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Record W4412392894 · doi:10.1016/j.radonc.2025.111039

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

2025· review· en· W4412392894 on OpenAlexaff
Constantinos Zamboglou, William De Doncker, Stefano Arcangeli, Alejandro Berlín, Pierre Blanchard, Glenn Bauman, Riccardo Campi, Elena Castro, Ananya Choudhury, Alan Dal Pra, Cédric Draulans, Neil Desai, Giulio Francolini, Silke Gillessen, Anca‐Ligia Grosu, Juan Gómez Rivas, Tobias Hoelscher, George Hruby, Barbara Alicja Jereczek‐Fossa, Sophia C. 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-Sophie Schmidt-Hegemann, Tyler M. Seibert, Mohamed Shelan, Shankar Siva, Timo Soeterik, Daniel E. Spratt, Arnulf Stenzl, Iosif Strouthos, Philip Sutera, S. Supiot, Derya Tilki, Phuoc T. Tran, Alison Tree, Jonathan D. Tward, Yüksel Ürün, Neha Vapiwala, Mark R. Waddle, Eric Wegener, Thomas Zilli, Vedang Murthy, Alexander Thieme, Simon K. B. Spohn

Bibliographic record

VenueRadiotherapy and Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsInstitute of Cancer ResearchLondon Health Sciences CentreSunnybrook Health Science CentreWestern UniversityPrincess Margaret Cancer Centre
FundersResearch and Innovation Foundation
KeywordsProstate cancerMedicineAndrogen deprivation therapyRadiation therapyDelphi methodMultiparametric MRIClassifier (UML)Artificial intelligenceOncologyMedical physicsInternal medicineMachine learningCancerComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0060.004
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.411
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractno

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