this thesis, machine learning approaches were developed to predict a treatment plancloser to the attainable plan quality compared to a manually generated plan.In the thesis, tools have been developed to help move the treatment plan qualityfrom Required Plan Quality towards the Attainable Plan Quality, i.e. the best quality we can achieve with our current system

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works