Development and validation of a nomogram for predicting survival in intermediate- and high-risk neuroblastoma of the Children's Oncology Group risk stratification.

Abstract

To develop and validate a nomogram for predicting survival in intermediate- and high-risk neuroblastoma patients and to compare the accuracy of the nomogram in predicting survival with Children's Oncology Group (COG) risk stratification. A total of 885 intermediate- and high-risk neuroblastoma patients were enrolled in this study, including 243 patients from our hospital (the training set) and 642 patients from the TARGET database (the validation set). The factors related to event-free survival (EFS) and overall survival (OS) in neuroblastoma were determined to construct the nomogram by Cox regression analysis. The C-index, calibration curves, and area under the time-dependent receiver operating characteristic curves (AUCs) were used to assess the predictive performance of the nomogram. International Neuroblastoma Staging System stage and Mitosis-karyorrhexis index (MKI) were significant unfavorable factors for EFS, while MKI and MYCN status were significant unfavorable factors for OS. The C-index of the nomogram was 0.621 and 0.586 for predicting EFS, 0.650 and 0.570 for predicting OS in the training and validation sets, respectively. The calibration curves revealed good agreement in the EFS and OS predicted by the nomogram. The AUCs of the nomogram for 1-, 2-, 3-year EFS and OS were 0.633, 0.669, 0.604 and 0.672, 0.670, 0.702 in the training set, respectively. Moreover, the nomogram was able to classify patients into two groups according to risk scores, with the "high-risk" group having a lower survival rate than the "intermediate-risk" group. And the nomogram performed better than the COG risk stratification, which had a C-index of 0.537, 0.502 and 0.565, 0.572 for predicting EFS, OS in the training and validation sets, respectively. We developed and validated a prognostic nomogram for intermediate- and high-risk neuroblastoma patients that clinicians can use to make more informed decisions for individual patients.