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Zamboglou, C.* ; Doncker, W.* ; Christoforou, A.T.* ; Arcangeli, S.* ; Berlin, A.* ; Blanchard, P.* ; Bauman, G.* ; Campi, R.* ; Castro, E.* ; Choudhury, A.* ; Pra, A.D.* ; Draulans, C.* ; Desai, N.* ; Ferentinos, K.* ; Francolini, G.* ; Gillessen, S.* ; Grosu, A.L.* ; Rivas, J.G.* ; Hoelscher, T.* ; Hruby, G.* ; Jereczek-Fossa, B.A.* ; Kamran, S.* ; Kasivisvanathan, V.* ; Kishan, A.U.* ; Kounnis, V.* ; Loblaw, A.* ; Martin, J.* ; Mastroleo, F.* ; Merseburger, A.S.* ; Miszczyk, M.* ; Mohamad, O.* ; Ost, P.* ; Papatsoris, A.* ; Peeken, J.C. ; Sanguedolce, F.* ; Sargos, P.* ; Schmidt-Hegemann, N.* ; Seibert, T.M.* ; Shelan, M.* ; Siva, S.* ; Soeterik, T.F.W.* ; Spratt, D.E.* ; Stenzl, A.* ; Strouthos, I.* ; Sutera, P.* ; Supiot, S.* ; Tilki, D.* ; Tran, P.T.* ; Tree, A.C.* ; Tward, J.* ; Ürün, Y.* ; Vapiwala, N.* ; Waddle, M.R.* ; Wegener, E.* ; Zilli, T.* ; Murthy, V.* ; Thieme, A.H.* ; Spohn, S.*

oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer - a systematic review and expert consensus.

Radiother. Oncol. 210:111039 (2025)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Androgen Deprivation Therapy ; Artificial Intelligence ; Biomarkers ; Digital Pathology ; Personalized Medicine ; Prostate Cancer ; Radiotherapy ; Risk Stratification ; Treatment Selection
ISSN (print) / ISBN 0167-8140
e-ISSN 1879-0887
Journal Radiotherapy and Oncology
Quellenangaben Volume: 210, Issue: , Pages: , Article Number: 111039 Supplement: ,
Publisher Elsevier
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)