Survival nomogram for patients with metastatic siewert type II adenocarcinoma of the esophagogastric junction: a population-based study

被引:10
作者
Chen, Kun [1 ]
Deng, Xiaofang [1 ]
Yang, Zhihao [1 ]
Yu, Dongdong [2 ]
Zhang, Xiang [1 ]
Zhang, Jiandong [1 ]
Xie, Deyao [1 ]
He, Zhifeng [1 ]
Cheng, Dezhi [1 ]
机构
[1] Wenzhou Med Univ, Affiliated Hosp 1, Dept Thorac Surg, Wenzhou, Zhejiang, Peoples R China
[2] Wenzhou Med Univ, Affiliated Hosp 1, Dept Urol, Wenzhou, Peoples R China
关键词
Nomogram; esophagogastric junction; distant metastases; prognosis; SEER database; GASTRIC CARDIA; GASTROESOPHAGEAL JUNCTION; CARCINOMA; CANCER; CLASSIFICATION; EPIDEMIOLOGY; ESOPHAGUS; REGIONS; PATTERN;
D O I
10.1080/17474124.2020.1784726
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Background The aim of this study was to construct a nomogram to predict the survival of patients with metastatic Siewert Type II adenocarcinomas of the esophagogastric junction (AEG). Methods Patients were identified using the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was performed to assess the prognostic factors. A nomogram comprising independent prognostic factors was established and evaluated using C-indexes, calibration curves, and decision curve analyses. Results In total 1616 eligible patients were enrolled. Race, age, bone metastasis, liver metastasis, lung metastasis, other metastasis sites, and distant lymph nodes metastasis were independent prognostic factors and were integrated to construct the nomogram. The nomogram had a C-index of 0.590 (95% CI: 0.569-0.611) in the training cohort and 0.569 (95% CI: 0.532-0.606) in the validation cohort. The calibration plots for the probabilities of 6-month and 1-year overall survival demonstrated there was an optimum between nomogram prediction and actual observation. Conclusion We developed and validated a nomogram to predict individual prognosis for patients with metastatic Siewert Type II AEG, and the risk stratification system based on the nomogram could effectively stratify the patients into two risk subgroups, which can help clinicians accurately predict mortality risk and recommend personalized treatment modalities.
引用
收藏
页码:757 / 764
页数:8
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