Chao Zhu1,2, Yunhong You2, Shichao Liu2, Youxin Ji2, Jinming Yu3. 1. Clinical Medical College, Shandong University, Jinan, China. 2. Department of Oncology, Qingdao Central Hospital, Qingdao, China. 3. Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, China, sdyujinming@163.com.
Abstract
BACKGROUND: Distant metastasis of esophageal cancer (EC) is prone to be neglected, so it is necessary to screen out the high-risk population for more sensitive and rigorous pretreatment imaging evaluations. OBJECTIVE: The aim of this study was to evaluate the risk factors for distant metastasis in patients with EC and to construct a clinical nomogram. METHODS: Eligible patients diagnosed from 2010 to 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. Multivariable logistic regression analysis was applied to establish a prediction nomogram. Discrimination, calibration, clinical usefulness, and reproducibility were assessed by C-index, receiver-operating characteristic curve/the area under the curve (AUC), calibration plot, decision curve analysis (DCA), and bootstrapping validation. DCA was also used to compare the novel model with the conventional predictive methods. RESULTS: A total of 9,026 patients were included for analysis. The nomogram incorporated the predictors: age, sex, race, grade, T stage, N stage, histology, tumor location, and pathological grading. The prediction model presented good discrimination with an AUC of 0.738 and a concordance index of 0.747 (95% confidence interval: 0.734-0.760), which was confirmed to be 0.745 through bootstrapping validation. Calibration plot and DCA showed satisfactory calibration and good net benefit, respectively. Comparing with the conventional prediction methods, the nomogram yielded superior net benefit. CONCLUSIONS: We constructed and validated a novel nomogram to help clinicians access the risk of distant metastasis in patients with EC.
BACKGROUND: Distant metastasis of esophageal cancer (EC) is prone to be neglected, so it is necessary to screen out the high-risk population for more sensitive and rigorous pretreatment imaging evaluations. OBJECTIVE: The aim of this study was to evaluate the risk factors for distant metastasis in patients with EC and to construct a clinical nomogram. METHODS: Eligible patients diagnosed from 2010 to 2015 were selected from the Surveillance, Epidemiology and End Results (SEER) database. Multivariable logistic regression analysis was applied to establish a prediction nomogram. Discrimination, calibration, clinical usefulness, and reproducibility were assessed by C-index, receiver-operating characteristic curve/the area under the curve (AUC), calibration plot, decision curve analysis (DCA), and bootstrapping validation. DCA was also used to compare the novel model with the conventional predictive methods. RESULTS: A total of 9,026 patients were included for analysis. The nomogram incorporated the predictors: age, sex, race, grade, T stage, N stage, histology, tumor location, and pathological grading. The prediction model presented good discrimination with an AUC of 0.738 and a concordance index of 0.747 (95% confidence interval: 0.734-0.760), which was confirmed to be 0.745 through bootstrapping validation. Calibration plot and DCA showed satisfactory calibration and good net benefit, respectively. Comparing with the conventional prediction methods, the nomogram yielded superior net benefit. CONCLUSIONS: We constructed and validated a novel nomogram to help clinicians access the risk of distant metastasis in patients with EC.