Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
Publication year
2021Author(s)
Source
Modern Pathology, 34, 3, (2021), pp. 660-671ISSN
Publication type
Article / Letter to editor

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Organization
Pathology
Medical Imaging
Journal title
Modern Pathology
Volume
vol. 34
Issue
iss. 3
Page start
p. 660
Page end
p. 671
Subject
Radboudumc 14: Tumours of the digestive tract RIMLS: Radboud Institute for Molecular Life Sciences; Radboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 17: Women's cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 9: Rare cancers RIMLS: Radboud Institute for Molecular Life SciencesAbstract
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.
This item appears in the following Collection(s)
- Academic publications [204887]
- Electronic publications [103214]
- Faculty of Medical Sciences [81046]
- Open Access publications [71770]
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