Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study

Yao, Lisha1,2,3; Li, Suyun1,3,4; Tao, Quan5; Mao, Yun6; Dong, Jie7; Lu, Cheng1,3,8; Han, Chu1,3,8; Qiu, Bingjiang1,3,9; Huang, Yanqi1,3; Huang, Xin1,3,10; Liang, Yanting1,3,4; Lin, Huan1,2,3; Guo, Yongmei11; Liang, Yingying11; Chen, Yizhou12; Lin, Jie12; Chen, Enyan12; Jia, Yanlian13; Chen, Zhihong14; Zheng, Bochi15; Ling, Tong1,3; Liu, Shunli16; Tong, Tong17,18; Cao, Wuteng19; Zhang, Ruiping7,20; Chen, Xin11,21; Liu, Zaiyi1,2,3

2024-06-01

10.1016/j.ebiom.2024.105183

EBIOMEDICINE   影响因子和分区

2352-3964

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 < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real -world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, 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.

SCI

英语

其他

National Science Fund for Distinguished Young Scholars of China[81925023] ; Regional Innovation and Development Joint Fund of National Natural Science Foundation of China[U22A20345] ; National Natural Science Foundation of China["82072090","82371954"] ; Guangdong Provincial Key Laboratory of Arti fi cial Intelligence in Medical Image Analysis and Application[2022B1212010011] ; High-level Hospital Construction Project[DFJHBF202105]

General & Internal Medicine ; Research & Experimental Medicine

Medicine, General & Internal ; Medicine, Research & Experimental

WOS:001252787700001

ELSEVIER

Web of Science

1.Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Radiol, Guangzhou 510080, Peoples R China
2.South China Univ Technol, Sch Med, Guangzhou, Peoples R China
3.Guangdong Prov Key Lab Artificial Intelligence Med, Guangzhou, Peoples R China
4.South Med Univ, Sch Med, Guangzhou, Peoples R China
5.Southern Med Univ, Zhujiang Hosp, Dept Rehabil Med, Guangzhou, Peoples R China
6.Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China
7.Shanxi Med Univ, Shanxi Bethune Hosp, Affiliated Hosp 3, Shanxi Acad Med Sci,Dept Radiol, Taiyuan, Peoples R China
8.Southern Med Univ, Guangdong Prov Peoples Hosp, Med Res Inst, Guangdong Acad Med Sci, Guangzhou, Peoples R China
9.Guangdong Prov Peoples Hosp, Guangdong Acad Sci, Guangdong Cardiovasc Inst, Guangzhou, Peoples R China
10.Shantou Univ, Med Coll, Sch Med, Shantou, Peoples R China
11.South China Univ Technol, Guangzhou Peoples Hosp 1, Dept Radiol, Guangzhou, Peoples R China
12.Southern Med Univ, Puning Peoples Hosp, Dept Radiol, Jieyang, Peoples R China
13.Liaobu Hosp Guangdong, Dept Radiol, Dongguan, Peoples R China
14.Guangzhou Univ, Inst Comp Sci & Technol, Guangzhou, Peoples R China
15.Southern Univ Sci & Technol, Dept Biomed Engn, Shenzhen, Peoples R China
16.Qingdao Univ, Affiliated Hosp, Dept Radiol, Qingdao, Peoples R China
17.Fudan Univ, Shanghai Canc Ctr, Dept Radiol, Shanghai, Peoples R China
18.Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China
19.Sun Yat sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Peoples R China
20.Shanxi Med Univ, Shanxi Bethune Hosp, Shanxi Acad Med Sci, Dept Radiol, Taiyuan 030032, Peoples R China
21.South China Univ Technol, Guangzhou Peoples Hosp 1, Sch Med, Dept Radiol, Guangzhou 510180, Peoples R China

Yao, Lisha,Li, Suyun,Tao, Quan,et al. Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study[J]. EBIOMEDICINE,2024,104.

Yao, Lisha.,Li, Suyun.,Tao, Quan.,Mao, Yun.,Dong, Jie.,...&Liu, Zaiyi.(2024).Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.EBIOMEDICINE,104.

Yao, Lisha,et al."Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study".EBIOMEDICINE 104(2024).