Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer

Dejun Zhou, Fei Tian, Xiangdong Tian, Lin Sun, Xianghui Huang, Feng Zhao, Nan Zhou, Zuoyu Chen, Qiang Zhang, Meng Yang, Yichen Yang, Xuexi Guo, Zhibin Li, Jia Liu, Jiefu Wang, Junfeng Wang, Bangmao Wang, Guoliang Zhang, Baocun Sun, Wei Zhang, Dalu Kong, Kexin Chen () and Xiangchun Li ()
Additional contact information
Dejun Zhou: Tianjin Medical University
Fei Tian: Tianjin Medical University
Xiangdong Tian: Tianjin Medical University
Lin Sun: Tianjin Medical University
Xianghui Huang: Zhejiang University Ningbo Hospital
Feng Zhao: Nanyang Hospital of Traditional Chinese Medicine
Nan Zhou: Nanyang Hospital of Traditional Chinese Medicine
Zuoyu Chen: Tianjin Medical University
Qiang Zhang: Tianjin Medical University
Meng Yang: Tianjin Medical University
Yichen Yang: Tianjin Medical University
Xuexi Guo: Tianjin First Central Hospital
Zhibin Li: People’s First Hospital of Shangqiu
Jia Liu: Tianjin Medical University
Jiefu Wang: Tianjin Medical University
Junfeng Wang: Tianjin Medical University
Bangmao Wang: Tianjin Medical University
Guoliang Zhang: Tianjin First Central Hospital
Baocun Sun: Tianjin Medical University
Wei Zhang: Wake Forest Baptist Medical Center
Dalu Kong: Tianjin Medical University
Kexin Chen: Tianjin Medical University
Xiangchun Li: Tianjin Medical University

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract 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

Date: 2020
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DOI: 10.1038/s41467-020-16777-6

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