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Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists.
Bulten, Wouter; Balkenhol, Maschenka; Belinga, Jean-Joël Awoumou; Brilhante, Américo; Çakir, Asli; Egevad, Lars; Eklund, Martin; Farré, Xavier; Geronatsiou, Katerina; Molinié, Vincent; Pereira, Guilherme; Roy, Paromita; Saile, Günter; Salles, Paulo; Schaafsma, Ewout; Tschui, Joëlle; Vos, Anne-Marie; van Boven, Hester; Vink, Robert; van der Laak, Jeroen; Hulsbergen-van der Kaa, Christina; Litjens, Geert.
Afiliación
  • Bulten W; Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. wouter.bulten@radboudumc.nl.
  • Balkenhol M; Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Belinga JA; Department of Morphological Sciences and Anatomic Pathology Faculty of Medicine and Biomedical Sciences, University of Yaounde 1, Yaounde, Cameroon.
  • Brilhante A; Salomão Zoppi Diagnostics/DASA, São Paulo, Brazil.
  • Çakir A; Pathology Department, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.
  • Egevad L; Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
  • Eklund M; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Farré X; Department of Health, Public Health Agency of Catalonia, Lleida, Catalonia, Spain.
  • Geronatsiou K; Centre de Pathologie, Hopital Diaconat Mulhouse, Mulhouse, France.
  • Molinié V; Pathology department, Aix en Provence Hospital, Aix-en-Provence, France.
  • Pereira G; Histo Patologia Cirúrgica e Citologia, João Pessoa-PB, Brazil.
  • Roy P; Department of Pathology, Tata Medical Center, Kolkata, India.
  • Saile G; Iabor team w ag, Abteilung für Histopathologie und Zytologie, Goldach SG, Switzerland.
  • Salles P; Instituto Mário Penna, Belo Horizonte, Brazil.
  • Schaafsma E; Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Tschui J; Medics Pathologie, Bern, Switzerland.
  • Vos AM; Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • van Boven H; Department of Pathology, Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Vink R; Laboratory of Pathology East Netherlands, Hengelo, The Netherlands.
  • van der Laak J; Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Hulsbergen-van der Kaa C; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
  • Litjens G; Laboratory of Pathology East Netherlands, Hengelo, The Netherlands.
Mod Pathol ; 34(3): 660-671, 2021 03.
Article en En | MEDLINE | ID: mdl-32759979
ABSTRACT
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 Cohen's 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 Cohen's 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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Interpretación de Imagen Asistida por Computador / Diagnóstico por Computador / Patólogos / Aprendizaje Profundo / Microscopía Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans / Male Idioma: En Revista: Mod Pathol Asunto de la revista: PATOLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Interpretación de Imagen Asistida por Computador / Diagnóstico por Computador / Patólogos / Aprendizaje Profundo / Microscopía Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans / Male Idioma: En Revista: Mod Pathol Asunto de la revista: PATOLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos
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