An Hybrid Local Search for the Direct Aperture Optimisation Problem

Radiotherapy is a cancer treatment that uses high levels of radiation to destroy cancerous cells and shrink tumours while minimising harm to surrounding organs at risk (OARs). One of the techniques used in radiotherapy is Intensity Modulated Radiation Therapy (IMRT). Usually, the IMRT problem is approached sequentially, that is, we first need to determine the set of beam angles from which radiation will be delivered. Then, the radiation intensities for each selected beam angle are computed. Finally, the sequence of aperture shapes needed to deliver the computed treatment plan is generated. Unfortunately, the treatment plans generated by this approach have many apertures, which leads to longer treatment times. In contrast, the Direct Aperture Optimisation (DAO) problem considers constraints associated with the number of deliverable aperture shapes and physical constraints of the machine during the optimisation process of the intensities. The DAO approach generates, in general, better treatments with fewer apertures for IMRT. This is important because fewer apertures usually means shorter delivery times. In this work, we propose an hybrid local search strategy with mathematical programming to efficiently solve the DAO problem. We apply our proposed local search algorithm to a set of prostate cases, obtaining very competitive results.

I. Introduction

Cancer is a significant cause of morbidity and mortality worldwide, producing millions of deaths a year and generating economic and social costs. According to GLOBOCAN, there were 19.3 million new cases of cancer, and almost 10 million deaths from cancer in 2020 [1]. This disease is the second cause of death in the world [2], and the prevalence of this has increased in the last four decades [3]. However, researchers' advances in this area have been reflected in the improvements in cancer survival rate [4], doubling in the same period [3].