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Intensive Systematic “Train-the-Trainer” Course as an Effective Strategy to Improve Detection of Early Gastric Cancer: A Multicenter Retrospective Study

  • Original Article
  • Published:
Journal of Gastrointestinal Surgery Aims and scope

Abstract

Background

To improve the diagnosis of early gastric cancer (EGC), a train-the-trainer (TTT) course was developed. This trial aimed to investigate whether TTT courses from trained trainers could improve trainees’ EGC detection.

Methods

In this multi-center, retrospective study, the training was carried out 8 times in one year. Clinical records one year before (“2016”), during (“2017”), and after (“2018”) the course were collected. The primary endpoint was the improvement of EGC detection rate after TTT courses.

Results

Twenty-four trainees from 17 hospitals were included in this study. A total of 123,416 esophagogastroduodenoscopy and 65,570 colonoscopy procedures were analyzed. The early gastric cancer detection rate (EDR) was 0.101% in 2016, which significantly increased to 0.338% in 2018 (p = 0.015). The early gastric cancer ratio (ECR, ratio of newly detected EGCs to all newly detected gastric cancers) in 2016 was 8.440%, which consistently increased to 11.853% and 19.778% in 2017 and 2018 (p = 0.006), respectively. In contrast, the advanced gastric cancer detection rate (ADR) was similar before, during, or after the course (p = 0.987). The 3-year EDR, ECR, and ADR in esophageal and colorectal cancer were not significantly different.

Conclusions

The systematic training course can improve EGC detection rate and may be an effective educational strategy to reduce gastrointestinal cancers mortality.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

EGC :

Early gastric cancer

TTT :

Train-the-trainer

NBI :

Narrow-band imaging

EGD :

Esophagogastroduodenoscopy

EDR :

Early cancer detection rate

ECR :

Early cancer ratio

ADR :

Advanced cancer detection rate

BLI :

Blue laser imaging

IEE :

Image-enhanced endoscopy

CE :

Chromoendoscopy

ME :

Magnifying endoscopy

AI :

Artificial intelligence

CAD :

Computer-aided diagnosis

GI :

Gastrointestinal

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Acknowledgements

We would like to express our sincere appreciation to following participating endoscopists and their effort with data recording and IEE images providing: Dr. Wen Jing, Dr. Qiao Weiguang, Dr. Yuan Xiaogang, Dr. Lin Jianjiao, Dr. Wang Zhenggen, Dr. Pan Zhaojie, Dr. Ma Yuqian, Dr. Fu Meili, Dr. Zhang Yan, Dr. Li Kuangyi, Dr. Lei Haoqiang, Dr. Han Yanfeng, Dr. Tan Xiaojun, Dr. Chen Muwei, Dr. Yang Wei, Dr. Liu Wenting, Dr. Chen Shuxian, Dr. Zhu Haishan, Dr. Shen Bingzhen, Dr. He Junhui, Dr. Fu Ya, Dr. Luo Xiaobei, Dr. Zou Xiaoli, Dr. Zhang Qingfei, and Dr. Shen Yanhua.

Funding

This work was supported by the Guangdong Science and Technology Plan Project (2022A1515011477, 2020A1414010265, 2017B020209003), the President Foundation of Nanfang Hospital, Southern Medical University (No.2018C2025), Guangzhou Basic and Applied Basic Research Fund (202102020163).This work was partially supported by the National Natural Science Funds of China (12026605) and we would like to acknowledge Pazhou Lab, Guangzhou for its support of this research.

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Author notes

  1. Luo Xiaobei and Kenshi Yao have contributed equally to this work.

Authors and Affiliations

  1. Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Avenue, Guangzhou, 510515, China

    Xiaobei Luo, Xin Lin, Bitao Lin, Chaojun Zhu, Zhenyu Chen, Aimin Li, Yin Huang, Side Liu & Zelong Han

  2. Department of Gastroenterology, Zhuhai People’s Hospital (Zhuhai Hospital Affifiliated With Jinan University), Zhuhai, Guangdong, China

    Xiaobei Luo & Side Liu

  3. Department of Endoscopy, Chikushi Hospital, Fukuoka University, Fukuoka, Japan

    Kenshi Yao

  4. Department of Gastroenterology, Shenzhen Hospital of Southern Medical University, Shenzhen, China

    Silin Huang

  5. Mechanobiology Institute, National University of Singapore, Singapore, 117411, Singapore

    Jiahao Wang

  6. Institute of Bioengineering and Nanotechnology, Agency for Science, Technology and Research (A*STAR), Singapore, 138669, Singapore

    Jiahao Wang

  7. Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China

    Zhihao Li

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  1. Xiaobei Luo
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Contributions

LXB was involved in the conception and design; analysis and interpretation of the data and drafting of the article. KY was involved in the conception and design of the study; and critical revision of the article. LX, LBT, ZCJ, and WJH was involved in interpretation of the data and drafting of the article. HSL, CZY, LAM, and HY were involved in the systematic training; analysis and interpretation of the data. LZH was involved in statistical analysis and interpretation of the data. LSD was involved in conception and design of the study; and critical revision of the article. HZL was involved in the conception and design; analysis and interpretation of the data, and revision of the article. All authors had access to the study data and reviewed and final approved the article.

Corresponding authors

Correspondence to Side Liu or Zelong Han.

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Luo, X., Yao, K., Lin, X. et al. Intensive Systematic “Train-the-Trainer” Course as an Effective Strategy to Improve Detection of Early Gastric Cancer: A Multicenter Retrospective Study. J Gastrointest Surg 27, 1303–1312 (2023). https://doi.org/10.1007/s11605-023-05640-w

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