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@ARTICLE{SevillaMoreno:298940,
      author       = {A. C. Sevilla-Moreno and M. E. Puerta-Yepes and N. Wahl$^*$
                      and R. Benito-Herce and G. Cabal-Arango},
      title        = {{I}nterval {A}nalysis-{B}ased {O}ptimization: {A} {R}obust
                      {M}odel for {I}ntensity-{M}odulated {R}adiotherapy
                      ({IMRT}).},
      journal      = {Cancers},
      volume       = {17},
      number       = {3},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2025-00374},
      pages        = {504},
      year         = {2025},
      note         = {Division of Medical Physics in Radiation Oncology,},
      abstract     = {Background: Cancer remains one of the leading causes of
                      mortality worldwide, with radiotherapy playing a crucial
                      role in its treatment. Intensity-modulated radiotherapy
                      (IMRT) enables precise dose delivery to tumors while sparing
                      healthy tissues. However, geometric uncertainties such as
                      patient positioning errors and anatomical deformations can
                      compromise treatment accuracy. Traditional methods use
                      safety margins, which may lead to excessive irradiation of
                      healthy organs or insufficient tumor coverage. Robust
                      optimization techniques, such as minimax approaches, attempt
                      to address these uncertainties but can result in overly
                      conservative treatment plans. This study introduces an
                      interval analysis-based optimization model for IMRT,
                      offering a more flexible approach to uncertainty management.
                      Methods: The proposed model represents geometric
                      uncertainties using interval dose influence matrices and
                      incorporates Bertoluzza's metric to balance tumor coverage
                      and organ-at-risk (OAR) protection. The θ parameter allows
                      controlled robustness modulation. The model was implemented
                      in matRad, an open-source treatment planning system, and
                      evaluated on five prostate cancer cases. Results were
                      compared against traditional Planning Target Volume (PTV)
                      and minimax robust optimization approaches. Results: The
                      interval-based model improved tumor coverage by $5.8\%$
                      while reducing bladder dose by $4.2\%$ compared to PTV. In
                      contrast, minimax robust optimization improved tumor
                      coverage by $25.8\%$ but increased bladder dose by $23.2\%.$
                      The interval-based approach provided a better balance
                      between tumor coverage and OAR protection, demonstrating its
                      potential to enhance treatment effectiveness without
                      excessive conservatism. Conclusions: This study presents a
                      novel framework for IMRT planning that improves uncertainty
                      management through interval analysis. By allowing adjustable
                      robustness modulation, the proposed model enables more
                      personalized and clinically adaptable treatment plans. These
                      findings highlight the potential of interval analysis as a
                      powerful tool for optimizing radiotherapy outcomes,
                      balancing treatment efficacy and patient safety.},
      keywords     = {IMRT (Other) / interval analysis (Other) / radiotherapy
                      (Other) / robust optimization (Other) / uncertainty (Other)},
      cin          = {E041},
      ddc          = {610},
      cid          = {I:(DE-He78)E041-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39941871},
      pmc          = {pmc:PMC11816179},
      doi          = {10.3390/cancers17030504},
      url          = {https://inrepo02.dkfz.de/record/298940},
}