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  • Bulten, WouterRadboud Univ Nijmegen, Netherlands (author)

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

  • Article/chapterEnglish2021

Publisher, publication year, extent ...

  • NATURE PUBLISHING GROUP,2021
  • electronicrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:liu-168778
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168778URI
  • https://doi.org/10.1038/s41379-020-0640-yDOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:144351123URI

Supplementary language notes

  • Language:English
  • Summary in:English

Part of subdatabase

Classification

  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • Funding Agencies|Dutch Cancer Society (KWF)KWF Kankerbestrijding [KUN 2015-7970]
  • The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohens kappa, 0.799 vs. 0.872;p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohens kappa, 0.733 vs. 0.786;p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Balkenhol, MaschenkaRadboud Univ Nijmegen, Netherlands (author)
  • Belinga, Jean-Joel AwoumouUniv Yaounde I, Cameroon (author)
  • Brilhante, AmericoSalomao Zoppi Diagnost DASA, Brazil (author)
  • Cakir, AsliIstanbul Medipol Univ, Turkey (author)
  • Egevad, LarsKarolinska Institutet (author)
  • Eklund, MartinKarolinska Institutet (author)
  • Farre, XavierPubl Hlth Agcy Catalonia, Spain (author)
  • Geronatsiou, KaterinaHop Diaconat Mulhouse, France (author)
  • Molinie, VincentAix en Provence Hosp, France (author)
  • Pereira, GuilhermeHisto Patol Cirarg & Citol, Brazil (author)
  • Roy, ParomitaTata Med Ctr, India (author)
  • Saile, GunterAbt Histopathol & Zytol, Switzerland (author)
  • Salles, PauloInst Mario Penna, Brazil (author)
  • Schaafsma, EwoutRadboud Univ Nijmegen, Netherlands (author)
  • Tschui, JoelleMed Pathol, Switzerland (author)
  • Vos, Anne-MarieRadboud Univ Nijmegen, Netherlands (author)
  • van Boven, HesterAntoni van Leeuwenhoek Hosp, Netherlands (author)
  • Vink, RobertLab Pathol East Netherlands, Netherlands (author)
  • van der Laak, JeroenLinköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Klinisk patologi,Radboud Univ Nijmegen, Netherlands(Swepub:liu)jerva26 (author)
  • Hulsbergen-van der Kaa, ChristinaLab Pathol East Netherlands, Netherlands (author)
  • Litjens, GeertRadboud Univ Nijmegen, Netherlands (author)
  • Radboud Univ Nijmegen, NetherlandsUniv Yaounde I, Cameroon (creator_code:org_t)

Related titles

  • In:Modern Pathology: NATURE PUBLISHING GROUP34, s. 660-6710893-39521530-0285

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