Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction: a nationwide, multicohort, prospective study.

Autor:	Gao Y; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China., Xin L; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China., Lin H; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China., Yao B; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province, China., Zhang T; Department of Gastroenterology, Nanchong Central Hospital, Nanchong, Sichuan Province, China., Zhou AJ; Department of Gastroenterology, Lianshui People's Hospital Affiliated to Kangda College of Nanjing Medical University, Huai'an, Jiangsu Province, China., Huang S; Department of Gastroenterology, Lianshui People's Hospital Affiliated to Kangda College of Nanjing Medical University, Huai'an, Jiangsu Province, China., Wang JH; Department of Gastroenterology, The First People's Hospital of Yancheng, Yancheng, Jiangsu Province, China., Feng YD; Department of Gastroenterology, Zhongda Hospital, Southeast University, Nanjing, Jiangsu Province, China., Yao SH; Department of Gastroenterology, Yangzhong People's Hospital, Zhenjiang, Jiangsu Province, China., Guo Y; Department of Gastroenterology, Yangzhong People's Hospital, Zhenjiang, Jiangsu Province, China., Dang T; Department of Digestive Diseases, Inner Mongolia Institute of Digestive Diseases, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China., Meng XM; Department of Digestive Diseases, Inner Mongolia Institute of Digestive Diseases, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China., Yang ZZ; Digestive Endoscopy Unit, Linzhou People's Hospital, Anyang, Henan Province, China., Jia WQ; Gastrointestinal Endoscopy Center, Nanyang Second People's Hospital, Nanyang, Henan Province, China., Pang HF; Department of Gastroenterology, Digestive Endoscopy Unit, Tongliao City Hospital, Tongliao, Inner Mongolia, China., Tian XJ; Department of Gastroenterology, Xixia County People's Hospital, Nanyang, Henan Province, China., Deng B; Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, China., Wang JP; Department of Gastroenterology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi Province, China., Fan WC; Department of Gastroenterology, Digestive Endoscopy Center, The People's Hospital of Yanting City, Mianyang, Sichuan Province, China., Wang J; Department of Gastroenterology, Jinhu County People's Hospital, Huaian, Jiangsu Province, China., Shi LH; Department of Gastroenterology, the Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China., Yang GY; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu Province, China., Sun C; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China., Wang W; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China., Zang JC; Harbor Scientific Instrument, Xiangtan, Hunan, China., Li SY; Harbor Scientific Instrument, Xiangtan, Hunan, China., Shi RH; Department of Gastroenterology, Zhongda Hospital, Southeast University, Nanjing, Jiangsu Province, China., Li ZS; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China., Wang LW; Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, China. Electronic address: wangluoweimd@126.com.
Jazyk:	angličtina
Zdroj:	The lancet. Gastroenterology & hepatology [Lancet Gastroenterol Hepatol] 2023 May; Vol. 8 (5), pp. 432-445. Date of Electronic Publication: 2023 Mar 14.
DOI:	10.1016/S2468-1253(23)00004-3
Abstrakt:	Background: Oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction have a dismal prognosis, and early detection is key to reduce mortality. However, early detection depends on upper gastrointestinal endoscopy, which is not feasible to implement at a population level. We aimed to develop and validate a fully automated machine learning-based prediction tool integrating a minimally invasive sponge cytology test and epidemiological risk factors for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction before endoscopy. Methods: For this multicohort prospective study, we enrolled participants aged 40-75 years undergoing upper gastrointestinal endoscopy screening at 39 tertiary or secondary hospitals in China for model training and testing, and included community-based screening participants for further validation. All participants underwent questionnaire surveys, sponge cytology testing, and endoscopy in a sequential manner. We trained machine learning models to predict a composite outcome of high-grade lesions, defined as histology-confirmed high-grade intraepithelial neoplasia and carcinoma of the oesophagus and oesophagogastric junction. The predictive features included 105 cytological and 15 epidemiological features. Model performance was primarily measured with the area under the receiver operating characteristic curve (AUROC) and average precision. The performance measures for cytologists with AI assistance was also assessed. Findings: Between Jan 1, 2021, and June 30, 2022, 17 498 eligible participants were involved in model training and validation. In the testing set, the AUROC of the final model was 0·960 (95% CI 0·937 to 0·977) and the average precision was 0·482 (0·470 to 0·494). The model achieved similar performance to consensus of cytologists with AI assistance (AUROC 0·955 [95% CI 0·933 to 0·975]; p=0·749; difference 0·005, 95% CI, -0·011 to 0·020). If the model-defined moderate-risk and high-risk groups were referred for endoscopy, the sensitivity was 94·5% (95% CI 88·8 to 97·5), specificity was 91·9% (91·2 to 92·5), and the predictive positive value was 18·4% (15·6 to 21·6), and 90·3% of endoscopies could be avoided. Further validation in community-based screening showed that the AUROC of the model was 0·964 (95% CI 0·920 to 0·990), and 92·8% of endoscopies could be avoided after risk stratification. Interpretation: We developed a prediction tool with favourable performance for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction. This approach could prevent the need for endoscopy screening in many low-risk individuals and ensure resource optimisation by prioritising high-risk individuals. Funding: Science and Technology Commission of Shanghai Municipality. Competing Interests: Declaration of interests L-WW has received research support from the Science and Technology Commission of Shanghai Municipality (grant number 21Y31900100). J-CZ and S-YL are employees of the Harbor Scientific Instrument and received salary support. All other authors declare no competing interests. (Copyright © 2023 Elsevier Ltd. All rights reserved.)
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