Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning

Engberg, Lovisa; Forsgren, Anders; Eriksson, Kjell; Hårdemark, Björn

doi:10.1002/mp.12146

Cited by 17 publications

(25 citation statements)

References 26 publications

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“…Alternative convex methods for allowing fractions of a structure to exceed a dose threshold exist, such as the conditional value at risk (CVaR) formulation . While nonconvex DVH criteria have been able to produce high quality treatment plans, recent work by Engberg et al . showed that in a multicriteria optimization context, abandoning explicit DVH criteria for CVaR constraints yielded better DVH curves for two cases with three CVaR constraints each.…”

Section: Discussionmentioning

confidence: 99%

On mixed electron–photon radiation therapy optimization using the column generation approach

2017

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Purpose Despite considerable increase in the number of degrees of freedom handled by recent radiotherapy optimisation algorithms, treatments are still typically delivered using a single modality. Column generation is an iterative method for solving large optimisation problems. It is well suited for mixed‐modality (e.g., photon–electron) optimisation as the aperture shaping and modality selection problem can be solved rapidly, and the performance of the algorithm scales favourably with increasing degrees of freedom. We demonstrate that the column generation method applied to mixed photon–electron planning can efficiently generate treatment plans and investigate its behaviour under different aperture addition schemes. Materials and methods Column generation was applied to the problem of mixed‐modality treatment planning for a chest wall case and a leg sarcoma case. 6 MV beamlets (100 cm SAD) were generated for the photon components along with 5 energies for electron beamlets (6, 9, 12, 16 and 20 MeV), simulated as shortened‐SAD (80 cm) beams collimated with a photon MLC. For the chest wall case, IMRT‐only, modulated electron radiation therapy (MERT)‐only, and mixed electron–photon (MBRT) treatment plans were created using the same planning criteria. For the sarcoma case, MBRT and MERT plans were created to study the behaviour of the algorithm under two different sets of planning criteria designed to favour specific modalities. Finally, the efficiency and plan quality of four different aperture addition schemes was analysed by creating chest wall MBRT treatment plans which incorporate more than a single aperture per iteration of the column generation loop based on a heuristic aperture ranking scheme. Results MBRT plans produced superior target coverage and homogeneity relative to IMRT and MERT plans created using the same optimisation criteria, all the while preserving the normal tissue‐sparing advantages of electron therapy. Adjusting the planning criteria to favour a specific modality in the sarcoma case resulted in the algorithm correctly emphasizing the appropriate modality. As expected, adding a single aperture per iteration yielded the lowest (best) cost function value per aperture included in the treatment plan. However, a greedier scheme was able to converge to approximately the same cost function after 125 apertures in one third of the running time. Electron apertures were on average 50–100% larger than photon apertures for all aperture addition schemes. The distribution of intensities among the available modalities followed a similar trend for all schemes, with the dominant modalities being 6 MV photons along with 6, 9 and 20 MeV electrons. Conclusion The column generation method applied to mixed modality treatment planning was able to produce clinically realistic treatment plans and combined the advantages of photon and electron radiotherapy. The running time of the algorithm depended heavily on the choice of mixing scheme. Adding the highest ranked aperture for each modality provided the best trade‐off b...

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Section: Discussionmentioning

confidence: 99%

On mixed electron–photon radiation therapy optimization using the column generation approach

2017

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“…Note that this formulation of DVH penalty functions is nonconvex and therefore the algorithm is not guaranteed to converge to a global minimum. Convex formulations of partial volume constraints exist and have been shown to be superior in a multicriteria optimization setting . We nonetheless chose to use the nonconvex formulation to keep the planning process intuitive, and we expect the conclusions reached in this work to be unaffected by the choice of partial volume objective formulation.…”

Section: Methodsmentioning

confidence: 99%

Robust mixed electron–photon radiation therapy optimization

2019

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Purpose Mixed beam electron–photon radiation therapy (MBRT) is an emerging technique that has the potential to reduce dose to normal tissue while improving target coverage for cancer sites with superficial tumors. Advances in optimization algorithms and robotic linear accelerators have made the creation and delivery of complex MBRT plans realistic without the need for special additional collimators, devices, or resetup of the patient. However, no study has been performed on the robustness of MBRT dose distributions to patient setup errors. Intensity‐modulated delivery of other charged particles such as protons have been shown to require robust planning techniques to maintain adequate target coverage under positioning errors. We therefore assess the sensitivity of MBRT treatment plans to positioning uncertainties when created under the traditional planning target volume (PTV)‐based planning paradigm and present a novel optimization model for the creation of robust MBRT plans. Methods The column generation method was applied to robust MBRT treatment planning by deriving the pricing problem for stochastic and “worst case” minimax optimization models, two common formulations of robustness. Robust treatment plans were created for two patient cases representative of the cancer sites which stand to benefit from MBRT: soft tissue sarcoma (STS) irradiation and chest wall irradiation with deep‐seated internal mammary, axillary, and supraclavicular nodes (CW‐N). For both patient cases, beamlet dose distributions for electrons and photons were generated for positioning shifts in six directions, ±5mm(xfalse^,yfalse^,zfalse^) in addition to a nominal unshifted scenario, for a total of seven sets of beamlets. Robust plans were created by specifying dose coverage constraints to the clinical target volume (CTV), as opposed to the PTV. Comparisons were performed against traditional PTV‐based plans created with a single set of unshifted beamlets. Results The dose distributions of traditional PTV‐based MBRT plans showed significant degradation in target coverage homogeneity when patient positioning errors were considered. For both cancer sites, cold spots below 95% and hot spots above 108% of the prescription dose appeared within the CTV when shifting the patient by 5 mm, corresponding to the margin added to the CTV to form the PTV. In contrast, CTV‐based robust plans created with the new optimization model maintained target coverage within the 95%–108% limits, for all positioning errors. Conclusion The quality of MBRT treatment plans created using a traditional PTV‐based optimization model was highly sensitive to patient positioning errors. For both patient cases, positioning errors resulted in perturbations to the nominal dose distributions which would have rendered PTV‐based plans clinically unacceptable. In contrast, CTV‐based robust plans were able to maintain adequate target coverage under all positioning error scenarios considered. We therefore conclude that to ensure the fidelity of the dose distribution delivered to the pat...

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“…were the first to formulate a model for dose planning with CVaR constraints; a recent study in IMRT with CVaR constraints can be found in Ref. . An optimization model for HDR BT with CVaR constraints is proposed in Ref.…”

Section: Methodsmentioning

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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Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning

Cited by 17 publications

References 26 publications

On mixed electron–photon radiation therapy optimization using the column generation approach

On mixed electron–photon radiation therapy optimization using the column generation approach

Robust mixed electron–photon radiation therapy optimization

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

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