Evidence map›Paper›PMID 32542445›Full record

ArticleVirchows Archiv : an international journal of pathology2020

Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.

Lars Egevad, Daniela Swanberg, Brett Delahunt, Peter Ström, Kimmo Kartasalo, Henrik Olsson, Dan M Berney, David G Bostwick, Andrew J Evans, Peter A Humphrey and 16 more

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

Article in Virchows Archiv : an international journal of pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Using active surveillance for Gleason 7 (3+4) prostate cancer: A narrative review.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2024
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Human exposome assessment platform.Environmental epidemiology (Philadelphia, Pa.) · 2021
    Article
  19. Article
  20. AI reality check when evaluating difficult to grade prostate cancers.Virchows Archiv : an international journal of pathology · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors.

Lars EgevadDepartment of Oncology-Pathology, Karolinska Institutet|, Karolinska University Hospital, Radiumhemmet P1:02, 171 76, Stockholm, Sweden. lars.egevad@ki.se.ORCID http://orcid.org/0000-0001-8531-222X
Daniela SwanbergDepartment of Oncology-Pathology, Karolinska Institutet|, Karolinska University Hospital, Radiumhemmet P1:02, 171 76, Stockholm, Sweden.
Brett DelahuntDepartment of Pathology and Molecular Medicine, Wellington School of Medicine and Health sciences, University of Otago, Wellington, New Zealand.
Peter StrömDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Kimmo KartasaloDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Henrik OlssonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Dan M BerneyBarts Cancer Institute, Queen Mary University of London, London, UK.
David G BostwickBostwick Laboratories, Orlando, FL, USA.
Andrew J EvansLaboratory Medicine Program, University Health Network, Toronto, ON, Canada.
Peter A HumphreyDepartment of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Kenneth A IczkowskiDepartment of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA.
James G KenchDepartment of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and Central Clinical School, University of Sydney, Sydney, New South Wales, Australia.
Glen KristiansenInstitute of Pathology, University Hospital Bonn, Bonn, Germany.
Katia R M LeiteDepartment of Urology, Laboratory of Medical Research, University of Sao Paulo Medical School, Sao Paulo, Brazil.
Jesse K McKenneyCleveland Clinic, Pathology and Laboratory Medicine Institute, Cleveland, OH, USA.
Jon OxleyDepartment of Cellular Pathology, Southmead Hospital, Bristol, UK.
Chin-Chen PanDepartment of Pathology, Taipei Veterans General Hospital, Taipei, Taiwan.
Hemamali SamaratungaAquesta Uropathology and University of Queensland, Brisbane, Queensland, Australia.
John R SrigleyDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
Hiroyuki TakahashiDepartment of Pathology, The Jikei University School of Medicine, Tokyo, Japan.
Toyonori TsuzukiDepartment of Surgical Pathology, School of Medicine, Aichi Medical University, Nagoya, Japan.
Theo van der KwastLaboratory Medicine Program, University Health Network, Toronto, ON, Canada.
Murali VarmaDepartment of Cellular Pathology, University Hospital of Wales, Cardiff, UK.
Ming ZhouDepartment of Pathology and Laboratory Medicine, Tufts Medical Center and Tufts School of Medicine, Boston, MA, USA.
Mark ClementsDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Martin EklundDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Funding

Cancerfonden CAN 2017/270
6 · The paper itself

Abstract

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Indexed as

Artificial IntelligenceDatabases, FactualHumansImage Interpretation, Computer-AssistedMaleNeoplasm GradingObserver VariationProstatic NeoplasmsArtificial intelligenceGradingPathologyProstate cancerReproducibilityStandardization

Identifiers

PMID32542445
PMCPMC7683442

What OpenQuestion holds

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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.