Clinical characteristics, prognosis, and nomogram for upper esophageal cancer: a SEER database analysis

Upper esophageal cancer (ESCA) is a distinct subtype of ESCA that accounts for < 10% of ESCA cases. However, its unique clinical characteristics remain unclear, and without specialized prognostic model. We aimed to clarify its unique clinical characteristics and develop a specialized prognostic model. Data for a total of 1371 upper ESCA cases and 15,434 cases of ESCA at other segments were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Compared with that of patients with ESCA at other segments, a greater proportion of patients with upper ESCA were older and female; had an abnormal marital status; had tumors at the T4 stage, N0 stage, and M0 stage; and had squamous cell carcinoma and differentiation grade II. Moreover, the prognosis of upper ESCA was significantly poorer, and the constituent ratio stratified by the above characteristics from 2004 to 2015 showed no significant changes of average annual percent change (AAPC). Cox regression analysis was used to identify independent prognostic factors. Age, sex, marital status, histologic type, grade, and T, N and M stage were included in the development of the nomogram. The C-indexes of the training cohort and validation cohort were 0.64 (95% CI 0.62-0.66) and 0.62 (95% CI 0.58-0.64), respectively. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) results confirmed the good performance of the upper ESCA model. The C-index, integrated discrimination improvement (IDI), net reclassification improvement (NRI), time-dependent AUC, and DCA and survival analysis results confirmed that the upper ESCA model performed better than the TNM model in predicting the prognosis of upper ESCA. Finally, compared with the total ESCA model, which is based on a total of 16,805 ESCA cases, the upper ESCA model showed better performance in predicting the prognosis of upper ESCA. In conclusion, we outlined the unique clinical characteristics of upper ESCA and developed a specialized prognostic model that exhibited better performance in predicting the prognosis of upper ESCA than did the TNM model and total ESCA model.