English

Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists

Image and Video Processing 2020-10-26 v1 Computer Vision and Pattern Recognition Quantitative Methods

Abstract

While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems, based on deep learning, have proven to 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 fourteen observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard significantly increased (quadratically weighted Cohen's kappa, 0.799 vs 0.872; p=0.018). Our results show the added value of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Keywords

Cite

@article{arxiv.2002.04500,
  title  = {Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists},
  author = {Wouter Bulten and Maschenka Balkenhol and Jean-Joël Awoumou Belinga and Américo Brilhante and Aslı Çakır and Xavier Farré and Katerina Geronatsiou and Vincent Molinié and Guilherme Pereira and Paromita Roy and Günter Saile and Paulo Salles and Ewout Schaafsma and Joëlle Tschui and Anne-Marie Vos and Hester van Boven and Robert Vink and Jeroen van der Laak and Christina Hulsbergen-van de Kaa and Geert Litjens},
  journal= {arXiv preprint arXiv:2002.04500},
  year   = {2020}
}

Comments

21 pages, 5 figures