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Lancet Oncol. 2020 Feb;21(2):233-241. doi: 10.1016/S1470-2045(19)30739-9. Epub 2020 Jan 8.

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

Author information

1
Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands. Electronic address: wouter.bulten@radboudumc.nl.
2
Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands.
3
Department of Pathology, Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, Netherlands.
4
Laboratory of Pathology East Netherlands, Hengelo, Netherlands.
5
Department of Radiology & Nuclear Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands.

Abstract

BACKGROUND:

The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater concordance; however, such expertise is not widely available. Prostate cancer diagnostics could thus benefit from robust, reproducible Gleason grading. We aimed to investigate the potential of deep learning to perform automated Gleason grading of prostate biopsies.

METHODS:

In this retrospective study, we developed a deep-learning system to grade prostate biopsies following the Gleason grading standard. The system was developed using randomly selected biopsies, sampled by the biopsy Gleason score, from patients at the Radboud University Medical Center (pathology report dated between Jan 1, 2012, and Dec 31, 2017). A semi-automatic labelling technique was used to circumvent the need for manual annotations by pathologists, using pathologists' reports as the reference standard during training. The system was developed to delineate individual glands, assign Gleason growth patterns, and determine the biopsy-level grade. For validation of the method, a consensus reference standard was set by three expert urological pathologists on an independent test set of 550 biopsies. Of these 550, 100 were used in an observer experiment, in which the system, 13 pathologists, and two pathologists in training were compared with respect to the reference standard. The system was also compared to an external test dataset of 886 cores, which contained 245 cores from a different centre that were independently graded by two pathologists.

FINDINGS:

We collected 5759 biopsies from 1243 patients. The developed system achieved a high agreement with the reference standard (quadratic Cohen's kappa 0·918, 95% CI 0·891-0·941) and scored highly at clinical decision thresholds: benign versus malignant (area under the curve 0·990, 95% CI 0·982-0·996), grade group of 2 or more (0·978, 0·966-0·988), and grade group of 3 or more (0·974, 0·962-0·984). In an observer experiment, the deep-learning system scored higher (kappa 0·854) than the panel (median kappa 0·819), outperforming 10 of 15 pathologist observers. On the external test dataset, the system obtained a high agreement with the reference standard set independently by two pathologists (quadratic Cohen's kappa 0·723 and 0·707) and within inter-observer variability (kappa 0·71).

INTERPRETATION:

Our automated deep-learning system achieved a performance similar to pathologists for Gleason grading and could potentially contribute to prostate cancer diagnosis. The system could potentially assist pathologists by screening biopsies, providing second opinions on grade group, and presenting quantitative measurements of volume percentages.

FUNDING:

Dutch Cancer Society.

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