A new concept for interactive radiotherapy planning with multicriteria optimization: First clinical evaluation

Thieke, Christian; Küfer, Karl‐Heinz; Monz, Michael; Scherrer, Alexander; Alonso, Fernando; Oelfke, Uwe; Huber, Peter E.; Debus, Jürgen; Bortfeld, Thomas

doi:10.1016/j.radonc.2007.06.020

Cited by 121 publications

(99 citation statements)

References 16 publications

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“…choose the final treatment plan, after analyzing the isodose maps (figure 2), DVHs (figure 3), graphical information (figure 4) and numerical information (table 1 and figure 4). Similar ideas for presenting information for decision makers have been presented also in Küfer et al (2003), Thieke et al (2007), Craft et al (2007), Monz et al (2008), Ehrgott and Winz (2008), for example. Compared to the trial-and-error method of finding appropriate weights or methods demanding a large database of Pareto optimal solutions, our approach makes treatment planning times shorter, and a good trade-off between the objectives can be found to improve the treatment plan's quality.…”

Section: Comparison and Discussionmentioning

confidence: 92%

Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

Ruotsalainen¹,

Miettinen²,

Palmgren³

et al. 2010

Phys. Med. Biol.

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In this paper, we present an anatomy-based three-dimensional dose optimization approach for HDR brachytherapy using interactive multiobjective optimization (IMOO). In brachytherapy, the goals are to irradiate a tumor without causing damage to healthy tissue. These goals are often conflicting, i.e. when one target is optimized the other will suffer, and the solution is a compromise between them. IMOO is capable of handling multiple and strongly conflicting objectives in a convenient way. With the IMOO approach, a treatment planner's knowledge is used to direct the optimization process. Thus, the weaknesses of widely used optimization techniques (e.g. defining weights, computational burden and trial-and-error planning) can be avoided, planning times can be shortened and the number of solutions to be calculated is small. Further, plan quality can be improved by finding advantageous trade-offs between the solutions. In addition, our approach offers an easy way to navigate among the obtained Pareto optimal solutions (i.e. different treatment plans). When considering a simulation model of clinical 3D HDR brachytherapy, the number of variables is significantly smaller compared to IMRT, for example. Thus, when solving the model, the CPU time is relatively short. This makes it possible to exploit IMOO to solve a 3D HDR brachytherapy optimization problem. To demonstrate the advantages of IMOO, two clinical examples of optimizing a gynecologic cervix cancer treatment plan are presented.

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Section: Comparison and Discussionmentioning

confidence: 92%

Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

Ruotsalainen¹,

Miettinen²,

Palmgren³

et al. 2010

Phys. Med. Biol.

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“…In current inverse planning systems for IMRT the dose distribution is determined by a computerised optimisation based on dose prescriptions for targets and other volumes which have been assigned an importance level [58]. To determine the plan quality, a number is assigned based on the deviation from prescription dose in each volume and the optimisation result is the plan with the lowest number.…”

Section: Optimisation Functions For Robust Proton Planningmentioning

confidence: 99%

“…There also exists a problem that, if upper and lower constraints are met, the optimisation process will not further improve doses to these volumes. This means that IMRT and IMPT cannot be exploited to their full potential owing to limitations with inverse planning [58]. The concept of multi-objective Pareto optimisation (often known as MCO) has been introduced into radiotherapy treatment planning to overcome these problems [59].…”

Section: Optimisation Functions For Robust Proton Planningmentioning

confidence: 99%

Treatment planning optimisation in proton therapy

McGowan¹,

Burnet²,

Lomax³

2013

BJR

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ABSTRACT. The goal of radiotherapy is to achieve uniform target coverage while sparing normal tissue. In proton therapy, the same sources of geometric uncertainty are present as in conventional radiotherapy. However, an important and fundamental difference in proton therapy is that protons have a finite range, highly dependent on the electron density of the material they are traversing, resulting in a steep dose gradient at the distal edge of the Bragg peak. Therefore, an accurate knowledge of the sources and magnitudes of the uncertainties affecting the proton range is essential for producing plans which are robust to these uncertainties. This review describes the current knowledge of the geometric uncertainties and discusses their impact on proton dose plans. The need for patient-specific validation is essential and in cases of complex intensity-modulated proton therapy plans the use of a planning target volume (PTV) may fail to ensure coverage of the target. In cases where a PTV cannot be used, other methods of quantifying plan quality have been investigated. A promising option is to incorporate uncertainties directly into the optimisation algorithm. A further development is the inclusion of robustness into a multicriteria optimisation framework, allowing a multi-objective Pareto optimisation function to balance robustness and conformity. The question remains as to whether adaptive therapy can become an integral part of a proton therapy, to allow re-optimisation during the course of a patient's treatment. The challenge of ensuring that plans are robust to range uncertainties in proton therapy remains, although these methods can provide practical solutions. The ability to create and deliver the ideal treatment plan, where the target volume receives 100% of the prescribed dose and normal tissue receives 0%, is the holy grail of radiation therapy [1]. It is, however, impossible to achieve this perfect balance. Instead, multiple trade-offs are required to achieve a clinically acceptable plan, so the problem becomes one of optimisation. There are many factors that can affect how ''optimised'' a patient's treatment can be. This review focuses on the challenges of proton therapy plan optimisation, particularly in regard to range uncertainties, and how to incorporate them into the plan evaluation and verification process.The nature of proton therapy makes the aim of cure without complications potentially more achievable, owing to the highly localised deposition of dose in the characteristic Bragg peak [2]. This relates predominantly to the ability to deliver high doses of radiation close to normal tissue structures, which would be dose limiting in conventional X-ray treatments, and to the finite range of protons, which results in a reduced integral dose to surrounding normal tissues.From a clinical perspective, the exact role of proton therapy has yet to be defined. However, for childhood cancers, proton therapy delivers a lower dose to tissues around the tumour than X-rays, resulting in less growth disturbance and l...

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“…After deciding what beam angles should be used, a patient will be treated using an optimal plan obtained by solving the fluence map (or intensity) optimization problem -the problem of determining the optimal beamlet weights for the fixed beam angles. Many mathematical optimization models and algorithms have been proposed for the intensity problem, including linear models [3,4], mixed integer linear models [5,6], nonlinear models [7,8], and multiobjective models [9,10]. After an acceptable set of fluence maps is produced, one must find a suitable way for delivery (realization problem).…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Programming Approach to Fluence Map Optimization for Intensity-Modulated Radiation Therapy Treatment Planning

et al. 2015

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Abstract-The fluence map optimization (FMO) problem is one of the most studied problems in intensity-modulated radiation therapy treatment planning. Although many approaches have shown to yield good solutions to the FMO problem, the optimal solutions obtained ensure that the resulting treatment is the best possible with respect to the weighting parameters of the formulation used. Since the 'optimal' weighting scheme is unknown, the choice of the weight parameters is typically a long trial-and-error process until a satisfactory solution is achieved. Moreover, for selecting the best irradiating directions, it is not clear how traditional trial-and-error parameter tuning should be incorporated or managed. A two-stage programming approach is proposed to reduce the dependency of the optimal solutions on the weight parameters and simultaneously improve the overall plan quality. This approach is yet another step towards automated generation of treatment plans which will result in breakthrough developments in radiation therapy care.

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A new concept for interactive radiotherapy planning with multicriteria optimization: First clinical evaluation

Cited by 121 publications

References 16 publications

Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy

Treatment planning optimisation in proton therapy

A Two-Stage Programming Approach to Fluence Map Optimization for Intensity-Modulated Radiation Therapy Treatment Planning

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