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Review
. 2025 Sep:210:111039.
doi: 10.1016/j.radonc.2025.111039. Epub 2025 Jul 9.

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

Affiliations
Review

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

Constantinos Zamboglou et al. Radiother Oncol. 2025 Sep.

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.

Keywords: Androgen deprivation therapy; Artificial intelligence; Biomarkers; Digital pathology; Personalized medicine; Prostate cancer; Radiotherapy; Risk stratification; Treatment selection.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alison Tree is supported by a Cancer Research UK Radiation Research Centre of Excellence at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust (grant ref: A28724 and RRCOER-Jun24/100006) and a Cancer Research UK Programme Grant (ref: C33589/A28284). Alison Tree acknowledges NHS funding to the NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. She declares research funding from Elekta, Varian, Accuray and Artera. Alison Tree declares honoraria or travel assistance from Elekta, Accuray and Janssen. Marcin Miszczyk was supported by the European Urological Scholarship Programme (EUSP) Scholarship of the European Association of Urology (EAU). Tyler Seibert reports honoraria from Varian Medical Systems, WebMD, MJH Life Sciences, GE Healthcare, Blue Earth Diagnostics, and Janssen; he has an equity interest in CorTechs Labs, Inc. and serves on its Scientific Advisory Board; he receives research funding from GE Healthcare and Blue Earth Diagnostics, as well as in-kind research support from Quibim, Inc., both through the University of California San Diego. These companies might potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. Silke Gillessen reports personal honoraria for invited speaker for ESMO, Schweizerische Gesellschaft für Medizinische Onkologie (SGMO)/Meister ConCept GmbH; other honoraria from University of Applied Sciences and Arts of Southern Switzerland (SUPSI). Travel grants from Bayer, Gilead, Intellisphere LLC, Johnson & Johnson. Institutional honoraria advisory boards or in Independent Data Monitoring-/Steering Committees from Amgen, Astellas, AstraZeneca, Avalere Health, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Innomedica, Ipsen, LinkinVax, Macrogenics, Merck, MDS Merck Sharp & Dohme, Novartis, Pfizer; invited speaker for AdMeTech Foundation, EPG Health, ESMO, Intellisphere LLC, Medtoday Switzerland, Orikata, PeerVoice, Pfizer, Schweizerische Gesellschaft für Medizinische Onkologie (SGMO)/Meister ConCept GmbH, Silvio Grasso Consulting, Swiss group for Clinical Cancer Research (SAKK), UroPratica Group. Patent for a research method for biomarker WO2009138392. Elena Castro acts as a consultant or advisory role for AstraZeneca, Bayer, Full Life, Johnson & Johnson, Lilly, Medscape, Merck, MSD Oncology, Novartis and Pfizer; travel/accommodation support from AstraZeneca, Bayer, Janssen and Pfizer; honoraria from Astellas Pharma, AstraZeneca, Bayer, Johnson & Johnson, Medscape, Peerview, Pfizer and Telix; and research funding from Bayer, Johnson & Johnson and Pfizer. Constantinos Zamboglou and Iosif Strouthos receive study support from Artera for the HypoPro and HypoElect trials. Constantinos Zamboglou acts as a consultant for Teleflex. All remaining authors have declared no conflicts of interest.

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