The determination of optimal treatment plans for Volumetric Modulated Arc Therapy (VMAT)

Dursun, Pınar; Taşkın, Z. Caner; Altınel, İ. Kuban

doi:10.1016/j.ejor.2018.06.023

Cited by 11 publications

(17 citation statements)

References 53 publications

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“…To address the solvability concerns, decomposition approaches could be developed to increase the efficiency of the solution procedures for the RDAO model. Column generation could be employed for aperture generation, as in Romeijn et al [2005], Men et al [2007] and Mahnam et al [2017], or Benders' decomposition could be used to create faster solution algorithms, as in Taşkın et al [2010] and Dursun et al [2019b]. These improvements would be critical, for handling larger and more complex treatment regions with multiple at-risk organs, as well as the integration of more sophisticated, and potentially non-linear constraints.…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation of this work concerns the CPG methodology, which exceeded expectations in terms of generating stand-alone plans, but was not powerful enough as a warm start to bring the more precise RDAO model to optimality, which was its intended purpose. Going forward, the CPG heuristic may generalize well to other applications, such as VMAT, when it is modeled as a sequence of DAO-style problems at a large number of control-points, as in Men et al [2010] and Dursun et al [2019b]. Alternatively, the CPG heuristic could be used in column generation planning tools, which often benefit from a strong warm starting point.…”

Section: Discussionmentioning

confidence: 99%

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Robust Direct Aperture Optimization for Radiation Therapy Treatment Planning

Ripsman,

Purdie,

Chan

et al. 2021

Preprint

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Intensity-modulated radiation therapy (IMRT) allows for the design of customized, highly-conformal treatments for cancer patients. Creating IMRT treatment plans, however, is a mathematically complex process, which is often tackled in multiple, simpler stages. This sequential approach typically separates radiation dose requirements from mechanical deliverability considerations, which may result in suboptimal treatment quality. For patient health to be considered paramount, holistic models must address these plan elements concurrently, eliminating quality loss between stages. This combined direct aperture optimization (DAO) approach is rarely paired with uncertainty mitigation techniques, such as robust optimization, due to the inherent complexity of both parts. This paper outlines a robust DAO (RDAO) model and discusses novel methodologies for efficiently integrating salient constraints. Since the highly-complex RDAO model is difficult to solve, an original candidate plan generation (CPG) heuristic is proposed. The CPG produces rapid, high-quality, feasible plans, which are immediately clinically viable, and can also be used to generate a feasible incumbent solution for warm starting the RDAO model. Computational results obtained using clinical patient datasets with motion uncertainty show the benefit of incorporating the CPG, both in terms of first incumbent solution and final output plan quality.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Robust Direct Aperture Optimization for Radiation Therapy Treatment Planning

Ripsman,

Purdie,

Chan

et al. 2021

Preprint

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“…Finally, Dursun et al. () extend the approach developed in Romeijn et al. () to volumetric modulated arc therapy, a technology where the radiation source rotates around the body.…”

Section: Medicinementioning

confidence: 99%

Conditional value‐at‐risk beyond finance: a survey

Filippi

Guastaroba

Speranza

2019

Int Trans Operational Res

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A large number of problems involve making decisions in an uncertain environment and, hence, with unknown outcomes. Optimization models aimed at controlling the trade‐off between risk and return in finance have been widely studied since the seminal work by Markowitz in 1952. In financial applications, shortfall or quantile risk measures are receiving ever‐increasing attention. Conditional value‐at‐risk (CVaR) is arguably the most popular of such measures. In the last decades, optimization models aimed at controlling risk have been applied to several application domains different from financial optimization. This survey provides an overview of the main contributions where CVaR is incorporated into an optimization approach and applied to a context different from financial engineering. The literature is classified following an application‐oriented perspective. The applications cover classical areas studied in operational research—such as supply chain management, scheduling, and networks—and less classical areas such as energy and medicine. For each area, concise paper excerpts are provided that convey the main ideas of the problems studied, and analyze how the CVaR has been used to cope with different sources of uncertainty. Finally, some open research directions are outlined.

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“…Another approach, the hypofractionated high dose intensity modulated radiotherapy (60 Gy, 20 fractions per 3 Gy), is equally successful and has been implemented recently. The main goal of the hypofractionation of prostate radiotherapy is to abbreviate unsolicited breaks between fractions, which might provide a better radiobiological effect for the overall effectiveness of treatment [ 4 ]. Recent studies have also shown that after hypofractionation of dose, the overall bowel and urinary condition, as well as sexual dysfunctions, are not significantly worse than conventional radiotherapy; therefore, the effects of hypofractionated radiotherapy have been studied recently [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…It allows a much more conformal treatment plan to be achieved with a high dose deposited into the tumor. Moreover, it provides one of the highest and effective coverage of the target [ 4 , 8 , 9 ]. Despite using advanced treatment techniques and dose calculation algorithms, it is still necessary for us to verify the appropriate position of the patient’s body [ 10 ].…”

Section: Introductionmentioning

confidence: 99%