Preoperative Risk-Stratification of Pediatric Malignant Brainstem Glioma based on a validated nomogram: A SEER based Analysis

Abstract

Purpose: Pediatric patients diagnosed with brainstem malignant gliomas (BSMGs) have a poor prognosis. Our study aimed to construct and validate a prognostic nomogram to predict the cancer‐specific survival preoperatively (CSS) rates and then a risk-stratification given to these patients.Methods: From 1998 to 2016, we extracted patients' data from Surveillance Epidemiology and End Results (SEER) database. A total of 1160 patients were enrolled and randomly divided into training and validating groups. Subsequently, the Cox regression analysis was used to screen variables. Then, the nomogram was constructed. Lastly, we calculated C-indexes and plotted calibration curves and the utility of decision curve analyses (DCAs) to assess our survival model's benefits.Result: Here, after multivariate cox regression analysis, we established four variables for constructing nomogram for CSS rates. Subsequently, the C-index, the area under the receiver operating characteristic curve, and calibration curves were used to confirm the nomogram's good performance. DCAs of the nomogram indicated that both groups obtained good 1-, 3-, and 5-year net benefits. Conclusion: The nomogram model for preoperatively predicting CSS provided a convenient and practical tool to assess pediatric patients' prognosis with BSMG.

Prediction of Cancer-Specific Survival of Brainstem Glioma in Children Based on Risk Stratification Model

“…While these differences likely reflect true predominance in these population, incidence rates may be biased towards higher reporting in non-Hispanic Whites given previous reports of their greater access to care and earlier diagnosis [31]. It was important to verify discriminatory practices using the C-index and calibration, which was examined by contrasting the compatibility between the theoretical and measured survival of patients [32][33][34][35], to confirm the nomogram and ensure that the algorithm could be deployed broadly and improve the classification accuracy. Compared to the previous staging approach, our nomogram was more effective at discriminating and predicting survival.…”