Evidence map›Paper›PMID 36809483›Full record

ArticleVirchows Archiv : an international journal of pathology2023

Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies.

Catarina Eloy, Ana Marques, João Pinto, Jorge Pinheiro, Sofia Campelos, Mónica Curado, João Vale, António Polónia

Open access · hybridFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in Virchows Archiv : an international journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
26.4field-weighted citation impact, top 1% of its field
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

51 citing papers in PubMed, 1 synthesis or guideline pooled it, 102 citations in OpenAlex.

  1. Pooled it
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  6. Review
  7. Article
  8. Article
  9. Modern integrative prostate cancer diagnostics.Current opinion in urology · 2026
    Review
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  12. Article
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  20. Using artificial intelligence to prioritize pathology samples: report of a test drive.Virchows Archiv : an international journal of pathology · 2025
    Article
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

8 authors at 1 institution in 1 country.

Catarina EloyPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
Ana MarquesPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
João PintoPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
Jorge PinheiroPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
Sofia CampelosPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
Mónica CuradoPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
João ValePathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal.
António PolóniaPathology Laboratory, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal. antoniopolonia@yahoo.com.ORCID http://orcid.org/0000-0001-8312-1681
Universidade do Porto · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.

Indexed as

ProstateProstatic NeoplasmsArtificial IntelligenceBiopsyHumansMalePathologistsArtificial intelligenceComputational pathologyDigital pathologyProstate cancer

Identifiers

PMID36809483
PMCPMC10033575
OpenAlexW4321437138

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read89
Read underepoch 390

Registered trials

None linked

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.