Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 813
- Type
- D - Journal article
- DOI
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10.1371/journal.pmed.1002730
- Title of journal
- PLoS Medicine
- Article number
- e1002730
- First page
- -
- Volume
- 16
- Issue
- 1
- ISSN
- 1549-1277
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
17
- Research group(s)
-
-
- Citation count
- 80
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Reports study that applies latest artificial intelligence techniques to predict colorectal cancer patient survival. To ensure reproducibility, data and software were released open-source via Zenodo. Published in PLOS Medicine (acceptance 15%), output is widely cited in top journals (Nature Reviews Clinical Oncology, Nature Reviews Nephrology, Nature Cancer, Nature Communications) and also patent WO2020185660A1. This report contributed to JN Kather being awarded German Cancer Society Science Prize and Theodor Frerichs Prize.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
