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

Author:

Bulten WouterORCID,Balkenhol MaschenkaORCID,Belinga Jean-Joël Awoumou,Brilhante Américo,Çakır Aslı,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,Delahunt Brett,Samaratunga Hemamali,Grignon David J.,Evans Andrew J.,Berney Daniel M.,Pan Chin-Chen,Kristiansen Glen,Kench James G.,Oxley Jon,Leite Katia R. M.,McKenney Jesse K.,Humphrey Peter A.,Fine Samson W.,Tsuzuki Toyonori,Varma Murali,Zhou Ming,Comperat Eva,Bostwick David G.,Iczkowski Kenneth A.,Magi-Galluzzi Cristina,Srigley John R.,Takahashi Hiroyuki,van der Kwast Theo,van Boven Hester,Vink Robert,van der Laak JeroenORCID,Hulsbergen-van der Kaa Christina,Litjens GeertORCID,

Abstract

AbstractThe 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.

Funder

KWF Kankerbestrijding

Publisher

Springer Science and Business Media LLC

Subject

Pathology and Forensic Medicine

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