High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing

Deist, Timo M.; Gorissen, Bram L.

doi:10.1088/0031-9155/61/3/1155

Cited by 35 publications

(57 citation statements)

References 31 publications

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“…Most prominent are the inverse planning by integer programming (IPIP) approach published by Siauw et al . and an adapted Simulated Annealing method by Gorissen et al . Those methods cannot guarantee to retrieve the global optimum.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy

et al. 2017

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Purpose: In this study, we introduce a novel, fast, inverse treatment planning strategy for interstitial high-dose-rate (HDR) brachytherapy with multiple regions of interest solely based on dose-volumehistogram-related dosimetric measures (DMs). Methods: We present a new problem formulation of the objective function that approximates the indicator variables of the standard DM optimization problem with a smooth logistic function. This problem is optimized by standard gradient-based methods. The proposed approach is then compared against state-of-the-art optimization strategies. Results: All generated plans fulfilled prescribed DMs for all organs at risk. Compared to clinical practice, a statistically significant improvement ðp ¼ 0:01Þ in coverage of target structures was achieved. Simultaneously, DMs representing high-dose regions were significantly reduced ðp ¼ 0:01Þ. The novel optimization strategies run-time was (0.8 AE 0.3) s and thus outperformed the best competing strategies of the state of the art. In addition, the novel DM-based approach was associated with a statistically significant ðp ¼ 0:01Þ increase in the number of active dwell positions and a decrease in the maximum dwell time. Conclusions:The generated plans showed a clinically significant increase in target coverage with fewer hot spots, with an optimization time approximately three orders of magnitude shorter than manual optimization currently used in clinical practice. As optimization is solely based on DMs, intuitive, interactive, real-time treatment planning, which motivated the adoption of manual optimization in our clinic, is possible.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…To achieve intuitive treatment planning, one possible strategy is an optimization model solely based on dose‐volume‐histogram (DVH)‐related dosimetric measures (DMs) . But the underlying optimization problem results in a mixed‐integer linear‐programming (MILP) problem that is computationally expensive to solve.…”

Section: Introductionmentioning

confidence: 99%

A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy

et al. 2017

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“…Constraints (1b) and (1c) are similar but for OAR. We consider the following DVM, which is adopted from Siauw et al (2011) and Deist and Gorissen (2016), but without the dwell time modulation restriction in the latter reference:…”

Section: Modelsmentioning

confidence: 99%

“…By using a binary indicator variable for each dose point, a mixed-integer model was obtained. Research indicates that this model cannot be solved to optimality for practical examples within a reasonable time limit, and, in order to find good solutions, heuristics are often used, as in Beliën et al (2009), Siauw et al (2011 and Deist and Gorissen (2016).…”

Section: Introductionmentioning

confidence: 99%

Mathematical optimization of high dose-rate brachytherapy—derivation of a linear penalty model from a dose-volume model

2018

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High dose-rate brachytherapy is a method for cancer treatment where the radiation source is placed within the body, inside or close to a tumour. For dose planning, mathematical optimization techniques are being used in practice and the most common approach is to use a linear model which penalizes deviations from specified dose limits for the tumour and for nearby organs. This linear penalty model is easy to solve, but its weakness lies in the poor correlation of its objective value and the dose-volume objectives that are used clinically to evaluate dose distributions. Furthermore, the model contains parameters that have no clear clinical interpretation. Another approach for dose planning is to solve mixed-integer optimization models with explicit dose-volume constraints which include parameters that directly correspond to dose-volume objectives, and which are therefore tangible. The two mentioned models take the overall goals for dose planning into account in fundamentally different ways. We show that there is, however, a mathematical relationship between them by deriving a linear penalty model from a dose-volume model. This relationship has not been established before and improves the understanding of the linear penalty model. In particular, the parameters of the linear penalty model can be interpreted as dual variables in the dose-volume model.

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“…The above‐mentioned weaknesses of current methods have resulted in a research focus on finding improved automated methods for dwell time optimization, capable of using the dosimetric indices (or an approximation thereof) as the actual quality measures for the optimization . While the advantage of using the dosimetric indices as quality measures is obvious (due to the clinical guidelines), it is important to be aware of the pitfalls and weaknesses of this approach, which can be severe if not foreseen.…”

Section: Introductionmentioning

confidence: 99%

An extended dose–volume model in high dose‐rate brachytherapy – Using mean‐tail‐dose to reduce tumor underdosage

2019

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PurposeHigh dose–rate brachytherapy is a method of radiotherapy for cancer treatment in which the radiation source is placed within the body. In addition to give a high enough dose to a tumor, it is also important to spare nearby healthy organs [organs at risk (OAR)]. Dose plans are commonly evaluated using the so‐called dosimetric indices; for the tumor, the portion of the structure that receives a sufficiently high dose is calculated, while for OAR it is instead the portion of the structure that receives a sufficiently low dose that is of interest. Models that include dosimetric indices are referred to as dose–volume models (DVMs) and have received much interest recently. Such models do not take the dose to the coldest (least irradiated) volume of the tumor into account, which is a distinct weakness since research indicates that the treatment effect can be largely impaired by tumor underdosage even to small volumes. Therefore, our aim is to extend a DVM to also consider the dose to the coldest volume.MethodsAn improved DVM for dose planning is proposed. In addition to optimizing with respect to dosimetric indices, this model also takes mean dose to the coldest volume of the tumor into account.ResultsOur extended model has been evaluated against a standard DVM in ten prostate geometries. Our results show that the dose to the coldest volume could be increased, while also computing times for the dose planning were improved.ConclusionWhile the proposed model yields dose plans similar to other models in most aspects, it fulfils its purpose of increasing the dose to cold tumor volumes. An additional benefit is shorter solution times, and especially for clinically relevant times (of minutes) we show major improvements in tumour dosimetric indices.

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High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing

Cited by 35 publications

References 31 publications

A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy

A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy

Mathematical optimization of high dose-rate brachytherapy—derivation of a linear penalty model from a dose-volume model

An extended dose–volume model in high dose‐rate brachytherapy – Using mean‐tail‐dose to reduce tumor underdosage

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