Fast automated multi-criteria planning for HDR brachytherapy explored for prostate cancer

Breedveld, S.; Bennan, Amit Ben Antony; Aluwini, S.; Schaart, Dennis R.; Kolkman-Deurloo, I.K.K.; Heijmen, B.

doi:10.1088/1361-6560/ab44ff

Cited by 23 publications

(37 citation statements)

References 34 publications

(46 reference statements)

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“…[53,55,56]. Another approach to handle DVMs is to approximate the Heaviside step functions (corresponding to the binary indicator variables) with smooth nonlinear sigmoid functions, 6,35,[68][69][70]…”

Section: B3 Properties and Solutions Methodsmentioning

confidence: 99%

“…Also treatment plan quality can be improved with the use of optimization. 6,[31][32][33][34][35][36] In one study, 34 planners were presented with a manually constructed dose plan and five dose plans which were constructed with an optimization model. It turned out that they preferred an optimized dose plan in 53 out of 54 cases.…”

Section: B Why Use Optimization?mentioning

confidence: 99%

“…In the study 35 on prostate cancer they consider the radiobiological objective LTCP (logarithmic TCP), 96 which with the notation given in Tables II and VII, is formulated as…”

Section: E2 Tcp Modelsmentioning

confidence: 99%

“…160 In the framework of TROTS, a dataset with patients previously treated for prostate cancer with HDR BT has recently been proposed. 35 With advances in technology, related both to computing power and to algorithmic performance, there are several potential enhancements of the treatment planning. Increasing the number of dose points, using more advanced models, or solving MIP models to optimality are some of the possibilities.…”

Section: C State Of Researchmentioning

confidence: 99%

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Optimization in treatment planning of high dose‐rate brachytherapy — Review and analysis of mathematical models

2021

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Treatment planning in high dose-rate brachytherapy has traditionally been conducted with manual forward planning, but inverse planning is today increasingly used in clinical practice. There is a large variety of proposed optimization models and algorithms to model and solve the treatment planning problem. Two major parts of inverse treatment planning for which mathematical optimization can be used are the decisions about catheter placement and dwell time distributions. Both these problems as well as integrated approaches are included in this review. The proposed models include linear penalty models, dose-volume models, mean-tail dose models, quadratic penalty models, radiobiological models, and multiobjective models. The aim of this survey is twofold: (i) to give a broad overview over mathematical optimization models used for treatment planning of brachytherapy and (ii) to provide mathematical analyses and comparisons between models. New technologies for brachytherapy treatments and methods for treatment planning are also discussed. Of particular interest for future research is a thorough comparison between optimization models and algorithms on the same dataset, and clinical validation of proposed optimization approaches with respect to patient outcome.

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Section: B3 Properties and Solutions Methodsmentioning

confidence: 99%

Section: B Why Use Optimization?mentioning

confidence: 99%

“…In the study 35 on prostate cancer they consider the radiobiological objective LTCP (logarithmic TCP), 96 which with the notation given in Tables II and VII, is formulated as…”

Section: E2 Tcp Modelsmentioning

confidence: 99%

Section: C State Of Researchmentioning

confidence: 99%

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Optimization in treatment planning of high dose‐rate brachytherapy — Review and analysis of mathematical models

2021

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“…Optimization methods for dose planning have been reported to only take a few seconds, see for example [11,36], and most approaches do not require more than a few minutes to find acceptable dose plans. Secondly, several studies have shown improvements in dose plan quality compared to manual planning; see [11,36,37,38,39,40] for prostate cancer examples. Furthermore, the use of mathematical optimization also reduces the dependence of personnel skills and experience on treatment outcome.…”

Section: Why Use Optimization?mentioning

confidence: 99%

Treatment Planning of High Dose-Rate Brachytherapy - Mathematical Modelling and Optimization

Morén

2021

Linköping Studies in Science and Technology. Dissertations

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Cancer is a widespread class of diseases that each year affects millions of people. It is mostly treated with chemotherapy, surgery, radiation therapy, or combinations thereof. High doserate (HDR) brachytherapy (BT) is one modality of radiation therapy, which is used to treat for example prostate cancer and gynecologic cancer. In BT, catheters (i.e., hollow needles) or applicators are used to place a single, small, but highly radioactive source of ionizing radiation close to or within a tumour, at dwell positions. An emerging technique for HDR BT treatment is intensity modulated brachytherapy (IMBT), in which static or dynamic shields are used to further shape the dose distribution, by hindering the radiation in certain directions.The topic of this thesis is the application of mathematical optimization to model and solve the treatment planning problem. The treatment planning includes decisions on catheter placement, that is, how many catheters to use and where to place them, as well as decisions for dwell times. Our focus is on the latter decisions. The primary treatment goals are to give the tumour a sufficiently high radiation dose while limiting the dose to the surrounding healthy organs, to avoid severe side effects. Because these aims are typically in conflict, optimization models of the treatment planning problem are inherently multiobjective. Compared to manual treatment planning, there are several advantages of using mathematical optimization for treatment planning. First, the optimization of treatment plans requires less time, compared to the time-consuming manual planning. Secondly, treatment plan quality can be improved by using optimization models and algorithms. Finally, with the use of sophisticated optimization models and algorithms the requirements of experience and skill level for the planners are lower. The use of optimization for treatment planning of IMBT is especially important because the degrees of freedom are too many for manual planning.The contributions of this thesis include the study of properties of treatment planning models, suggestions for extensions and improvements of proposed models, and the development of new optimization models that take clinically relevant, but uncustomary aspects, into account in the treatment planning. A common theme is the modelling of constraints on dosimetric indices, each of which is a restriction on the portion of a volume that receives at least a specified dose, or on the lowest dose that is received by a portion of a volume. Modelling dosimetric indices explicitly yields mixed-integer programs which are computationally demanding to solve. We have therefore investigated approximations of dosimetric indices, for example using smooth non-linear functions or convex functions. Contributions of this thesis are also a literature review of proposed treatment planning models for HDR BT, including mathematical analyses and comparisons of models, and a study of treatment planning for IMBT, which shows how robust optimization can be used to mitigate the risks fro...

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Fully automated treatment planning for MLC‐based robotic radiotherapy

et al. 2021

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To propose and validate a fully automated multi-criterial treatment planning solution for a CyberKnife ® equipped with an InCise™ 2 multi-leaf collimator. Methods: The AUTO BAO plans are generated with fully-automated prioritized multi-criterial optimization (AUTO MCO) of pencil-beam fluence maps with integrated non-coplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been in-house developed. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil-beams, segment dose depositions, and final dose calculations are performed with a stand-alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. Results: AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (-49%)), segments (59.5/98.9 (-40%)), and delivery times (17.1/22.3 min. (-23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (-21%)) and had on average shorter delivery times (18.6/22.3 min. (-17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. Conclusions: A new, vendor-independent optimization workflow for fully automated generation of deliverable high-quality CyberKnife ® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times i Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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Fast automated multi-criteria planning for HDR brachytherapy explored for prostate cancer

Cited by 23 publications

References 34 publications

Optimization in treatment planning of high dose‐rate brachytherapy — Review and analysis of mathematical models

Optimization in treatment planning of high dose‐rate brachytherapy — Review and analysis of mathematical models

Treatment Planning of High Dose-Rate Brachytherapy - Mathematical Modelling and Optimization

Fully automated treatment planning for MLC‐based robotic radiotherapy

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