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@ARTICLE{Zamboglou:302842,
      author       = {C. Zamboglou and W. D. Doncker and A. T. Christoforou and
                      S. Arcangeli and A. Berlin and P. Blanchard and G. Bauman
                      and R. Campi and E. Castro and A. Choudhury and A. D. Pra
                      and C. Draulans and N. Desai and K. Ferentinos and G.
                      Francolini and S. Gillessen and A.-L. Grosu$^*$ and J. G.
                      Rivas and T. Hoelscher and G. Hruby and B. A. Jereczek-Fossa
                      and S. Kamran and V. Kasivisvanathan and A. U. Kishan and V.
                      Kounnis and A. Loblaw and J. Martin and F. Mastroleo and A.
                      S. Merseburger and M. Miszczyk and O. Mohamad and P. Ost and
                      A. Papatsoris and J. C. Peeken$^*$ and F. Sanguedolce and P.
                      Sargos and N. Schmidt-Hegemann and T. M. Seibert and M.
                      Shelan and S. Siva and T. F. W. Soeterik and D. E. Spratt
                      and A. Stenzl and I. Strouthos and P. Sutera and S. Supiot
                      and D. Tilki and P. T. Tran and A. C. Tree and J. Tward and
                      Y. Ürün and N. Vapiwala and M. R. Waddle and E. Wegener
                      and T. Zilli and V. Murthy and A. H. Thieme and S. Spohn},
      title        = {o{D}igital pathology biomarkers for guiding
                      radiotherapy-based treatment concepts in prostate cancer - a
                      systematic review and expert consensus.},
      journal      = {Radiotherapy and oncology},
      volume       = {210},
      issn         = {0167-8140},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2025-01382},
      pages        = {111039},
      year         = {2025},
      abstract     = {Current risk-stratification systems for prostate cancer
                      (PCa) do not sufficiently reflect the disease heterogeneity,
                      and digital pathology (DP) combined with artificial
                      intelligence (AI) tools (DP-AI) may offer a solution to this
                      challenge. The aim of this work is to summarize the role of
                      DP-AI for PCa patients treated with radiotherapy (RT), and
                      to point out future areas of research. We conducted (1) a
                      systematic review on the evidence of DP-AI for patients
                      treated with RT and (2) a survey of experts using a modified
                      Delphi method, addressing the current role of DP-AI in
                      clinical and research practice to identify relevant fields
                      of future development. Eleven studies investigated DP-AI in
                      PCa RT, with most using the multimodal AI (MMAI) classifier
                      and four ongoing studies are currently prospectively testing
                      the DP-AI performance. DP-AI showed strong prognostic and
                      predictive performance for endpoints like distant metastasis
                      free survival and overall survival, outperforming
                      traditional risk models and assisting treatment decisions
                      such as androgen deprivation therapy (ADT) duration.
                      Fifty-one and 35 experts responded to round 1 and round 2 of
                      the survey respectively. Questions with ≥75 $\%$ agreement
                      were considered relevant and included in the qualitative
                      analysis. Survey results confirmed growing adoption of DP
                      scanners, although regional differences in re-imbursement
                      mechanisms and availability persist, with experts endorsing
                      DP-AI's potential across localized, postoperative, and
                      metastatic settings, though further prospective validation
                      is needed. DP-AI tools show strong prognostic and predictive
                      potential in various PCa by guiding patient stratification
                      and optimising ADT duration in primary RT. Prospective
                      studies and validation in cohorts using modern diagnostic
                      and treatment methods are needed before broad clinical
                      adoption.},
      subtyp        = {Review Article},
      keywords     = {Androgen deprivation therapy (Other) / Artificial
                      intelligence (Other) / Biomarkers (Other) / Digital
                      pathology (Other) / Personalized medicine (Other) / Prostate
                      cancer (Other) / Radiotherapy (Other) / Risk stratification
                      (Other) / Treatment selection (Other)},
      cin          = {FR01 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)FR01-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40645505},
      doi          = {10.1016/j.radonc.2025.111039},
      url          = {https://inrepo02.dkfz.de/record/302842},
}