Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre studyResearch in context
Lisha Yao,
Suyun Li,
Quan Tao,
Yun Mao,
Jie Dong,
Cheng Lu,
Chu Han,
Bingjiang Qiu,
Yanqi Huang,
Xin Huang,
Yanting Liang,
Huan Lin,
Yongmei Guo,
Yingying Liang,
Yizhou Chen,
Jie Lin,
Enyan Chen,
Yanlian Jia,
Zhihong Chen,
Bochi Zheng,
Tong Ling,
Shunli Liu,
Tong Tong,
Wuteng Cao,
Ruiping Zhang,
Xin Chen,
Zaiyi Liu
Affiliations
Lisha Yao
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
Suyun Li
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China
Quan Tao
Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Yun Mao
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
Jie Dong
Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), The Third Affiliated Hospital of Shanxi Medical University, Taiyuan, China
Cheng Lu
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
Chu Han
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
Bingjiang Qiu
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Sciences), Guangzhou, China
Yanqi Huang
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
Xin Huang
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, Shantou University Medical College, Shantou, China
Yanting Liang
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; School of Medicine, South Medical University, Guangzhou, China
Huan Lin
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
Yongmei Guo
Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
Yingying Liang
Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China
Yizhou Chen
Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
Jie Lin
Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
Enyan Chen
Department of Radiology, Puning People's Hospital, Southern Medical University, Jieyang, China
Yanlian Jia
Department of Radiology, Liaobu Hospital of Guangdong, Dongguan, China
Zhihong Chen
Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
Bochi Zheng
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
Tong Ling
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
Shunli Liu
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
Tong Tong
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
Wuteng Cao
Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
Ruiping Zhang
Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), The Third Affiliated Hospital of Shanxi Medical University, Taiyuan, China; Corresponding author. Department of Radiology, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), Shanxi Medical University, Taiyuan, 030032, China.
Xin Chen
Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China; Corresponding author. Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
Zaiyi Liu
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Corresponding author. Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Summary: Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. Methods: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists’ detection performance. Findings: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p 0.99), and it detected 2 cases that had been missed by radiologists. Interpretation: The developed DL model can accurately detect colorectal cancer and improve radiologists’ detection performance, showing its potential as an effective computer-aided detection tool. Funding: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).
