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Towards an integral clinical proton dose prediction uncertainty by considering delineation variation

  • Nils Peters
    Affiliations
    OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany

    Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
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  • Ludvig P. Muren
    Affiliations
    Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark

    Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Open AccessPublished:March 05, 2022DOI:https://doi.org/10.1016/j.phro.2022.03.001
      Proton therapy is more susceptible to uncertainties of the beam placement compared to conventional radiotherapy due to the finite range of protons in the patient. To ensure target coverage, additional healthy tissue around the target volume is irradiated in clinical routine. Advancements in both patient imaging and treatment planning therefore aim to reduce these clinical safety margins. However, any reduction of the overall safety margin also reduces the possibility to compensate for uncertainty sources that are not explicitly considered in the uncertainty estimation and therefore comes with a risk of decreased target coverage [
      • Baumann M.
      • Krause M.
      • Overgaard J.
      • Debus J.
      • Bentzen S.M.
      • Daartz J.
      • et al.
      Radiation oncology in the era of precision medicine.
      ]. At the same time, an appropriate technique for combining and considering the different uncertainties in the treatment planning process is necessary as not to dilute technical advancements.
      Uncertainties in proton therapy planning and delivery originate from the whole treatment chain, starting from patient imaging and the subsequent target and organ-at-risk delineation, across to treatment planning, patient positioning and finally beam delivery, with each uncertainty factor in itself describing a combination of different uncertainties [
      • Paganetti H.
      Range uncertainties in proton therapy and the role of Monte Carlo simulations.
      ]. Several approaches to combine errors in radiotherapy have been investigated, such as the straightforward calculation of (weighted) sums where assumptions regarding the error distribution are implicitly included [
      • Paganetti H.
      Range uncertainties in proton therapy and the role of Monte Carlo simulations.
      ,
      • Yang M.
      • Zhu X.R.
      • Park P.C.
      • Titt U.
      • Mohan R.
      • Virshup G.
      • et al.
      Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration.
      ], or more complex approaches such as numerical error sampling, relying on probability density functions (PDFs) [
      • Bär E.
      • Lalonde A.
      • Royle G.
      • Lu H.-M.
      • Bouchard H.
      The potential of dual-energy CT to reduce proton beam range uncertainties.
      ,
      • Rizzoglio V.
      • Adelmann A.
      • Gerbershagen A.
      • Meer D.
      • Nesteruk K.P.
      • Schippers J.M.
      Uncertainty quantification analysis and optimization for proton therapy beam lines.
      ]. A comprehensive framework for combining Type A uncertainties (those obtained as standard deviation from repeated measurements) and Type B uncertainties (those based on scientific judgement by assigning PDFs) is given in the Guide to the expression of Uncertainty in Measurement [

      ISO. Guide to the Expression of Uncertainty in Measurement. Geneva, Switzerland: International Organization for Standardization; 2008.

      ], which has also been applied for uncertainty estimation in proton therapy [
      • Berthold J.
      • Khamfongkhruea C.
      • Petzoldt J.
      • Thiele J.
      • Hölscher T.
      • Wohlfahrt P.
      • et al.
      First-in-human validation of CT-based proton range prediction using prompt gamma imaging in prostate cancer treatments.
      ,
      • Peters N.
      • Wohlfahrt P.
      • Hofmann C.
      • Möhler C.
      • Menkel S.
      • Tschiche M.
      • et al.
      Reduction of clinical safety margins in proton therapy enabled by the clinical implementation of dual-energy CT for direct stopping-power prediction.
      ]. Resulting uncertainty factors regarding patient setup, range prediction and organ motion can furthermore be considered in treatment planning by examining dose distributions for different error scenarios [
      • Fredriksson A.
      • Forsgren A.
      • Hårdemark B.
      Minimax optimization for handling range and setup uncertainties in proton therapy.
      ,
      • Pflugfelder D.
      • Wilkens J.J.
      • Oelfke U.
      Worst case optimization: A method to account for uncertainties in the optimization of intensity modulated proton therapy.
      ,
      • Liu W.
      • Zhang X.
      • Li Y.
      • Mohan R.
      Robust optimization of intensity modulated proton therapy.
      ,
      • Unkelbach J.
      • Paganetti H.
      Robust proton treatment planning: Physical and biological optimization.
      ].
      A major uncertainty factor, considered only implicitly as part of the safety margin, is the target delineation. It depends not only on the available image information, which is ambiguous due to microscopical anatomical spread not being visible – typically considered by increasing the gross tumor volume by several millimeters, depending on the tumor site – but also on the level of training received by the clinician performing the delineation (the ‘observer’), leading to large inter-observer variation (IOV) depending on the skill level. With the risk of treatment-center-specific systematic deviations, e.g. due to different interpretation of clinical guidelines, this potentially makes delineation the weakest link in accurate proton treatment planning. This can only be intercepted by (large) safety margins [
      • Njeh C.F.
      Tumor delineation: The weakest link in the search for accuracy in radiotherapy.
      ].
      However, no consensus on the necessary level of uncertainty exists yet. While numerous studies were performed, they lack methodological consistency, making it difficult to pin down an uncertainty to cover both the accuracy and variation in delineation [
      • Vinod S.K.
      • Jameson M.G.
      • Min M.
      • Holloway L.C.
      Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies.
      ]. There is only limited data on the resulting variation in target expansion between treatment centers. While for prostate cancer- and brain tumor patients the variation is on the level of or exceeding patient setup-up errors [
      • Segedin B.
      • Petric P.
      Uncertainties in target volume delineation in radiotherapy - Are they relevant and what can we do about them?.
      ], the target expansion varies greatly in the heterogeneous head and neck region, ranging from 0 to 15 mm between treatment centers [
      • Hong T.S.
      • Tomé W.A.
      • Harari P.M.
      Heterogeneity in head and neck IMRT target design and clinical practice.
      ].
      The presented work by Hofmaier et al. [
      • Hofmaier J.
      • Walter F.
      • Hadi I.
      • Rottler M.
      • von Bestenbostel R.
      • Dedes G.
      • et al.
      Combining inter-observer variability, range and setup uncertainty in a variance-based sensitivity analysis for proton therapy.
      ], published in this virtual special issue of physics highlight papers from the recent ESTRO 2021 conference, quantitatively assesses the impact on calculated dose of IOV in target delineation in combination with uncertainties in patient setup and range prediction. The work utilizes a Monte-Carlo variance-based sensitivity analysis framework [
      • Saltelli A.
      • Annoni P.
      • Azzini I.
      • Campolongo F.
      • Ratto M.
      • Tarantola S.
      Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index.
      ] for error combination, where input parameters are sampled from assumed uncertainty distributions as well as from a set of delineations to quantify their influence on dose calculation and consequently dose/volume parameters. The approach allows for a direct, patient-specific quantification of the individual uncertainty factors, which can be used to support decision making in the clinical plan evaluation process. For individual patients in a small cohort of benign skull base meningioma patients, the authors traced back relevant deteriorations on D95% of the clinical target volume to the variations in delineation.
      It should be noted that their specific metric for the IOV was calculated from different target volume delineations and a consensus target volume obtained with the STAPLE algorithm [
      • Warfield S.K.
      • Zou K.H.
      • Wells W.M.
      Simultaneous Truth and Performance Level Estimation (STAPLE): an algorithm for the validation of image segmentation.
      ]. This makes the presented results susceptible to individual delineations deviating from the group, as also pointed out by the authors. A different metric choice may lead to completely different results. The presented work is therefore first of all a feasibility study on how delineation uncertainties in proton therapy can be considered on a patient-individual level as part of a sensitivity analysis. At the same time, a major benefit of the framework is its adaptability in including different uncertainty sources, such as relative biological effectiveness, as done in previous publications from the authors [
      • Hofmaier J.
      • Dedes G.
      • Carlson D.J.
      • Parodi K.
      • Belka C.
      • Kamp F.
      Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions.
      ], with the major limiting factor being computation time.
      A future application of the presented framework on a larger patient cohort of primary brain tumor- as well as pelvic cancer patients may allow for a better understanding of the degree to which current clinical safety margins cover variations in delineation. There, an IOV metric more robust against outliers is needed. In clinical routine, where the labor-intensive delineation by multiple clinicians is generally unfeasible, the application of the framework on contours from different automated delineation approaches may prove beneficial to identify patients either requiring larger or allowing for smaller safety margins.

      Declaration of Competing Interest

      The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: OncoRay has an institutional research agreement with Siemens Healthineers in the field of dual-energy CT for particle therapy (2016–2020). Furthermore, OncoRay has an institutional agreement as reference center for dual-energy CT in radiotherapy as well as a software evaluation contract with Siemens Healthineers.

      References

        • Baumann M.
        • Krause M.
        • Overgaard J.
        • Debus J.
        • Bentzen S.M.
        • Daartz J.
        • et al.
        Radiation oncology in the era of precision medicine.
        Nat Rev Cancer. 2016; 16: 234-249https://doi.org/10.1038/nrc.2016.18
        • Paganetti H.
        Range uncertainties in proton therapy and the role of Monte Carlo simulations.
        Phys Med Biol. 2012; 57: R99-R117https://doi.org/10.1088/0031-9155/57/11/R99
        • Yang M.
        • Zhu X.R.
        • Park P.C.
        • Titt U.
        • Mohan R.
        • Virshup G.
        • et al.
        Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration.
        Phys Med Biol. 2012; 57: 4095-4115https://doi.org/10.1088/0031-9155/57/13/4095
        • Bär E.
        • Lalonde A.
        • Royle G.
        • Lu H.-M.
        • Bouchard H.
        The potential of dual-energy CT to reduce proton beam range uncertainties.
        Med Phys. 2017; 44: 2332-2344https://doi.org/10.1002/mp.12215
        • Rizzoglio V.
        • Adelmann A.
        • Gerbershagen A.
        • Meer D.
        • Nesteruk K.P.
        • Schippers J.M.
        Uncertainty quantification analysis and optimization for proton therapy beam lines.
        Phys Medica. 2020; 75: 11-18https://doi.org/10.1016/j.ejmp.2020.05.013
      1. ISO. Guide to the Expression of Uncertainty in Measurement. Geneva, Switzerland: International Organization for Standardization; 2008.

        • Berthold J.
        • Khamfongkhruea C.
        • Petzoldt J.
        • Thiele J.
        • Hölscher T.
        • Wohlfahrt P.
        • et al.
        First-in-human validation of CT-based proton range prediction using prompt gamma imaging in prostate cancer treatments.
        Int J Radiat Oncol Biol Phys. 2021; 111: 1033-1043https://doi.org/10.1016/j.ijrobp.2021.06.036
        • Peters N.
        • Wohlfahrt P.
        • Hofmann C.
        • Möhler C.
        • Menkel S.
        • Tschiche M.
        • et al.
        Reduction of clinical safety margins in proton therapy enabled by the clinical implementation of dual-energy CT for direct stopping-power prediction.
        Radiother Oncol. 2022; https://doi.org/10.1016/j.radonc.2021.11.002
        • Fredriksson A.
        • Forsgren A.
        • Hårdemark B.
        Minimax optimization for handling range and setup uncertainties in proton therapy.
        Med Phys. 2011; 38: 1672-1684https://doi.org/10.1118/1.3556559
        • Pflugfelder D.
        • Wilkens J.J.
        • Oelfke U.
        Worst case optimization: A method to account for uncertainties in the optimization of intensity modulated proton therapy.
        Phys Med Biol. 2008; 53: 1689-1700https://doi.org/10.1088/0031-9155/53/6/013
        • Liu W.
        • Zhang X.
        • Li Y.
        • Mohan R.
        Robust optimization of intensity modulated proton therapy.
        Med Phys. 2012; 39: 1079-1091https://doi.org/10.1118/1.3679340
        • Unkelbach J.
        • Paganetti H.
        Robust proton treatment planning: Physical and biological optimization.
        Semin Radiat Oncol. 2018; 28: 88-96https://doi.org/10.1016/j.semradonc.2017.11.005
        • Njeh C.F.
        Tumor delineation: The weakest link in the search for accuracy in radiotherapy.
        J Med Phys. 2008; 33: 136-140https://doi.org/10.4103/0971-6203.44472
        • Vinod S.K.
        • Jameson M.G.
        • Min M.
        • Holloway L.C.
        Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies.
        Radiother Oncol. 2016; 121: 169-179https://doi.org/10.1016/j.radonc.2016.09.009
        • Segedin B.
        • Petric P.
        Uncertainties in target volume delineation in radiotherapy - Are they relevant and what can we do about them?.
        Radiol Oncol. 2016; 50: 254-262https://doi.org/10.1515/raon-2016-0023
        • Hong T.S.
        • Tomé W.A.
        • Harari P.M.
        Heterogeneity in head and neck IMRT target design and clinical practice.
        Radiother Oncol. 2012; 103: 92-98https://doi.org/10.1016/j.radonc.2012.02.010
        • Hofmaier J.
        • Walter F.
        • Hadi I.
        • Rottler M.
        • von Bestenbostel R.
        • Dedes G.
        • et al.
        Combining inter-observer variability, range and setup uncertainty in a variance-based sensitivity analysis for proton therapy.
        Phys Imaging Radiat Oncol. 2021; 20: 117-120https://doi.org/10.1016/j.phro.2021.11.005
        • Saltelli A.
        • Annoni P.
        • Azzini I.
        • Campolongo F.
        • Ratto M.
        • Tarantola S.
        Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index.
        Comput Phys Commun. 2010; 181: 259-270https://doi.org/10.1016/j.cpc.2009.09.018
        • Warfield S.K.
        • Zou K.H.
        • Wells W.M.
        Simultaneous Truth and Performance Level Estimation (STAPLE): an algorithm for the validation of image segmentation.
        IEEE Trans Med Imaging. 2004; 23: 903-921https://doi.org/10.1109/TMI.2004.828354
        • Hofmaier J.
        • Dedes G.
        • Carlson D.J.
        • Parodi K.
        • Belka C.
        • Kamp F.
        Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions.
        Med Phys. 2021; 48: 805-818https://doi.org/10.1002/mp.14596