列线图
医学
印戒细胞癌
内科学
比例危险模型
接收机工作特性
监测、流行病学和最终结果
一致性
单变量
多元分析
肿瘤科
TNM分期系统
流行病学
癌症
腺癌
多元统计
肿瘤分期
癌症登记处
数学
统计
作者
Xiaoxiao Shao,Xichen Li,Zi-Jian Lin,Zi-Jian Lin,Zi-Jian Lin,Weizhong Wang,He Huang
出处
期刊:Digestive Diseases
[S. Karger AG]
日期:2024-02-09
卷期号:: 1-9
被引量:2
摘要
<b><i>Introduction:</i></b> The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC). <b><i>Methods:</i></b> A total of 3,408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot, and area under the receiver operating characteristic curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort. <b><i>Results:</i></b> We identified age, tumor lymph node metastasis (TNM) staging system, surgery, and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751–0.774) and 0.766 (95% CI: 0.748–0.784) and a C-index of CSS of 0.765 (95% CI: 0.753–0.777) and 0.773 (95% CI: 0.755–0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets. <b><i>Conclusion:</i></b> The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.
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