Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer
Open accessAbstract
Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three independent datasets. At the patient-level, CRCNet achieves an area under the precision-recall curve (AUPRC) of 0.882 (95% CI: 0.828-0.931), 0.874 (0.820-0.926) and 0.867 (0.795-0.923). CRCNet exceeds average endoscopists performance on recall rate across two test sets (91.3% versus 83.8%; two-sided t-test, p < 0.001 and 96.5% versus 90.3%; p = 0.006) and precision for one test set (93.7% versus 83.8%; p = 0.02), while obtains comparable recall rate on one test set and precision on the other two. At the image-level, CRCNet achieves an AUPRC of 0.990 (0.987-0.993), 0.991 (0.987-0.995), and 0.997 (0.995-0.999). Our study warrants further investigation of CRCNet by prospective clinical trials.
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Zhou and colleagues reported CRCNet, a deep learning model for detecting colorectal cancer in endoscopy images, with high performance across three independent datasets.
“Zhou and colleagues (15) have reported on CRCNet, a deep learning model for detecting the presence of colorectal cancer within endoscopy images, with reported high performance across three independent data sets.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [15]
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References 33 · Cited by 123
- Authors
- Zhou, D., Tian, F., Tian, X., Sun, L., Huang, X., Zhao, F., Zhou, N., Chen, Z., Zhang, Q., Yang, M., Yang, Y., Guo, X., Li, Z., Liu, J., Wang, J., Wang, J., Wang, B., Zhang, G., Sun, B., Zhang, W., Kong, D., Chen, K., & Li, X.
- Affiliations
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Ningbo First Hospital (CN)
- Tianjin Medical University (CN)
- Tianjin First Center Hospital (CN)
- Shangqiu First People's Hospital (CN)
- Tianjin Medical University General Hospital (CN)
- Atrium Health Wake Forest Baptist (US)
- Corresponding author
- Chen, Kexin
- Li, Xiangchun
- ORCID
- Venue
- Nature Communications
- Published
- 2020-06-11
- Volume
- 11
- Issue
- 1
- Pages
- 2961-2961
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7289893
- OpenAlex
- W3034825116
- Cited by
- 123
- References
- 33
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 31 · 2023: 25 · 2022: 18 · 2021: 15 · 2020: 2
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81903055, 31801117)
- Funder: Program for Changjiang Scholars and Innovative Research Team in University in China Grant Reference Number: IRT_14R40
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Keywords
- colorectal cancer, optical diagnostics, colonoscopy, deep learning, computer-aided diagnosis, endoscopic image classification, colorectal cancer detection
- MeSH terms
- Machine Learning, Deep Learning, Aged, Colonoscopy, Female, Gastroenterology, Humans, Male, Middle Aged, Retrospective Studies, Colorectal Neoplasms
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