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@ARTICLE{Eulitz:148860,
      author       = {J. Eulitz and B. Lutz and P. Wohlfahrt and A. Dutz and W.
                      Enghardt$^*$ and C. Karpowitz$^*$ and M. Krause$^*$ and E.
                      G. C. Troost$^*$ and A. Lühr},
      title        = {{A} {M}onte {C}arlo based radiation response modelling
                      framework to assess variability of clinical {RBE} in proton
                      therapy.},
      journal      = {Physics in medicine and biology},
      volume       = {64},
      number       = {22},
      issn         = {1361-6560},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2020-00052},
      pages        = {225020},
      year         = {2019},
      abstract     = {The clinical implementation of a variable relative
                      biological effectiveness (RBE) in proton therapy is
                      currently controversially discussed. Initial clinical
                      evidence indicates a variable proton RBE, which needs to be
                      verified. In this study, a radiation response modelling
                      framework for assessing clinical RBE variability is
                      established. It was applied to four selected glioma patients
                      (grade III) treated with adjuvant radio(chemo)therapy and
                      who developed late morphological image changes on
                      T1-weighted contrast-enhanced (T1w-CE) magnetic resonance
                      (MR) images within approximately two years of
                      recurrence-free follow-up. The image changes were correlated
                      voxelwise with dose and linear energy transfer (LET) values
                      using univariable and multivariable logistic regression
                      analysis. The regression models were evaluated by the
                      area-under-the-curve (AUC) method performing a leave-one-out
                      cross validation. The tolerance dose TD50 at which $50\%$ of
                      patient voxels experienced toxicity was interpolated from
                      the models. A Monte Carlo (MC) model was developed to
                      simulate dose and LET distributions, which includes variance
                      reduction (VR) techniques to decrease computation time. Its
                      reliability and accuracy were evaluated based on dose
                      calculations of the clinical treatment planning system (TPS)
                      as well as absolute dose measurements performed in the
                      patient specific quality assurance. Morphological image
                      changes were related to a combination of dose and LET. The
                      multivariable models revealed cross-validated AUC values of
                      up to 0.88. The interpolated TD50 curves decreased with
                      increasing LET indicating an increase in biological
                      effectiveness. The MC model reliably predicted average TPS
                      dose within the clinical target volume as well as absolute
                      water phantom dose measurements within $2\%$ accuracy using
                      dedicated VR settings. The observed correlation of dose and
                      LET with late brain tissue damage suggests considering RBE
                      variability for predicting chronic radiation-induced brain
                      toxicities. The MC model simulates radiation fields in
                      patients precisely and time-efficiently. Hence, this study
                      encourages and enables in-depth patient evaluation to assess
                      the variability of clinical proton RBE.},
      cin          = {L301},
      ddc          = {530},
      cid          = {I:(DE-He78)L301-20160331},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31374558},
      doi          = {10.1088/1361-6560/ab3841},
      url          = {https://inrepo02.dkfz.de/record/148860},
}