Robust treatment planning for intensity modulated radiotherapy of prostate cancer based on coverage probabilities

Baum, Christoph; Alber, M.; Birkner, M.; Nüsslin, Fridtjof

doi:10.1016/j.radonc.2005.09.005

Cited by 82 publications

(75 citation statements)

References 29 publications

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“…The distribution-free nature of our model is a key component of its flexibility. Directly incorporating robustness into treatment planning is an active topic of research (Baum et al 2006). Our work is similar in spirit to another recent study on robust IMRT (Chu et al 2005).…”

Section: Discussionmentioning

confidence: 67%

A robust approach to IMRT optimization

2006

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Managing uncertainty is a major challenge in radiation therapy treatment planning, including uncertainty induced by intrafraction motion, which is particularly important for tumours in the thorax and abdomen. Common methods to account for motion are to introduce a margin or to convolve the static dose distribution with a motion probability density function. Unlike previous work in this area, our development does not assume that the patient breathes according to a fixed distribution, nor is the patient required to breathe the same way throughout the treatment. Despite this generality, we create a robust optimization framework starting from the convolution method that is robust to fluctuations in breathing motion, yet spares healthy tissue better than a margin solution. We describe how to generate the data for our model using breathing motion data and we test our model on a computer phantom using data from real patients. In our numerical results, the robust solution delivers approximately 38% less dose to the healthy tissue than the margin solution, while providing the same level of protection against breathing uncertainty.

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Section: Discussionmentioning

confidence: 67%

A robust approach to IMRT optimization

2006

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“…Using linear programming duality, they can formulate the optimization problem as a large linear program. Baum et al [3] use coverage probabilities for the target and tumor as penalties in the objective functions to derive a robust treatment. Unkelbach and Oelfke [58] discuss, from a mathematical and a physics perspective, the difference between using coverage probabilities and stochastic programming in IMRT optimization.…”

Section: Traffic Network Designmentioning

confidence: 99%

On the Robustness of Global Optima and Stationary Solutions to Stochastic Mathematical Programs with Equilibrium Constraints, Part 2: Applications

Cromvik

Patriksson

2009

J Optim Theory Appl

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We consider a stochastic mathematical program with equilibrium constraints (SMPEC), and show that under certain assumptions, global optima and stationary solutions are robust with respect to changes in the underlying probability distribution. In particular, the discretization scheme Sample Average Approximation, which is convergent for both global optima and stationary solutions, can be combined with the robustness result to motivate the use of SMPECs in practice.We also consider SMPECs with multiple objectives, and establish robustness of weakly Pareto optimal and stationary solutions.Two applications are presented, both principally and numerically, in order to exemplify the use of SMPECs: a classic traffic network design problem where travel costs are uncertain, and the optimization of a treatment plan in intensity modulated radiation therapy, where radiobiological parameters are uncertain.

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“…A number of authors have explored PWOF approaches, including Löf et al, 18 Baum et al, 19 Sir et al, 20 Yang et al, 21 and Witte et al 22 Löf et al 18 proposed a comprehensive adaptive control framework for fractionated radiotherapy that incorporated random setup uncertainties, and demonstrated the occurrence of horns in the resulting dose distributions. This work was pursued by Rehbinder et al, 23 who used fluence modulation to compensate for random errors and couch corrections for systematic errors.…”

Section: Introductionmentioning

confidence: 99%

Coverage optimized planning: Probabilistic treatment planning based on dose coverage histogram criteria

et al. 2010

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This work ͑i͒ proposes a probabilistic treatment planning framework, termed coverage optimized planning ͑COP͒, based on dose coverage histogram ͑DCH͒ criteria; ͑ii͒ describes a concrete proofof-concept implementation of COP within the PINNACLE treatment planning system; and ͑iii͒ for a set of 28 prostate anatomies, compares COP plans generated with this implementation to traditional PTV-based plans generated with planning criteria approximating those in the high dose arm of the Radiation Therapy Oncology Group 0126 protocol. Let D v denote the dose delivered to fractional volume v of a structure. In conventional intensity modulated radiation therapy planning, D v has a unique value derived from the static ͑planned͒ dose distribution. In the presence of geometric uncertainties ͑e.g., setup errors͒ D v assumes a range of values. The DCH is the complementary cumulative distribution function of D v . DCHs are similar to dose volume histograms ͑DVHs͒. Whereas a DVH plots volume v versus dose D, a DCH plots coverage probability Q versus D. For a given patient, Q is the probability ͑i.e., percentage of geometric uncertainties͒ for which the realized value of D v exceeds D. PTV-based treatment plans can be converted to COP plans by replacing DVH optimization criteria with corresponding DCH criteria. In this approach, PTVs and planning organ at risk volumes are discarded, and DCH criteria are instead applied directly to clinical target volumes ͑CTVs͒ or organs at risk ͑OARs͒. Plans are optimized using a similar strategy as for DVH criteria. The specific implementation is described. COP was found to produce better plans than standard PTV-based plans, in the following sense. While target OAR dose tradeoff curves were equivalent to those for PTV-based plans, COP plans were able to exploit slack in OAR doses, i.e., cases where OAR doses were below their optimization limits, to increase target coverage. Specifically, because COP plans were not constrained by a predefined PTV, they were able to provide wider dosimetric margins around the CTV, by pushing OAR doses up to, but not beyond, their optimization limits. COP plans demonstrated improved target coverage when averaged over all 28 prostate anatomies, indicating that the COP approach can provide benefits for many patients. However, the degree to which slack OAR doses can be exploited to increase target coverage will vary according to the individual patient anatomy. The proof-of-concept COP implementation investigated here utilized a probabilistic DCH criteria only for the CTV minimum dose criterion. All other optimization criteria were conventional DVH criteria. In a mature COP implementation, all optimization criteria will be DCH criteria, enabling direct planning control over probabilistic dose distributions. Further research is necessary to determine the benefits of COP planning, in terms of tumor control probability and/or normal tissue complication probabilities.

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Robust treatment planning for intensity modulated radiotherapy of prostate cancer based on coverage probabilities

Cited by 82 publications

References 29 publications

A robust approach to IMRT optimization

A robust approach to IMRT optimization

On the Robustness of Global Optima and Stationary Solutions to Stochastic Mathematical Programs with Equilibrium Constraints, Part 2: Applications

Coverage optimized planning: Probabilistic treatment planning based on dose coverage histogram criteria

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