A novel nomogram for predicting post-recurrence survival in recurrent neuroblastoma patients

Patients with recurrent neuroblastoma (NB) have a broad range of prognoses. This research aimed to de-velop a nomogram to assess post-recurrence survival (PRS) in patients with recurrent neuroblastoma. The TARGET database was utilized to enroll 825 individuals diagnosed with neuroblastoma between 1986 and 2012, 250 of whom were diagnosed with recurrent NB. These patients were randomly divided into a training group (n = 175) and a validation group (n = 75) at a ratio of 7:3. The Kaplan-Meier method was used for survival analysis. A prognosis no-mogram was constructed based on post-recurrence survival indicators identified through Cox regression and LASSO analysis. The nomogram's capability for classification and calibration was assessed using the calibration curve, the area under the time-dependent receiver operating characteristic curve (AUC), and the consistency index (C-index). The nomogram was verified in the validation cohort, and its clinical applicabilities were assessed using the decision curve analysis (DCA). Four PRS predictors, COG risk group, INSS stage, MYCN status, and age, were identified to construct the nomogram, which showed good discrimination and calibration in the training and validation sets. The C-index of the training and validation sets was 0.681 [95% confidence interval (CI), 0.632-0.730] and 0.666 [95% CI, 0.593-0.739], respectively. The nomogram's AUC values for the training and validation sets at 1, 3, and 5 years were 0.747, 0.775, and 0.782 vs. 0.721, 0.757, and 0.776. The nomogram's AUC values were consistently higher than those of the COG risk groups and INSS stage, indicating that the nomogram had superior differentiation com-pared to the INSS stage and COG risk group. The DCA curve also demonstrated that the nomogram we developed outperformed conventional COG risk groups and INSS stage regarding clinical advantage. In the present study, we developed and validated a novel nomogram that should facilitate more accurate and personalized assessment of the survival probability of children with relapsed neuroblastoma. This model should assist physicians in their clinical decision-making process.