Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study
文献类型：期刊论文
| 作者 | Hu, Hao1,2; Gong, Lixin3,4,5; Dong, Di5,6 ; Zhu, Liang1,2; Wang, Min7; He, Jie8; Shu, Lei9; Cai, Yiling10; Cai, Shilun1,2; Su, Wei1,2
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| 刊名 | GASTROINTESTINAL ENDOSCOPY
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| 出版日期 | 2021-06-01 |
| 卷号 | 93期号:6页码:1333-+ |
| ISSN号 | 0016-5107 |
| DOI | 10.1016/j.gie.2020.11.014 |
| 通讯作者 | Zhou, Pinghong(zhou.pinghong@zs-hospital.sh.cn) ; Tian, Jie(tian@ieee.org) |
| 英文摘要 | Background and Aims: Narrow-band imaging with magnifying endoscopy (ME-NBI) has shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in diagnostic algorithms requires substantial expertise and experience. In this study, we aimed to develop a computer-aided diagnostic model for EGM (EGCM) to analyze and assist in the diagnosis of EGC under ME-NBI. Methods: A total of 1777 ME-NBI images from 295 cases were collected from 3 centers. These cases were randomly divided into a training cohort (n Z 170), an internal test cohort (ITC, n Z 73), and an external test cohort (ETC, n Z 52). EGCM based on VGG-19 architecture (Visual Geometry Group [VGG], Oxford University, Oxford, UK) with a single fully connected 2-classification layer was developed through fine-tuning and validated on all cohorts. Furthermore, we compared the model with 8 endoscopists with varying experience. Primary comparison measures included accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: EGCM acquired AUCs of.808 in the ITC and.813 in the ETC. Moreover, EGCM achieved similar predictive performance as the senior endoscopists (accuracy:.770 vs.755, P = .355; sensitivity:.792 vs.767, P = .183; specificity:.745 vs.742, P = .931) but better than the junior endoscopists (accuracy:.770 vs.728, P<.05). After referring to the results of EGCM, the average diagnostic ability of the endoscopists was significantly improved in terms of accuracy, sensitivity, PPV, and NPV (P<.05). Conclusions: EGCM exhibited comparable performance with senior endoscopists in the diagnosis of EGC and showed the potential value in aiding and improving the diagnosis of EGC by endoscopists. |
| WOS关键词 | UPPER GASTROINTESTINAL ENDOSCOPY ; DIFFERENTIAL-DIAGNOSIS ; RADIOMIC NOMOGRAM ; PREDICTION |
| WOS研究方向 | Gastroenterology & Hepatology |
| 语种 | 英语 |
| WOS记录号 | WOS:000652833900018 |
| 出版者 | MOSBY-ELSEVIER |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45185]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Zhou, Pinghong; Tian, Jie |
| 作者单位 | 1.Fudan Univ, Zhongshan Hosp, Endoscopy Ctr, 180 Fenglin Rd, Shanghai 200032, Peoples R China 2.Fudan Univ, Zhongshan Hosp, Endoscopy Res Inst, 180 Fenglin Rd, Shanghai 200032, Peoples R China 3.Northeastern Univ, Coll Med, Shenyang, Peoples R China 4.Northeastern Univ, Biol Informat Engn Sch, Shenyang, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Beijing Key Lab Mol Imagi, Beijing, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 7.Shanghai Jiao Tong Univ, Shanghai Childrens Hosp, Dept Gastroenterol Hepatol & Nutr, Shanghai, Peoples R China 8.Fudan Univ, Zhongshan Hosp, Endoscopy Ctr, Xiamen Branch, Xiamen, Peoples R China 9.1 Hosp Wuhan, Dept Gastroenterol, Wuhan, Peoples R China 10.Xiamen Univ, Dept Gastroenterol, Affiliated Dongnan Hosp, Zhangzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Hao,Gong, Lixin,Dong, Di,et al. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study[J]. GASTROINTESTINAL ENDOSCOPY,2021,93(6):1333-+. |
| APA | Hu, Hao.,Gong, Lixin.,Dong, Di.,Zhu, Liang.,Wang, Min.,...&Tian, Jie.(2021).Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study.GASTROINTESTINAL ENDOSCOPY,93(6),1333-+. |
| MLA | Hu, Hao,et al."Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study".GASTROINTESTINAL ENDOSCOPY 93.6(2021):1333-+. |
入库方式： OAI收割
来源：自动化研究所
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