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000302842 1001_ $$aZamboglou, Constantinos$$b0
000302842 245__ $$aoDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer - a systematic review and expert consensus.
000302842 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2025
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000302842 520__ $$aCurrent risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity, and digital pathology (DP) combined with artificial intelligence (AI) tools (DP-AI) may offer a solution to this challenge. The aim of this work is to summarize the role of DP-AI for PCa patients treated with radiotherapy (RT), and to point out future areas of research. We conducted (1) a systematic review on the evidence of DP-AI for patients treated with RT and (2) a survey of experts using a modified Delphi method, addressing the current role of DP-AI in clinical and research practice to identify relevant fields of future development. Eleven studies investigated DP-AI in PCa RT, with most using the multimodal AI (MMAI) classifier and four ongoing studies are currently prospectively testing the DP-AI performance. DP-AI showed strong prognostic and predictive performance for endpoints like distant metastasis free survival and overall survival, outperforming traditional risk models and assisting treatment decisions such as androgen deprivation therapy (ADT) duration. Fifty-one and 35 experts responded to round 1 and round 2 of the survey respectively. Questions with ≥75 % agreement were considered relevant and included in the qualitative analysis. Survey results confirmed growing adoption of DP scanners, although regional differences in re-imbursement mechanisms and availability persist, with experts endorsing DP-AI's potential across localized, postoperative, and metastatic settings, though further prospective validation is needed. DP-AI tools show strong prognostic and predictive potential in various PCa by guiding patient stratification and optimising ADT duration in primary RT. Prospective studies and validation in cohorts using modern diagnostic and treatment methods are needed before broad clinical adoption.
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000302842 650_7 $$2Other$$aAndrogen deprivation therapy
000302842 650_7 $$2Other$$aArtificial intelligence
000302842 650_7 $$2Other$$aBiomarkers
000302842 650_7 $$2Other$$aDigital pathology
000302842 650_7 $$2Other$$aPersonalized medicine
000302842 650_7 $$2Other$$aProstate cancer
000302842 650_7 $$2Other$$aRadiotherapy
000302842 650_7 $$2Other$$aRisk stratification
000302842 650_7 $$2Other$$aTreatment selection
000302842 7001_ $$aDoncker, William De$$b1
000302842 7001_ $$aChristoforou, Andreas Thomas$$b2
000302842 7001_ $$aArcangeli, Stefano$$b3
000302842 7001_ $$aBerlin, Alejandro$$b4
000302842 7001_ $$aBlanchard, Pierre$$b5
000302842 7001_ $$aBauman, Glenn$$b6
000302842 7001_ $$aCampi, Riccardo$$b7
000302842 7001_ $$aCastro, Elena$$b8
000302842 7001_ $$aChoudhury, Ananya$$b9
000302842 7001_ $$aPra, Alan Dal$$b10
000302842 7001_ $$aDraulans, Cédric$$b11
000302842 7001_ $$aDesai, Neil$$b12
000302842 7001_ $$aFerentinos, Konstantinos$$b13
000302842 7001_ $$aFrancolini, Giulio$$b14
000302842 7001_ $$aGillessen, Silke$$b15
000302842 7001_ $$0P:(DE-HGF)0$$aGrosu, Anca-Ligia$$b16
000302842 7001_ $$aRivas, Juan Gómez$$b17
000302842 7001_ $$aHoelscher, Tobias$$b18
000302842 7001_ $$aHruby, George$$b19
000302842 7001_ $$aJereczek-Fossa, Barbara Alicja$$b20
000302842 7001_ $$aKamran, Sophia$$b21
000302842 7001_ $$aKasivisvanathan, Veeru$$b22
000302842 7001_ $$aKishan, Amar U$$b23
000302842 7001_ $$aKounnis, Valentinos$$b24
000302842 7001_ $$aLoblaw, Andrew$$b25
000302842 7001_ $$aMartin, Jarad$$b26
000302842 7001_ $$aMastroleo, Federico$$b27
000302842 7001_ $$aMerseburger, Axel S$$b28
000302842 7001_ $$aMiszczyk, Marcin$$b29
000302842 7001_ $$aMohamad, Osama$$b30
000302842 7001_ $$aOst, Piet$$b31
000302842 7001_ $$aPapatsoris, Athanasios$$b32
000302842 7001_ $$0P:(DE-HGF)0$$aPeeken, Jan C$$b33
000302842 7001_ $$aSanguedolce, Francesco$$b34
000302842 7001_ $$aSargos, Paul$$b35
000302842 7001_ $$aSchmidt-Hegemann, Nina$$b36
000302842 7001_ $$aSeibert, Tyler M$$b37
000302842 7001_ $$aShelan, Mohamed$$b38
000302842 7001_ $$aSiva, Shankar$$b39
000302842 7001_ $$aSoeterik, Timo F W$$b40
000302842 7001_ $$aSpratt, Daniel E$$b41
000302842 7001_ $$aStenzl, Arnulf$$b42
000302842 7001_ $$aStrouthos, Iosif$$b43
000302842 7001_ $$aSutera, Philip$$b44
000302842 7001_ $$aSupiot, Stephane$$b45
000302842 7001_ $$aTilki, Derya$$b46
000302842 7001_ $$aTran, Phuoc T$$b47
000302842 7001_ $$aTree, Alison C$$b48
000302842 7001_ $$aTward, Jonathan$$b49
000302842 7001_ $$aÜrün, Yüksel$$b50
000302842 7001_ $$aVapiwala, Neha$$b51
000302842 7001_ $$aWaddle, Mark R$$b52
000302842 7001_ $$aWegener, Eric$$b53
000302842 7001_ $$aZilli, Thomas$$b54
000302842 7001_ $$aMurthy, Vedang$$b55
000302842 7001_ $$aThieme, Alexander Henry$$b56
000302842 7001_ $$aSpohn, Simon$$b57
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