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@ARTICLE{Puri:305343,
      author       = {T. Puri and T. Rancati and P. Seibold$^*$ and A. Webb and
                      E. V. Osorio and D. Azria and M.-P. Farcy-Jacquet and J.
                      Chang-Claude$^*$ and A. Dunning and M. Lambrecht and B.
                      Avuzzi and D. de Ruysscher and E. Sperk and A. Vega and L.
                      Veldeman and B. Rosenstein and S. Kerns and C. Talbot and A.
                      McWilliam and P. Hoskin and A. Choudhury and C. West and M.
                      van Herk},
      collaboration = {R. Consortium},
      title        = {{S}ensitivity analysis of dose-response model parameters
                      for the bladder in prostate cancer radiotherapy.},
      journal      = {Physica medica},
      volume       = {139},
      issn         = {1120-1797},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2025-02087},
      pages        = {105178},
      year         = {2025},
      abstract     = {This study evaluates how model parameter values affect
                      dose-response maps (DRMs) in identifying high-risk bladder
                      subregions associated with late urinary toxicities in
                      prostate cancer patients post-radiotherapy.Data from 1808
                      patients were analyzed for five late bladder toxicities.
                      Baseline scores were subtracted from maximum toxicity at 12
                      and 24 months and dichotomized into grades ≥ 1 and ≥ 2.
                      Bladders were segmented on computed tomography scans, and
                      dose-surface maps (DSMs) were created on 91 × 90 voxel
                      grids using spherical and cylindrical coordinates. Voxel
                      doses were converted to equivalent dose in 2 Gy fractions
                      (EQD2, α/β 1-3 Gy). Welch's t and Mann-Whitney U equations
                      were applied at each voxel location. Multiple comparisons
                      were corrected via permutation testing (10-10000
                      iterations), and statistically significant voxels were
                      identified using the 90th and 95th percentiles of Tmax/Umax.
                      Sensitivity of parameters was assessed by varying one
                      parameter at a time, with changes > 400 voxels $(∼5\%$ of
                      8190) classified as large and ≤ 400 as small.Urinary tract
                      obstruction was the only toxicity significantly associated
                      with bladder DSMs, focusing results on this outcome. After
                      baseline adjustment and dichotomization, event/nonevent
                      counts were 62/701 (grade≥1) and 21/742 (grade≥2; N =
                      763). DRM results showed large effects of toxicity grade
                      threshold, coordinate system, statistical test equation, and
                      Tmax/Umax thresholding. EQD2 α/β showed variable effects,
                      large for cylindrical and small for spherical coordinates,
                      while the number of permutations had only a small
                      effect.Parameter selection significantly influences
                      high-risk subregion identification in DRMs, emphasizing the
                      need for standardized parameter reporting for meaningful
                      external comparisons.},
      keywords     = {Bladder (Other) / IBDM (Other) / Image-based data mining
                      (Other) / Imaging biomarker (Other) / Multicenter clinical
                      trial (Other) / OAR (Other) / Organ-at-risk (Other) /
                      Prediction model (Other) / Predictive modeling (Other) /
                      Prostate cancer (Other) / Radiotherapy (Other) /
                      Standardization (Other) / Toxicity (Other) / VBA (Other) /
                      Voxel-based dose–response analysis (Other)},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41072101},
      doi          = {10.1016/j.ejmp.2025.105178},
      url          = {https://inrepo02.dkfz.de/record/305343},
}