Prognostic impact of examined lymph-node count for patients with esophageal cancer: development and validation prediction model

Abstract

Abstract Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97–0.99, P < 0.01; testing cohort: HR = 0.98, CI = 0.98–0.99, P < 0.01) and the cut-off point was 18 (training cohort: P < 0.01; testing cohort: P < 0.01). We developed the RSF model with high prediction accuracy (AUC: training cohort: 87.5; testing cohort: 79.3) and low Brier Score (training cohort: 0.122; testing cohort: 0.152). The ELNs count beyond 18 is associated with better overall survival. The RSF model has preferable clinical capability in terms of individual prognosis assessment in patients after radical esophagectomy.