Is Proton Beam Therapy More Effective than Intensity-Modulated Radiotherapy in Prostate Cancer Treatment?

Prostate cancer is the most common form of cancer found in American males. Breaking technological advances in prostate cancer treatment continue to develop to help fight this disease, one such is proton beam therapy. Proton beam therapy is theorized to spare even more healthy tissue than photon radiotherapy because it delivers a majority of its radiation during the Bragg peak. Since this technology is substantially costlier than any other form of radiation therapy, physicians are assessing its effectiveness and determining if it is worth the cost. Currently, there is no significant difference seen in patient quality of life between recipients of proton or photon therapy. This can possibly because of secondary neutrons that are generated when protons exit the nozzle. Pencil beam scanning, a recent advancement in proton therapy delivery, is theorized to make protons have much better treatment outcomes than photons and would eliminate the issue of secondary neutrons. More studies need to be conducted to determine if pencil beam scanning ensure better quality of life over photon therap

Optimization Problems in Radiation Therapy Treatment Planning.

Radiation therapy is one of the most common methods used to treat many types of cancer. External beam radiation therapy and the models associated with developing a treatment plan for a patient are studied. External beams of radiation are used to deliver a highly complex so-called dose distribution to a patient that is designed to kill the cancer cells while sparing healthy organs and normal tissue. Treatment planning models and optimization are used to determine the delivery machine instructions necessary to produce a desirable dose distribution. These instructions make up a treatment plan. This thesis studies four problems in radiation therapy treatment plan optimization. First, treatment planners generate a plan with a number of competing treatment plan criteria. The relationship between criteria is not known a priori. A methodology is developed for physicians and treatment planners to efficiently navigate a clinically relevant region of the Pareto frontier generated by trading off these different criteria in an informed way. Second, the machine instructions for intensity modulated radiation therapy, a common treatment modality, consist of the locations of the external beams and the non-uniform intensity profiles delivered from each of these locations. These decisions are traditionally made with separate, sequential models. These decisions are integrated into a single model and propose a heuristic solution methodology. Third, volumetric modulated arc therapy (VMAT), a treatment modality where the beam travels in a coplanar arc around the patient while continuously delivering radiation, is a popular topic among optimizers studying treatment planning due to the difficult nature of the problem and the lack of a universally accepted treatment planning method. While current solution methodologies assume a predetermined coplanar path around the patient, that assumption is relaxed and the generation of a non-coplanar path is integrated into a VMAT planning algorithm. Fourth, not all patient information is available when developing a treatment plan pre-treatment. Some information, like a patient's sensitivity to radiation, can be realized during treatment through physiological tests. Methodologies of pre-treatment planning considering adaptation to new information are studied.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113366/1/troylong_1.pd

A hybrid approach to beam angle optimization in intensity-modulated radiation therapy

Intensity-Modulated Radiation Therapy is the technique of delivering radiation to cancer patients by using non-uniform radiation fields from selected angles, with the aim of reducing the intensity of the beams that go through critical structures while reaching the dose prescription in the target volume. Two decisions are of fundamental importance: to select the beam angles and to compute the intensity of the beams used to deliver the radiation to the patient. Often, these two decisions are made separately: first, the treatment planners, on the basis of experience and intuition, decide the orientation of the beams and then the intensities of the beams are optimized by using an automated software tool. Automatic beam angle selection (also known as Beam Angle Optimization) is an important problem and is today often based on human experience. In this context, we face the problem of optimizing both the decisions, developing an algorithm which automatically selects the beam angles and computes the beam intensities. We propose a hybrid heuristic method, which combines a simulated annealing procedure with the knowledge of the gradient. Gradient information is used to quickly find a local minimum, while simulated annealing allows to search for global minima. As an integral part of this procedure, the beam intensities are optimized by solving a Linear Programming model. The proposed method presents a main difference from previous works: it does not require to have on input a set of candidate beam angles. Indeed, it dynamically explores angles and the only discretization that is necessary is due to the maximum accuracy that can be achieved by the linear accelerator machine. Experimental results are performed on phantom and real-life case studies, showing the advantages that come from our approach

Desenvolvimento e avaliação de matheurísticas para o combinado problema do posicionamento dos feixes e distribuição de dose no planejamento de radioterapia

Orientador : Neida Maria Patias VolpiTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 08/07/2016Inclui referências : f. 71-75Área de concentraçãoResumo: O processo de planejamento de radioterapia é um fator essencial para garantir o nível máximo de eficiência do tratamento subsequente. Neste planejamento, há pelo menos dois problemas de decisão que podem ser modelados e resolvidos utilizando técnicas de Pesquisa Operacional. Estes incluem a melhor posição para emissão do feixe (problema do posicionamento dos feixes) e a quantidade ótima da dose que deve ser entregue através de cada feixe (problema da distribuição de dose). Esta tese apresenta um modelo matemático para otimizar concomitantemente os problemas do posicionamento dos feixes e da distribuição de dose, na presença de múltiplos objetivos. Três matheurísticas são propostas para resolver casos realistas que são de grande escala. As matheurísticas usam, respectivamente, Algoritmos Genéticos, Busca Tabu e Busca em Vizinhança Variável e são, portanto, denominadas GArad, TSrad e VNSrad. O desempenho das metodologias propostas é avaliado em dois tipos de instâncias de câncer na região da próstata, que envolvem um único corte de tomografia computadorizada (CT) e um conjunto de cortes de CT (problema 3D). Para o problema em um único corte de CT, os resultados das matheurísticas propostas são comparados com a solução ótima obtida por método exato. Em ambas instâncias, avaliaram-se os resultados em relação à cobertura de dose no tumor, e aos limites percentuais de dose nos órgãos de risco, além de avaliar a performance das metodologias em diferentes tempos computacionais. No geral, as metodologias fornecem uma solução para os problemas do posicionamento dos feixes e distribuição de dose, e, além disso, são metodologias flexíveis para considerar as necessidades específicas do paciente. Palavras-chaves: Saúde; Radioterapia; Otimização; Matheurística; Algoritmo Genético; Busca Tabu; Busca em Vizinhança Variável.Abstract: Radiotherapy planning is a vital component in ensuring the maximum level of effectiveness of the subsequent treatment. In the planning task, there are at least two connected decision problems that can be modelled and solved using Operational Research techniques. These include the best position of the radiotherapy machine (beam angle problem) and the optimal quantity of the dose that has to be delivered through each beam (dose distribution problem). This thesis presents a mathematical optimisation model for solving the combined beam angle and dose distribution problem in the presence of multiple objectives. Three matheuristics are developed to solve realistic large-scale instances. The matheuristics use Genetic Algorithms, Tabu Search and Variable Neighbourhood Search and are hence termed GArad, TSrad and VNSrad, respectively. The performance of the proposed methods is assessed on two prostate cancer instances, namely a single computed tomography (CT) slice and a set of CT slices (3D problem). For the single-slice problem, the results of the proposed matheuristics are compared to the optimal solutions obtained by an exact method where the experiments show that the proposed methods are able to achieve optimality or to produce a relatively small deviation. For the multi-slice problem, the computational experiments show that the proposed methods produce viable solutions which can be attained in a reasonable computational time. Overall, the methodologies can provide a solution for beam angle and dose distribution problems, and besides that they are flexible to consider the patient needs. Key-words: Healthcare; Radiotherapy; Optimisation; Matheuristic; Genetic Algorithm; Tabu Search; Variable Neighbourhood Search

APPLYING OPERATIONS RESEARCH MODELS TO PROBLEMS IN HEALTH CARE

Intensity- modulated radiation therapy is a form of cancer treatment that directs high energy x-rays to irradiate a tumor volume. In order to minimize the damage to surround-ing tissue the radiation is delivered from multiple angles. The selection of angles is an NP-hard problem and is currently done manually in most hospitals. We use previously evaluated treatment plans to train a machine learning model to sort potential treatment plans. By sorting potential treatment plans we can find better solutions while only evalu-ating a fifth as many plans. We then construct a genetic algorithm and use our machine learning models to search the space of all potential treatment plans to suggest a potential best plan. Using the genetic algorithm we are able to find plans 2% better on average than the previously best known plans. Proton therapy is a new form of radiation therapy. We simulated a proton therapy treatment center in order to optimize patient throughput and minimize patient wait time. We are able to schedule patients reducing wait times between 20% and 35% depending on patient tardiness and absenteeism. Finally, we analyzed the impact of operations research on the treatment of pros-tate cancer. We reviewed the work that has been published in both operations research and medical journals, seeing how it has impacted policy and doctor recommendations