{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T23:30:23Z","timestamp":1785713423153,"version":"3.56.0"},"reference-count":30,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T00:00:00Z","timestamp":1611446400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Aliment Pharmacol Ther"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:title>Summary<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The risk of gastric cancer after <jats:italic>Helicobacter pylori<\/jats:italic> (<jats:italic>H. pylori<\/jats:italic>) eradication remains unknown.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>To evaluate the performances of seven different machine learning models in predicting gastric cancer risk after <jats:italic>H. pylori<\/jats:italic> eradication.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We identified <jats:italic>H. pylori<\/jats:italic>\u2010infected patients who had received clarithromycin\u2010based triple therapy between 2003 and 2014 in Hong Kong. Patients were divided into training (n\u00a0=\u00a064\u00a0238) and validation sets (n\u00a0=\u00a025\u00a0330), according to period of eradication therapy. The data were used to construct seven machine learning models to predict risk of gastric cancer development within 5\u00a0years after <jats:italic>H. pylori<\/jats:italic> eradication. A total of 26 clinical variables were input into these models. The performances were measured by the area under receiver operating characteristic curve (AUC) analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>During a mean follow\u2010up of 4.7\u00a0years, 0.21% of <jats:italic>H. pylori<\/jats:italic>\u2010eradicated patients developed gastric cancer. Of the seven machine learning models, extreme gradient boosting (XGBoost) had the best performance in predicting cancer development (AUC 0.97, 95%CI 0.96\u20100.98), and was superior to conventional logistic regression (AUC 0.90, 95% CI 0.84\u20100.92). With the XGBoost model, the number of patients considered at high risk of gastric cancer was 6.6%, with miss rate of 1.9%. Patient age, presence of intestinal metaplasia, and gastric ulcer were the heavily weighted factors used by the XGBoost.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Based on simple baseline patient information, machine learning model can accurately predict the risk of post\u2010eradication gastric cancer. This model could substantially reduce the number of patients who require endoscopic surveillance.<\/jats:p><\/jats:sec>","DOI":"10.1111\/apt.16272","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T17:45:21Z","timestamp":1693590321000},"page":"864-872","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Applications of machine learning models in the prediction of gastric cancer risk in patients after <i>Helicobacter pylori<\/i> eradication"],"prefix":"10.1111","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5993-1059","authenticated-orcid":false,"given":"Wai K.","family":"Leung","sequence":"first","affiliation":[{"name":"Department of Medicine University of Hong Kong Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ka Shing","family":"Cheung","sequence":"additional","affiliation":[{"name":"Department of Medicine University of Hong Kong Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8619-5210","authenticated-orcid":false,"given":"Bofei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Surgery University of Hong Kong Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon Y. K.","family":"Law","sequence":"additional","affiliation":[{"name":"Department of Surgery University of Hong Kong Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas K. L.","family":"Lui","sequence":"additional","affiliation":[{"name":"Department of Medicine University of Hong Kong Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,24]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"World Health Organization.Cancer fact sheets: stomach cancer.https:\/\/gcoiarcfr\/today\/fact\u2010sheets\u2010cancers?cancer=5&type=0&sex=0. Accessed 27 July 2020."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2016.01.028"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2018.03.028"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.291.2.187"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.4251\/wjgo.v10.i5.115"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djx267"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2017-314605"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc19-0437"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1158\/1055-9965.EPI-19-1044","article-title":"Statins Were Associated with a Reduced Gastric Cancer Risk in Patients with Eradicated Helicobacter Pylori Infection: A Territory\u2010Wide Propensity Score Matched Study","volume":"29","author":"Cheung KS","year":"2020","journal-title":"Cancer Epidem Biomar"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2018-317556"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.2017.7"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.29705"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2014.07.043"},{"issue":"76","key":"e_1_2_9_15_1","first-page":"e2","article-title":"Application of Artificial Intelligence to Gastroenterology and Hepatology","volume":"158","author":"Le Berre C","year":"2020","journal-title":"Gastroenterology"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-00029-9_4"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2019.09.009"},{"key":"e_1_2_9_18_1","article-title":"Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check\u2010up data: A case\u2010control study","volume":"9","author":"Taninaga J","year":"2019","journal-title":"Sci Rep\u2010Uk"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8321942"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djy144"},{"key":"e_1_2_9_21_1","unstructured":"SurajS AkshayvarunS VenkateshB.Training Sparse Neural Networks. arXiv Website.https:\/\/arxiv.org\/abs\/1611.06694.pdf. Accessed 27 July 2020."},{"key":"e_1_2_9_22_1","unstructured":"LeeJH ShiZ GaoZ.On LASSO for Predictive Regression. arXiv Website.https:\/\/arxiv.org\/abs\/1810.03140.pdf. Accessed 27 July 2020."},{"key":"e_1_2_9_23_1","volume-title":"Machine learning: a probabilistic perspective","author":"Kelvin PM","year":"2012"},{"key":"e_1_2_9_24_1","unstructured":"ChenY TryphonTG AllenRT.Probabilistic Kernel Support Vector Machines. arXiv Website.https:\/\/arxiv.org\/abs\/1904.06762.pdf. Accessed 27 July 2020."},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X20909688"},{"key":"e_1_2_9_26_1","unstructured":"ChenT GuestrinC.XGBoost: a scalable tree boosting system. arXiv Website.https:\/\/arxiv.org\/pdf\/1603.02754.pdf. Accessed March 24 2019."},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(01)00065-2"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1055\/a-0859-1883"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2019.12.003"},{"key":"e_1_2_9_30_1","article-title":"The Colombian chemoprevention trial. Twenty\u2010year follow\u2010up of a cohort of patients with gastric precancerous lesions","volume":"18","author":"Piazuelo MB","year":"2020","journal-title":"Gastroenterology"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1097\/MCG.0000000000000406"}],"container-title":["Alimentary Pharmacology &amp; Therapeutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/apt.16272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T17:45:44Z","timestamp":1693590344000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/apt.16272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,24]]},"references-count":30,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["10.1111\/apt.16272"],"URL":"https:\/\/doi.org\/10.1111\/apt.16272","archive":["Portico"],"relation":{},"ISSN":["0269-2813","1365-2036"],"issn-type":[{"value":"0269-2813","type":"print"},{"value":"1365-2036","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,24]]},"assertion":[{"value":"2020-10-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-10","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}