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| Home > Publications database > Accounting for range uncertainties in the optimization of combined proton-photon treatments via stochastic optimization. |
| Journal Article | DKFZ-2020-00965 |
; ; ;
2020
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.ijrobp.2020.04.029
Abstract: Proton treatment slots are still a limited resource. Combined proton-photon treatments, in which most fractions are delivered with photons and only a few with protons, may represent a practical solution to optimize the allocation of proton resources over the patient population. We demonstrate how a limited number of proton fractions can be optimally used in multi-modality treatments, also addressing the issue of the robustness of combined treatments against proton range uncertainties.Combined proton-photon treatments are planned by simultaneously optimizing intensity-modulated radiation therapy (IMRT) and proton therapy (IMPT) plans while accounting for the fractionation effect through the biologically effective dose (BED) model. The method is investigated for different tumor sites (a spinal metastasis, a sacral chordoma, and an atypical meningioma) in which organs at risk (OARs) are located within or near the tumor. Stochastic optimization is applied to mitigate range uncertainties.In optimal combinations, proton and photon fractions deliver similar doses to OARs overlaying the target volume to protect these dose-limiting normal tissues through fractionation. Meanwhile, parts of the tumor are hypofractionated with protons. Thus, the total dose delivered with photons is reduced compared to simple combinations where each modality delivers the prescribed dose per fraction to the target volume. The benefit of optimal combinations persists when range errors are accounted for via stochastic optimization.Limited proton resources are optimally used in combined treatments if parts of the tumor are hypofractionated with protons while near-uniform fractionation is maintained in serial OARs. Proton range uncertainties can be efficiently accounted for through stochastic optimization and are not an obstacle for clinical application.
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