Evidence map›Paper›PMID 39232875›Full record

ArticleEuropean urology oncology2025

Comparison of Pathologist and Artificial Intelligence-based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy.

Lia D Oliveira, Jiayun Lu, Eric Erak, Adrianna A Mendes, Oluwademilade Dairo, Onur Ertunc, Ibrahim Kulac, Javier A Baena-Del Valle, Tracy Jones, Jessica L Hicks and 11 more

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

Article in European urology oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
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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

21 authors.

Lia D OliveiraDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jiayun LuJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Eric ErakDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Adrianna A MendesDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Oluwademilade DairoDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Onur ErtuncSuleyman Demirel University School of Medicine, Isparta, Turkey.
Ibrahim KulacKoç University School of Medicine, Istanbul, Turkey.
Javier A Baena-Del ValleFundacion Santa Fe de Bogota University Hospital, Bogota, Colombia.
Tracy JonesDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jessica L HicksDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Stephanie GlavarisDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Gunes GunerFaculty of Medicine, Hacettepe University, Ankara, Turkey.
Igor D VidalDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Bruce J TrockDepartment of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Uttara JoshiAIRA Matrix Private Limited, Mumbai, India.
Chaith KondraguntaAIRA Matrix Private Limited, Mumbai, India.
Saikiran BonthuAIRA Matrix Private Limited, Mumbai, India.
Corinne JoshuJohns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Nitin SinghalAIRA Matrix Private Limited, Mumbai, India.
Angelo M De MarzoDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Tamara L LotanDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: tlotan1@jhmi.edu.

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI Rosalie Wollett · 1985 to 2026
$208.6M
TRANSGENIC MODELS FOR PROSTATE CANCER AND AUTOIMMUNITYP50CA058236 · NCI · JOHNS HOPKINS UNIVERSITY · PI DENMEADE, SAMUEL R · 1992 to 2020
$43.9M
Multidiciplinary Integrative Genomic Approach to Distinguish Lethal from Indolent Prostate Cancer in Men of Europena and African AncestryU01CA196390 · NCI · JOHNS HOPKINS UNIVERSITY · PI DE MARZO, ANGELO MICHAEL, PIENTA, KENNETH J. · 2015 to 2020
$5.8M
NCI NIH HHS P30 CA006973NCI NIH HHS P50 CA058236NCI NIH HHS U01 CA196390NIMHD NIH HHS L32 MD000982
6 · The paper itself

Abstract

Gleason grade group (GG) is the most powerful prognostic variable in localized prostate cancer; however, interobserver variability remains a challenge. Artificial intelligence algorithms applied to histopathologic images standardize grading, but most have been tested only for agreement with pathologist GG, without assessment of performance with respect to oncologic outcomes. We compared deep learning-based and pathologist-based GGs for an association with metastatic outcome in three surgical cohorts comprising 777 unique patients. A digitized whole slide image of the representative hematoxylin and eosin-stained slide of the dominant tumor nodule was assigned a GG by an artificial intelligence-based grading algorithm and was compared with the GG assigned by a contemporary pathologist or the original pathologist-assigned GG for the entire prostatectomy. Harrell's C-indices based on Cox models for time to metastasis were compared. In a combined analysis of all cohorts, the C-index for the artificial intelligence-assigned GG was 0.77 (95% confidence interval [CI]: 0.73-0.81), compared with 0.77 (95% CI: 0.73-0.81) for the pathologist-assigned GG. By comparison, the original pathologist-assigned GG for the entire case had a C-index of 0.78 (95% CI: 0.73-0.82). PATIENT SUMMARY: Artificial intelligence-enabled prostate cancer grading on a single slide was comparable with pathologist grading for predicting metastatic outcome in men treated by radical prostatectomy, enabling equal access to expert grading in lower resource settings.

Indexed as

Artificial IntelligencePathologistsProstatectomyProstatic NeoplasmsAgedHumansMaleMiddle AgedNeoplasm GradingNeoplasm MetastasisPrognosisArtificial intelligenceDeep learningGrade groupMetastasisProstate

Identifiers

PMID39232875
PMCPMC11841679

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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.