Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
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Abstract For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.
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Multiple groups have reported Gleason scoring predictions from prostate cancer H&E images.
“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 [62]
Nagpal and colleagues used a deep learning model to predict Gleason scores from TCGA H&E images and reported significantly higher diagnostic accuracy than a cohort of 29 board-certified pathologists.
“Nagpal and colleagues (62) used a deep learning model to predict Gleason scores using H&E images from The Cancer Genome Atlas (TCGA) and reported significantly higher diagnostic accuracy compared with a cohort of 29 board-certified pathologists.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [62]
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- Authors
- Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., Corrado, G. S., MacDonald, R., Peng, L. H., Amin, M. B., Evans, A. J., Sangoi, A. R., Mermel, C. H., Hipp, J. D., & Stumpe, M. C.
- Affiliations
- Google (United States) (US)
- Naval Medical Center San Diego (US)
- Henry M. Jackson Foundation (US)
- University of Tennessee Health Science Center (US)
- University Health Network (CA)
- El Camino Hospital (US)
- Corresponding author
- Mermel, Craig H.
- Stumpe, Martin C.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2019-06-07
- Volume
- 2
- Issue
- 1
- Pages
- 48-48
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6555810
- OpenAlex
- W2901612843
- Cited by
- 495
- References
- 47
- Citations by year
- 2026: 35 · 2025: 48 · 2024: 56 · 2023: 61 · 2022: 74 · 2021: 99 · 2020: 81 · 2019: 35 · 2017: 1 · 2015: 1
- FWCI
- 32.97
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-11-19)
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- 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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- Gleason score, prostate cancer, deep learning, whole slide images, prostatectomy, risk stratification, diagnostic accuracy
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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