Worst case optimization: a method to account for uncertainties in the optimization of intensity modulated proton therapy

Pflugfelder, Daniel; Wilkens, Jan J.; Oelfke, Uwe

doi:10.1088/0031-9155/53/6/013

Cited by 253 publications

(285 citation statements)

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“…Because we did not use field specific PTVs, it was not possible to include range uncertainties in this study. Instead of creating field specific PTVs to include range uncertainties, a solution was proposed to include uncertainties in the optimization process itself [Pflugfelder et al, 2008, Unkelbach et al, 2009, Fredriksson et al, 2011, Chen et al, 2012. Chen et al have implemented a robust optimization in a multi-criteria optimization as well [Chen et al, 2012].…”

Section: Summary and Discussionmentioning

confidence: 99%

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

et al. 2016

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Stereotactic body image guided radiation therapy (SBRT) shows excellent results for the local control of early stage lung cancer. However, not all patients are eligible for SBRT, and advanced stage treatment is less successful and often associated with severe side effects. Scanned carbon ion therapy (PT) can deliver more conformal dose likely benefiting these patient groups.Therefore an in silico trial was conducted on early and advanced stage patients to identify potential advantages of PT. The patients were treated with SBRT at Champalimaud Center for the Unknown, Lisbon (Portugal). PT plans were simulated on 4DCTs, and rescanning was investigated for motion mitigation in 4D-dose calculations. A dedicated strategy for 4D intensity modulated particle therapy (IMPT) was developed and applied for advanced stage patients with multiple lesions. For clinically valid and reliable results the deformable image registrationnecessary for 4D-dose calculation -a quality assurance tool was developed and applied in the study.The results showed that target coverage was comparable in SBRT and PT, while PT delivered significantly lower doses to most critical structures, especially the heart, lungs, and esophagus.A highly complex case of advanced stage lung cancer could be treated in a single fraction of 24 Gy with PT, while SBRT could not deliver the full ablative dose treatment due to an excessive heart dose. The mean heart dose was reduced from 10 Gy to 0.8 Gy with PT for this specific patient.

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Section: Summary and Discussionmentioning

confidence: 99%

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

et al. 2016

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“…An alternative to using margins is being developed by incorporating errors directly into the optimisation algorithm, as proposed by Unkelbach et al [54] and Pflugfelder et al [55]. This can be implemented because in IMPT there exist many solutions which are all dosimetrically equivalent.…”

Section: Optimisation Functions For Robust Proton Planningmentioning

confidence: 99%

“…This can be implemented because in IMPT there exist many solutions which are all dosimetrically equivalent. This ''degeneracy'' of solutions can be used to reduce the sensitivity of the plan to uncertainties if they are incorporated into the optimisation process [55].…”

Section: Optimisation Functions For Robust Proton Planningmentioning

confidence: 99%

“…The latter optimised the worst case that could have occurred. The optimisation used by the Deutsches Krebsforschungszentrum (Heidelberg, Germany) inhouse TPSs for particle therapy (KonRad, Heidelberg, Germany), is the worst-case dose distribution approach [55]. The worst-case dose distribution was introduced by Lomax et al [56] and is a method of combining multiple dose distributions into a single one.…”

Section: Optimisation Functions For Robust Proton Planningmentioning

confidence: 99%

“…Pflugfelder et al [55] applied the worst-case optimisation method to real patient data where the target surrounded the spinal cord. Uncertainties of the beamlet ranges of ¡5 mm were considered.…”

Section: Optimisation Functions For Robust Proton Planningmentioning

confidence: 99%

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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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Optimization Models for Cancer Treatment Planning

Cao

Lim

2011

Wiley Encyclopedia of Operations Research and Management Science

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Operations research techniques have been significantly utilized in the practice of cancer treatment planning in order to deliver more efficient and precise treatments to patients. This article is designed to introduce recent advances in the treatment planning optimization for external radiation therapy, internal radiation therapy (brachytherapy), and chemotherapy. The main focus will be on gamma knife radiosurgery, conventional three‐dimensional radiotherapy (3DCRT), intensity‐modulated radiation therapy (IMRT), and proton therapy. Problem formulations and optimization models for each treatment type are discussed.

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Worst case optimization: a method to account for uncertainties in the optimization of intensity modulated proton therapy

Cited by 253 publications

References 13 publications

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

Treatment planning optimisation in proton therapy

Optimization Models for Cancer Treatment Planning

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