Evidence map›Paper›PMID 40691514›Full record

ArticleEuropean radiology2026

Artificial intelligence-generated apparent diffusion coefficient (AI-ADC) maps for prostate gland assessment: a multi-reader study.

Kutsev Bengisu Ozyoruk, Stephanie A Harmon, Enis C Yilmaz, Erich P Huang, David G Gelikman, Sonia Gaur, Francesco Giganti, Yan Mee Law, Daniel J Margolis, Pavan Kumar Jadda and 6 more

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Kutsev Bengisu OzyorukArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
Stephanie A HarmonArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
Enis C YilmazArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
Erich P HuangDivision of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
David G GelikmanArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
Sonia GaurDepartment of Radiology, Brigham & Women's Hospital, Boston, MA, USA.
Francesco GigantiDepartment of Radiology, University College London Hospital NHS Foundation Trust, London, UK.
Yan Mee LawDepartment of Radiology, Singapore General Hospital, Singapore, Singapore.
Daniel J MargolisWeill Cornell Imaging, Cornell University, New York, NY, USA.
Pavan Kumar JaddaOffice of Information Technology, Center for Cancer Research, NCI, Bethesda, MD, USA.
Sitarama RaaviOffice of Information Technology, Center for Cancer Research, NCI, Bethesda, MD, USA.
Sandeep GurramUrologic Oncology Branch, NCI, NIH, Bethesda, MD, USA.
Bradford J WoodCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Peter A PintoUrologic Oncology Branch, NCI, NIH, Bethesda, MD, USA.
Peter L ChoykeArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA.
Baris TurkbeyArtificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA. turkbeyi@mail.nih.gov.ORCID http://orcid.org/0000-0003-0853-6494

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare the quality of AI-ADC maps and standard ADC maps in a multi-reader study. MATERIALS AND

methodsMulti-reader study included 74 consecutive patients (median age = 66 years, [IQR = 57.25-71.75 years]; median PSA = 4.30 ng/mL [IQR = 1.33-7.75 ng/mL]) with suspected or confirmed PCa, who underwent mpMRI between October 2023 and January 2024. The study was conducted in two rounds, separated by a 4-week wash-out period. In each round, four readers evaluated T2W-MRI and standard or AI-generated ADC (AI-ADC) maps. Fleiss' kappa, quadratic-weighted Cohen's kappa statistics were used to assess inter-reader agreement. Linear mixed effect models were employed to compare the quality evaluation of standard versus AI-ADC maps.

resultsAI-ADC maps exhibited significantly enhanced imaging quality compared to standard ADC maps with higher ratings in windowing ease (β = 0.67 [95% CI 0.30-1.04], p < 0.05), prostate boundary delineation (β = 1.38 [95% CI 1.03-1.73], p < 0.001), reductions in distortion (β = 1.68 [95% CI 1.30-2.05], p < 0.001), noise (β = 0.56 [95% CI 0.24-0.88], p < 0.001). AI-ADC maps reduced reacquisition requirements for all readers (β = 2.23 [95% CI 1.69-2.76], p < 0.001), supporting potential workflow efficiency gains. No differences were observed between AI-ADC and standard ADC maps' inter-reader agreement.

conclusionOur multi-reader study demonstrated that AI-ADC maps improved prostate boundary delineation, had lower image noise, fewer distortions, and higher overall image quality compared to ADC maps. KEY POINTS: Question Can we synthesize apparent diffusion coefficient (ADC) maps with AI to achieve higher quality maps? Findings On average, readers rated quality factors of AI-ADC maps higher than ADC maps in 34.80% of cases, compared to 5.07% for ADC (p < 0.01). Clinical relevance AI-ADC maps may serve as a reliable diagnostic support tool thanks to their high quality, particularly when the acquired ADC maps include artifacts.

Indexed as

Artificial IntelligenceDiffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedProstateProstatic NeoplasmsAgedHumansMaleMiddle AgedObserver VariationRetrospective StudiesADC mapGenerative artificial intelligenceMagnetic resonance imagingProstate

Identifiers

PMID40691514
PMCPMC12712018

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.