{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T14:40:28Z","timestamp":1747147228347,"version":"3.40.5"},"reference-count":44,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Primary liver cancer (PLC) is a highly malignant disease. This study developed an effective and convenient tool to evaluate survival times of patients after hepatectomy, which can provide a reference point for clinical decisions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Clinical and laboratory data of 243 patients with PLC after hepatectomy were collected. Univariate cox regression analysis, Lasso regression analysis and multivariate cox regression analysis were used to determine the best prediction index. Multivariate cox regression analysis was used to construct a survival prediction model. A receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to verify the model. The patients in this model were divided into was divided into high\u2010risk and low\u2010risk groups according to the optimal cut\u2010off value of the ROC curve for different prognostic years. Kaplan\u2013Meier survival analysis and log\u2010rank test were used to analyse the survival differences between the two groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Tumour size, portal vein thrombosis, distant metastasis, alpha\u2010fetoprotein and protein induced by vitamin K absence or antagonist\u2010II levels were independent risk factors for overall survival (OS) after PLC surgery. The area under the concentration\u2010time curve for 2\u2010, 3\u2010 and 4\u2010year survival of patients was 0.710, 0.825 and 0.919, respectively, with a good calibration of the Hosmer\u2013Lemeshow test (<jats:italic>p<\/jats:italic>\u2009&gt;\u20090.05) and net benefit. The mortality rates in patients with 2, 3 and 4\u2009years of survival were 70.59%, 67.83% and 66.67% for the high\u2010risk group and 21.84%, 22.50% and 22.67% for the low\u2010risk group, respectively. The mortality rate of the high\u2010risk group was significantly higher than that of the low\u2010risk group (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The OS model of prognosis after PLC surgery constructed in this study can be used to predict the 2\u2010, 3\u2010 and 4\u2010year survival prognoses of patients, and patients with different prognosis years can be re\u2010stratified so that they achieve more accurate and personalised assessment, thereby providing a reference point for clinical decision\u2010making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.6031","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T09:06:23Z","timestamp":1683795983000},"page":"13329-13341","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Establishment and application of a survival rate graph model based on biomarkers 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