{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T10:18:32Z","timestamp":1787739512861,"version":"build-2784847793"},"reference-count":21,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"This work was supported by the Henan Provincial Medical Science and Technology Research Project, China","award":["RKX202402030"],"award-info":[{"award-number":["RKX202402030"]}]},{"name":"This work was supported by the Henan Provincial Medical Science and Technology Research Project, China","award":["ZLKFJJ20230509"],"award-info":[{"award-number":["ZLKFJJ20230509"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>RBF neural networks are widely used in gastric carcinoma prognostic models, but they face challenges including difficulty in determining the Gaussian radial basis function parameters of the hidden layer and the diversity\/ambiguity of factors affecting gastric carcinoma prognosis. The cloud model, a key tool in uncertainty theory, is adept at handling fuzziness and randomness of complex medical data by quantifying uncertainty. This study integrates the cloud model with RBF neural networks to address the aforementioned limitations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study included 11,474 gastric carcinoma patients from the SEER database and 769 from the Linzhou Centre for Disease Control and Prevention database. A new model combining a cloud model with RBF neural networks was used, where high-dimensional cloud transformations identified RBF hidden layer neurons to optimize the network structure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Comparison with conventional methods showed that the new model predicted overall survival (OS) with a C-index of 0.715. This value is not only significantly higher than that of clinical standard TNM staging (0.591) but also outperforms machine learning methods including random forest (0.614) and traditional RBF neural networks (0.632). It achieves excellent prognostic accuracy meeting the clinical criterion of good discriminative ability, even relying solely on simple clinical factors, which enhances its clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The model is a new and effective prognostic model that provides better and more accurate prognostic assessment for gastric carcinoma patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338261429605","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:40:21Z","timestamp":1773927621000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A New Cloud-Model-Based Prognostic Model for Gastric 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