Evidence map›Paper›PMID 38989669›Full record

ArticleBJU international2025

External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

Bogdana Schmidt, Simon John Christoph Soerensen, Hriday P Bhambhvani, Richard E Fan, Indrani Bhattacharya, Moon Hyung Choi, Christian A Kunder, Chia-Sui Kao, John Higgins, Mirabela Rusu and 1 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BJU international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bogdana SchmidtDivision of Urology, Department of Surgery, Huntsman Cancer Hospital, University of Utah, Salt Lake City, UT, USA.
Simon John Christoph SoerensenDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-5763-8297
Hriday P BhambhvaniDepartment of Urology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA.
Richard E FanDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Indrani BhattacharyaDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Moon Hyung ChoiDepartment of Radiology, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Korea.ORCID https://orcid.org/0000-0001-5962-4772
Christian A KunderDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Chia-Sui KaoDepartment of Pathology and Laboratory Medicine, Cleveland Clinic, Cleveland, OH, USA.
John HigginsDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Mirabela RusuDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Geoffrey A SonnDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.

Funding

Rad-pathomic deep learning models to assist radiologists in differentiating aggressive from indolent prostate cancer on MRIR37CA260346 · NCI · STANFORD UNIVERSITY · PI Mirabela Rusu · 2022 to 2026
$2.8M
NCI NIH HHS R37 CA260346
6 · The paper itself

Abstract

objectivesTo externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size. MATERIALS AND

methodsThe commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm

resultsThe agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85.

conclusionThe DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

Indexed as

Artificial IntelligenceNeoplasm GradingProstatectomyProstatic NeoplasmsAgedAlgorithmsHumansMaleMiddle AgedProstateartificial intelligenceGleason gradingprostate cancerradical prostatectomywhole‐mount prostatectomy

Identifiers

PMID38989669
PMCPMC11628895

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Registered trials

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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.