Increases prognostic value of clinical-pathological nomogram in patients with esophageal squamous cell carcinoma

BACKGROUND: This study was intended to construct a brand new prognostic nomogram after combine clinical and pathological characteristics to increases prognostic value in patients with esophageal squamous cell carcinoma. METHODS: A total of 1,634 patients were included. Subsequently, the tumor tissues of all patients were prepared into tissue microarrays. AIPATHWELL software was employed to explore tissue microarrays and calculate the tumor-stroma ratio. X-tile was adopted to find the optimal cut-off value. Univariate and multivariate Cox analyses were used to screen out remarkable characteristics for constructing the nomogram in the total populations. A novel prognostic nomogram with clinical and pathological characteristics was constructed on the basis of the training cohort (n=1,144). What’s more performance was validated in the validation cohort (n=490). Clinical-pathological nomogram were assessed by concordance index, time-dependent receiver operating characteristic, calibration curve and decision curve analysis. RESULTS: The patients can divide into two groups with cut-off value of 69.78 for the tumor-stroma ratio. It is noteworthy that the survival difference was noticeable (P<0.001). A clinical-pathological nomogram was constructed by combining clinical and pathological characteristics to predict the overall survival. In comparison with TNM stage, the concordance index and time-dependent receiver operating characteristic of the clinical-pathological nomogram showed better predictive value (P<0.001). High quality of calibration plots in overall survival was noticed. As demonstrated by the decision curve analysis, the nomogram has better value than the TNM stage. CONCLUSIONS: As evidently revealed by the research findings, tumor-stroma ratio is an independent prognostic factor in patients with esophageal squamous cell carcinoma. The clinical-pathological nomogram has an incremental value compared TNM stage in predicting overall survival.