{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T23:05:00Z","timestamp":1792019100402,"version":"4.4.0"},"reference-count":28,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"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":["J of Digest Diseases"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To establish a new and easy\u2010to\u2010use risk\u2010scoring predictive model to help identify high\u2010risk patients with multiple synchronous gastric neoplasms (MSGN), including early gastric cancer (EGC) and gastric dysplasia (GD), before initial endoscopic resection (ER).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively enrolled 1361 patients with EGC or GD who had undergone ER from November 2006 to September 2019. The patients were randomly divided into the training (n\u00a0=\u00a0681) and validation cohorts (n\u00a0=\u00a0680). In the training phase a prediction score was constructed to assess the independent predictors of MSGN based on multivariate logistic regression analysis. The performance of the prediction model was evaluated using the receiver operating characteristic (ROC) curve and the Hosmer\u2010Lemeshow test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of the 1361 patients, 122 (9.0%) had MSGN. Three predictors for MSGN were scored and weighted, as follows: elderly male (\u226565\u2009y; three points), a family history of gastric cancer (two points) and surface redness (two points). Accordingly, patients were divided into the low (risk score, 0\u20103 points) or high\u2010risk groups (risk score, 4\u20107 points). In the validation cohort, the incidence of MSGN in the low\u2010risk and high\u2010risk groups were 6.1% and 32.0%, respectively (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). Our predictive risk\u2010scoring model showed good discrimination (the area under the ROC curve [AUROC] 0.719, 95% confidence interval [CI] 0.634\u20100.794, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) and calibration ability (Hosmer\u2010Lemeshow test, \u03c7<jats:sup>2<\/jats:sup>\u00a0=\u00a06.539, <jats:italic>P<\/jats:italic>\u00a0=\u00a00.587) in the validation group.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This risk\u2010scoring model has a good performance in predicting MSGN before the initial ER.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1751-2980.13050","type":"journal-article","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T12:29:45Z","timestamp":1630758585000},"page":"637-644","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A predictive risk\u2010scoring model for multiple synchronous early gastric cancers or gastric dysplasia before initial endoscopic resection"],"prefix":"10.1111","volume":"22","author":[{"given":"Shan Shan","family":"Xu","sequence":"first","affiliation":[{"name":"Nankai University School of Medicine Nankai University  Tianjin China"},{"name":"Department of Gastroenterology and Hepatology First Medical Center of Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning Li","family":"Chai","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology First Medical Center of Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao Wei","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology and Hepatology First Medical Center of Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"En Qiang","family":"Linghu","sequence":"additional","affiliation":[{"name":"Nankai 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