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

Constantinos Zamboglou; William De Doncker; Andreas Thomas Christoforou; Stefano Arcangeli; Alejandro Berlin; Pierre Blanchard; Glenn Bauman; Riccardo Campi; Elena Castro; Ananya Choudhury; Alan Dal Pra; Cédric Draulans; Neil Desai; Konstantinos Ferentinos; Giulio Francolini; Silke Gillessen; Anca Ligia Grosu; Juan Gómez Rivas; Tobias Hoelscher; George Hruby; Barbara Alicja Jereczek-Fossa; Sophia Kamran; Veeru Kasivisvanathan; Amar U. Kishan; Valentinos Kounnis; Andrew Loblaw; Jarad Martin; Federico Mastroleo; Axel S. Merseburger; Marcin Miszczyk; Osama Mohamad; Piet Ost; Athanasios Papatsoris; Jan C. Peeken; Francesco Sanguedolce; Paul Sargos; Nina Schmidt-Hegemann; Tyler M. Seibert; Mohamed Shelan; Shankar Siva; Timo F.W. Soeterik; Daniel E. Spratt; Arnulf Stenzl; Iosif Strouthos; Philip Sutera; Stephane Supiot; Derya Tilki; Phuoc T. Tran; Alison C. Tree; Jonathan Tward; Yüksel Ürün; Neha Vapiwala; Mark R. Waddle; Eric Wegener; Thomas Zilli; Vedang Murthy; Alexander Henry Thieme; Simon Spohn

doi:10.1016/j.radonc.2025.111039

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

Constantinos Zamboglou, William De Doncker^*, Andreas Thomas Christoforou, Stefano Arcangeli, Alejandro Berlin, Pierre Blanchard, Glenn Bauman, Riccardo Campi, Elena Castro, Ananya Choudhury, Alan Dal Pra, Cédric Draulans, Neil Desai, Konstantinos Ferentinos, Giulio Francolini, Silke Gillessen, Anca Ligia Grosu, Juan Gómez Rivas, Tobias Hoelscher, George HrubyBarbara Alicja Jereczek-Fossa, Sophia Kamran, Veeru Kasivisvanathan, Amar U. Kishan, Valentinos Kounnis, Andrew Loblaw, Jarad Martin, Federico Mastroleo, Axel S. Merseburger, Marcin Miszczyk, Osama Mohamad, Piet Ost, Athanasios Papatsoris, Jan C. Peeken, Francesco Sanguedolce, Paul Sargos, Nina Schmidt-Hegemann, Tyler M. Seibert, Mohamed Shelan, Shankar Siva, Timo F.W. Soeterik, Daniel E. Spratt, Arnulf Stenzl, Iosif Strouthos, Philip Sutera, Stephane Supiot, Derya Tilki, Phuoc T. Tran, Alison C. Tree, Jonathan Tward, Yüksel Ürün, Neha Vapiwala, Mark R. Waddle, Eric Wegener, Thomas Zilli, Vedang Murthy, Alexander Henry Thieme, Simon Spohn

^*Corresponding author for this work

Clinic of Urology

1 Citation (Scopus)

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.

Original language	English
Article number	111039
Journal	Radiotherapy and Oncology
Volume	210
ISSN	0167-8140
DOIs	https://doi.org/10.1016/j.radonc.2025.111039
Publication status	Published - 09.2025

Funding

Funders	Funder number
Foundation for Biomedical Research and Innovation at Kobe

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

DFG Research Classification Scheme

2.22-33 Nuclear Medicine, Radiotherapy, Radiobiology

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10.1016/j.radonc.2025.111039

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Zamboglou, C., Doncker, W. D., 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., ... Spohn, S. (2025). oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer − a systematic review and expert consensus. Radiotherapy and Oncology, 210, Article 111039. https://doi.org/10.1016/j.radonc.2025.111039

@article{5d8dcccb1d4a48ac816b3db68220c8ee,

title = "oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer − a systematic review and expert consensus",

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.",

author = "Constantinos Zamboglou and Doncker, \{William De\} and Christoforou, \{Andreas Thomas\} and Stefano Arcangeli and Alejandro Berlin and Pierre Blanchard and Glenn Bauman and Riccardo Campi and Elena Castro and Ananya Choudhury and Pra, \{Alan Dal\} and C{\'e}dric Draulans and Neil Desai and Konstantinos Ferentinos and Giulio Francolini and Silke Gillessen and Grosu, \{Anca Ligia\} and Rivas, \{Juan G{\'o}mez\} and Tobias Hoelscher and George Hruby and Jereczek-Fossa, \{Barbara Alicja\} and Sophia Kamran and Veeru Kasivisvanathan and Kishan, \{Amar U.\} and Valentinos Kounnis and Andrew Loblaw and Jarad Martin and Federico Mastroleo and Merseburger, \{Axel S.\} and Marcin Miszczyk and Osama Mohamad and Piet Ost and Athanasios Papatsoris and Peeken, \{Jan C.\} and Francesco Sanguedolce and Paul Sargos and Nina Schmidt-Hegemann and Seibert, \{Tyler M.\} and Mohamed Shelan and Shankar Siva and Soeterik, \{Timo F.W.\} and Spratt, \{Daniel E.\} and Arnulf Stenzl and Iosif Strouthos and Philip Sutera and Stephane Supiot and Derya Tilki and Tran, \{Phuoc T.\} and Tree, \{Alison C.\} and Jonathan Tward and Y{\"u}ksel {\"U}r{\"u}n and Neha Vapiwala and Waddle, \{Mark R.\} and Eric Wegener and Thomas Zilli and Vedang Murthy and Thieme, \{Alexander Henry\} and Simon Spohn",

note = "Publisher Copyright: {\textcopyright} 2025 Elsevier B.V.",

year = "2025",

month = sep,

doi = "10.1016/j.radonc.2025.111039",

language = "English",

volume = "210",

journal = "Radiotherapy and Oncology",

issn = "0167-8140",

publisher = "Elsevier Ireland Ltd",

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Zamboglou, C, Doncker, WD, Christoforou, AT, Arcangeli, S, Berlin, A, Blanchard, P, Bauman, G, Campi, R, Castro, E, Choudhury, A, Pra, AD, Draulans, C, Desai, N, Ferentinos, K, Francolini, G, Gillessen, S, Grosu, AL, Rivas, JG, Hoelscher, T, Hruby, G, Jereczek-Fossa, BA, Kamran, S, Kasivisvanathan, V, Kishan, AU, Kounnis, V, Loblaw, A, Martin, J, Mastroleo, F, Merseburger, AS, Miszczyk, M, Mohamad, O, Ost, P, Papatsoris, A, Peeken, JC, Sanguedolce, F, Sargos, P, Schmidt-Hegemann, N, Seibert, TM, Shelan, M, Siva, S, Soeterik, TFW, Spratt, DE, Stenzl, A, Strouthos, I, Sutera, P, Supiot, S, Tilki, D, Tran, PT, Tree, AC, Tward, J, Ürün, Y, Vapiwala, N, Waddle, MR, Wegener, E, Zilli, T, Murthy, V, Thieme, AH & Spohn, S 2025, 'oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer − a systematic review and expert consensus', Radiotherapy and Oncology, vol. 210, 111039. https://doi.org/10.1016/j.radonc.2025.111039

TY - JOUR

T1 - oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer − a systematic review and expert consensus

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

PY - 2025/9

Y1 - 2025/9

N2 - 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.

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 % 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.

UR - https://www.scopus.com/pages/publications/105010263448

U2 - 10.1016/j.radonc.2025.111039

DO - 10.1016/j.radonc.2025.111039

M3 - Scientific review articles

C2 - 40645505

AN - SCOPUS:105010263448

SN - 0167-8140

VL - 210

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

M1 - 111039

ER -

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

Abstract

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UN SDGs

DFG Research Classification Scheme

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