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Article: Applications of Machine Learning Models in the Prediction of Gastric Cancer risk in patients after Helicobacter pylori eradication

TitleApplications of Machine Learning Models in the Prediction of Gastric Cancer risk in patients after Helicobacter pylori eradication
Authors
Issue Date2021
PublisherWiley-Blackwell. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2036
Citation
Alimentary Pharmacology and Therapeutics, 2021, v. 53 n. 8, p. 864-872 How to Cite?
AbstractBackground: The risk of gastric cancer after Helicobacter pylori (H. pylori) eradication remains unknown. Aim: To evaluate the performances of seven different machine learning models in predicting gastric cancer risk after H. pylori eradication. Methods: We identified H. pylori-infected patients who had received clarithromycin-based triple therapy between 2003 and 2014 in Hong Kong. Patients were divided into training (n = 64 238) and validation sets (n = 25 330), 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 years after H. pylori 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. Results: During a mean follow-up of 4.7 years, 0.21% of H. pylori-eradicated 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 0.96-0.98), and was superior to conventional logistic regression (AUC 0.90, 95% CI 0.84-0.92). 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. Conclusion: Based on simple baseline patient information, machine learning model can accurately predict the risk of post-eradication gastric cancer. This model could substantially reduce the number of patients who require endoscopic surveillance.
Persistent Identifierhttp://hdl.handle.net/10722/300692
ISSN
2023 Impact Factor: 6.6
2023 SCImago Journal Rankings: 2.794
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLeung, WK-
dc.contributor.authorCheung, KS-
dc.contributor.authorLI, B-
dc.contributor.authorLaw, SYK-
dc.contributor.authorLui, TKL-
dc.date.accessioned2021-06-18T14:55:38Z-
dc.date.available2021-06-18T14:55:38Z-
dc.date.issued2021-
dc.identifier.citationAlimentary Pharmacology and Therapeutics, 2021, v. 53 n. 8, p. 864-872-
dc.identifier.issn0269-2813-
dc.identifier.urihttp://hdl.handle.net/10722/300692-
dc.description.abstractBackground: The risk of gastric cancer after Helicobacter pylori (H. pylori) eradication remains unknown. Aim: To evaluate the performances of seven different machine learning models in predicting gastric cancer risk after H. pylori eradication. Methods: We identified H. pylori-infected patients who had received clarithromycin-based triple therapy between 2003 and 2014 in Hong Kong. Patients were divided into training (n = 64 238) and validation sets (n = 25 330), 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 years after H. pylori 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. Results: During a mean follow-up of 4.7 years, 0.21% of H. pylori-eradicated 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 0.96-0.98), and was superior to conventional logistic regression (AUC 0.90, 95% CI 0.84-0.92). 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. Conclusion: Based on simple baseline patient information, machine learning model can accurately predict the risk of post-eradication gastric cancer. This model could substantially reduce the number of patients who require endoscopic surveillance.-
dc.languageeng-
dc.publisherWiley-Blackwell. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2036-
dc.relation.ispartofAlimentary Pharmacology and Therapeutics-
dc.rightsSubmitted (preprint) Version This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Accepted (peer-reviewed) Version This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.titleApplications of Machine Learning Models in the Prediction of Gastric Cancer risk in patients after Helicobacter pylori eradication-
dc.typeArticle-
dc.identifier.emailLeung, WK: waikleung@hku.hk-
dc.identifier.emailCheung, KS: cks634@hku.hk-
dc.identifier.emailLaw, SYK: slaw@hku.hk-
dc.identifier.emailLui, TKL: lkl484@hku.hk-
dc.identifier.authorityLeung, WK=rp01479-
dc.identifier.authorityCheung, KS=rp02532-
dc.identifier.authorityLaw, SYK=rp00437-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/apt.16272-
dc.identifier.pmid33486805-
dc.identifier.scopuseid_2-s2.0-85099766722-
dc.identifier.hkuros322908-
dc.identifier.volume53-
dc.identifier.issue8-
dc.identifier.spage864-
dc.identifier.epage872-
dc.identifier.isiWOS:000611844500001-
dc.publisher.placeUnited Kingdom-

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