Prognostic nomogram models for elderly patients with differentiated thyroid carcinoma: A population-based study

老年分化型甲状腺癌患者预后列线图模型:一项基于人群的研究

阅读:1

Abstract

This study aimed to develop and validate a prognostic model for elderly patients with differentiated thyroid carcinoma (DTC) based on various demographic and clinical parameters in order to accurately predict patient outcomes. Patients who were diagnosed with DTC and were over 55 years old between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The patients were then randomly divided into a training set and a validation set in a 7:3 ratio, and patients from our center were included as an external validation group. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent prognostic factors, which were then utilized to develop nomograms for predicting the prognosis. The discriminative ability of the nomograms was evaluated using the concordance index, and the calibration was assessed using calibration plots. The clinical usefulness and benefits of the predictive models were determined through decision curve analysis. The findings of the stepwise Cox regression analysis revealed that several variables, including age, marital status, sex, multifocality, T stage, N stage, and M stage, were significantly associated with overall survival in elderly patients with DTC. Additionally, age, tumor size, multifocality, T stage, N stage, and M stage were identified as the primary determinants of cancer specific survival in elderly patients with DTC. Using these predictors, nomograms were constructed to estimate the probability of overall survival and cancer specific survival. The nomograms demonstrated a high level of predictive accuracy, as evidenced by the concordance index, and the calibration plots indicated that the predicted outcomes were consistent with the actual outcomes. Furthermore, the decision curve analysis demonstrated that the nomograms provided substantial clinical net benefit, indicating their utility in clinical practice.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。