Clinical features, treatment and prognosis analysis of distant metastatic esophageal cancer.

Esophageal cancer (EC) is one of the most common malignant tumors in China. EC is characterized by a poor clinical prognosis, with many patients being diagnosed at advanced stages. This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database. The clinical features, treatment, and prognostic factors of patients with distant metastatic EC were screened and analyzed, and a nomogram was drawn to construct a predictive model. Eligible patients with distant metastatic EC diagnosed from January 2004 to December 2015 were extracted from the SEER database. Propensity score matching (PSM) was used to eliminate group baseline differences.The data were divided into the training cohort (1116 cases) and validation cohort (426 cases) by using R software and random sampling function at the ratio of 7:3. The baseline table was plotted using Chi-square test or Fisher's exact test. Kaplan-Meier curve, log-rank test, and Cox regression were used for survival analysis. C-index and AUC were used to evaluate the performance of the prognosis model. The calibration curve was used to evaluate the calibration of the model. Using the data of the validation cohort, external validation is used to create a prediction model. After applying the inclusion and exclusion criteria and PSM, a total of 1542 cases diagnosed between 2004 and 2015 were included in the study. We analyzed the Kaplan-Meier survival of patients with metastatic EC before and after PSM, focusing on different treatment methods. The results indicated that radiotherapy, chemotherapy, and surgical treatment provided significant survival benefits to patients with metastatic EC(P < 0.05). Univariate and Multivariate regression analysis showed that T-stage, M-stage, primary site, surgery, chemotherapy, and radiotherapy were independent prognostic factors affecting the prognosis of distant metastatic EC (P < 0.05). Evaluating the predictive ability of the nomogram, the C index of the training cohort was 0.69 (95% CI 0.67-0.71), and the C-index of the validation cohort was 0.659 (95% CI 0.627-0.693)0.6606 patients met the inclusion criteria and were enrolled in the study's external validation group. In this group, the AUC values of our external validation model for 1-, 2-, and 3-year overall survival (OS) were 0.775 (95% CI 0.762-0.787), 0.790 (95% CI 0.744-0.807). The C-index was 0.726. The AUC values for both the training and validation cohorts for the 1-year OS ranged from 0.50 to 0.70, and the AUC for the rest of the training and validation cohort ranged from 0.70 to 0.90, which suggests that the model is moderately discriminating. The calibration curves of 1 year, 2 years, and 3 years in the two groups are very close to the 45° reference line, suggesting that the models exhibit a good degree of calibration. The C-index, the AUC, and calibration curves suggest that the models have good discriminating and calibration. The results reveal that the T stage, M stage, primary tumor site, surgery, chemotherapy, and radiotherapy play an important role in influencing the treatment effect and prognosis of patients. The nomogram prediction model, which is based on these independent risk factors, shows good discriminative ability and calibration.