TY  - JOUR
AU  - Zamboglou, Constantinos
AU  - Doncker, William De
AU  - Christoforou, Andreas Thomas
AU  - Arcangeli, Stefano
AU  - Berlin, Alejandro
AU  - Blanchard, Pierre
AU  - Bauman, Glenn
AU  - Campi, Riccardo
AU  - Castro, Elena
AU  - Choudhury, Ananya
AU  - Pra, Alan Dal
AU  - Draulans, Cédric
AU  - Desai, Neil
AU  - Ferentinos, Konstantinos
AU  - Francolini, Giulio
AU  - Gillessen, Silke
AU  - Grosu, Anca-Ligia
AU  - Rivas, Juan Gómez
AU  - Hoelscher, Tobias
AU  - Hruby, George
AU  - Jereczek-Fossa, Barbara Alicja
AU  - Kamran, Sophia
AU  - Kasivisvanathan, Veeru
AU  - Kishan, Amar U
AU  - Kounnis, Valentinos
AU  - Loblaw, Andrew
AU  - Martin, Jarad
AU  - Mastroleo, Federico
AU  - Merseburger, Axel S
AU  - Miszczyk, Marcin
AU  - Mohamad, Osama
AU  - Ost, Piet
AU  - Papatsoris, Athanasios
AU  - Peeken, Jan C
AU  - Sanguedolce, Francesco
AU  - Sargos, Paul
AU  - Schmidt-Hegemann, Nina
AU  - Seibert, Tyler M
AU  - Shelan, Mohamed
AU  - Siva, Shankar
AU  - Soeterik, Timo F W
AU  - Spratt, Daniel E
AU  - Stenzl, Arnulf
AU  - Strouthos, Iosif
AU  - Sutera, Philip
AU  - Supiot, Stephane
AU  - Tilki, Derya
AU  - Tran, Phuoc T
AU  - Tree, Alison C
AU  - Tward, Jonathan
AU  - Ürün, Yüksel
AU  - Vapiwala, Neha
AU  - Waddle, Mark R
AU  - Wegener, Eric
AU  - Zilli, Thomas
AU  - Murthy, Vedang
AU  - Thieme, Alexander Henry
AU  - Spohn, Simon
TI  - oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer - a systematic review and expert consensus.
JO  - Radiotherapy and oncology
VL  - 210
SN  - 0167-8140
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - DKFZ-2025-01382
SP  - 111039
PY  - 2025
AB  - 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 
KW  - Androgen deprivation therapy (Other)
KW  - Artificial intelligence (Other)
KW  - Biomarkers (Other)
KW  - Digital pathology (Other)
KW  - Personalized medicine (Other)
KW  - Prostate cancer (Other)
KW  - Radiotherapy (Other)
KW  - Risk stratification (Other)
KW  - Treatment selection (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40645505
DO  - DOI:10.1016/j.radonc.2025.111039
UR  - https://inrepo02.dkfz.de/record/302842
ER  -