Abstract
BACKGROUND: Upper tract urothelial carcinoma (UTUC) is a relatively rare disease with a poor prognosis. A growing body of evidence demonstrates that inflammation and the inflammatory microenvironment play a crucial role in tumorigenesis and tumor progression. Our aim was to evaluate the prognostic value of blood inflammation markers and develop a prediction model that incorporates inflammation markers in order to predict overall survival (OS) of UTUC. METHODS: We included 304 localized UTUC patients from two medical institutions who had undergone radical nephroureterectomy (RNU) (167 in the training cohort, 137 in the validation cohort). Univariate and multivariate Cox regression analyses were performed to screen the prognostic factors, and a nomogram and a web-based calculator were generated based on these predictors. The Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. RESULTS: Independent predictors incorporated in the nomogram were pathological stage, surgical margin, albumin-to-globulin ratio (AGR), and hemoglobin-to-red cell distribution width ratio (HRR). The c-index value was 0.726 in the training cohort and 0.761 in the validation cohort. The area under the ROC of the nomogram at 1-, 3- and 5-year in the training and validation sets were 0.765, 0.755, 0.763, and 0.791, 0.833, 0.802, respectively. Both the internal and external validation calibration plots showed a subtle distinction between the predicted and the actual probabilities. And it appears to provide incremental benefits for clinical decision-making in comparison to the American Joint Committee of Cancer (AJCC) staging system. CONCLUSIONS: In patients with UTUC after RNU, lower preoperative AGR and HRR were independent predictors of inferior survival. In addition, we created a novel blood inflammation marker-based dynamic nomogram that may be useful for surgeons or oncologists in risk stratification and patient selection for more intensive therapy and closer follow-up.