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Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
Open accessAbstract
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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“multiple groups have reported on Gleason scoring predictions from prostate cancer H&E images (62)(63)(64).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [63]
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Metadata
- Authors
- Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G.
- Affiliations
- Radboud University Medical Center (NL)
- Corresponding author
- Bulten, Wouter
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 21
- Issue
- 2
- Pages
- 233-241
- ISSN
- 1470-2045 (ISSN-L)
- OpenAlex
- W2999091210
- Cited by
- 705
- References
- 39
- Citations by year
- 2026: 84 · 2025: 96 · 2024: 112 · 2023: 115 · 2022: 128 · 2021: 115 · 2020: 55
- FWCI
- 40.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Department of Health and Social Care
- KWF Kankerbestrijding (KUN 2015-7970)
- Radboud Universitair Medisch Centrum
- AbbVie
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Urology › Prostate Cancer Diagnosis and Treatment
- Keywords
- Gleason score, prostate cancer, deep learning, prostate biopsy, interobserver variability, external validation, benign versus malignant differentiation, urologic pathology, computer-aided diagnosis, recurrence prediction
- MeSH terms
- Deep Learning, Biopsy, Diagnosis, Computer-Assisted, Humans, Image Interpretation, Computer-Assisted, Male, Predictive Value of Tests, Prostatic Neoplasms, Retrospective Studies, Reproducibility of Results, Observer Variation, Automation, Laboratory, Neoplasm Grading
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