{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:47:36Z","timestamp":1792082856169,"version":"4.4.0"},"reference-count":37,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T00:00:00Z","timestamp":1676160000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1F1A1048267"],"award-info":[{"award-number":["2020R1F1A1048267"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A2C1014829"],"award-info":[{"award-number":["2020R1A2C1014829"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI18C1216"],"award-info":[{"award-number":["HI18C1216"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Personalized survival prediction is important in gastric cancer patients after gastrectomy based on large datasets with many variables including time\u2010varying factors in nutrition and body morphometry. One year after gastrectomy might be the optimal timing to predict long\u2010term survival because most patients experience significant nutritional change, muscle loss, and postoperative changes in the first year after gastrectomy. We aimed to develop a personalized prognostic artificial intelligence (AI) model to predict 5\u00a0year survival at 1\u00a0year after gastrectomy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      From a prospectively built gastric surgery registry from a tertiary hospital, 4025 gastric cancer patients (mean age 56.1\u00a0\u00b1\u00a010.9, 36.2% females) treated gastrectomy and survived more than a year were selected. Eighty\u2010nine variables including clinical and derived time\u2010varying variables were used as input variables. We proposed a multi\u2010tree extreme gradient boosting (XGBoost) algorithm, an ensemble AI algorithm based on 100 datasets derived from repeated five\u2010fold cross\u2010validation. Internal validation was performed in split datasets (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a01121) by comparing our proposed model and six other AI algorithms. External validation was performed in 590 patients from other hospitals (mean age 55.9\u00a0\u00b1\u00a011.2, 37.3% females). We performed a sensitivity analysis to analyse the effect of the nutritional and fat\/muscle indices using a leave\u2010one\u2010out method.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the internal validation, our proposed model showed AUROC of 0.8237, which outperformed the other AI algorithms (0.7988\u20130.8165), 80.00% sensitivity, 72.34% specificity, and 76.17% balanced accuracy. In the external validation, our model showed AUROC of 0.8903, 86.96% sensitivity, 74.60% specificity, and 80.78% balanced accuracy. Sensitivity analysis demonstrated that the nutritional and fat\/muscle indices influenced the balanced accuracy by 0.31% and 6.29% in the internal and external validation set, respectively. Our developed AI model was published on a website for personalized survival prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our proposed AI model provides substantially good performance in predicting 5\u00a0year survival at 1\u00a0year after gastric cancer surgery. The nutritional and fat\/muscle indices contributed to increase the prediction performance of our AI model.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.13176","type":"journal-article","created":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T23:19:35Z","timestamp":1676243975000},"page":"847-859","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Prognostic artificial intelligence model to predict 5\u00a0year survival at 1\u00a0year after gastric cancer surgery based on nutrition and body morphometry"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-1419","authenticated-orcid":false,"given":"Heewon","family":"Chung","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, College of Electronics and Information Kyung Hee University  Yongin\u2010si Gyeonggi\u2010do Republic of 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