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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer - a systematic review and expert consensus.
Radiother. Oncol. 210:111039 (2025)
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.
Impact Factor
Scopus SNIP
Altmetric
0.000
0.000
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Androgen Deprivation Therapy ; Artificial Intelligence ; Biomarkers ; Digital Pathology ; Personalized Medicine ; Prostate Cancer ; Radiotherapy ; Risk Stratification ; Treatment Selection; Artificial-intelligence; Trial; Validation; Therapy
Sprache
englisch
Veröffentlichungsjahr
2025
HGF-Berichtsjahr
2025
ISSN (print) / ISBN
0167-8140
e-ISSN
1879-0887
Zeitschrift
Radiotherapy and Oncology
Quellenangaben
Band: 210,
Artikelnummer: 111039
Verlag
Elsevier
Verlagsort
Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Radiation Medicine (IRM)
POF Topic(s)
30203 - Molecular Targets and Therapies
Forschungsfeld(er)
Radiation Sciences
PSP-Element(e)
G-501300-001
Förderungen
Cypriot research and Innovation Foundation as part of the EU framework of the Cohesion Policy Programme "THALIA 2021-2027"
WOS ID
001545464100001
WOS ID
001543500700001
Scopus ID
105010263448
PubMed ID
40645505
Erfassungsdatum
2025-07-21