RESUMEN
BACKGROUND: The prognostic role of lactate dehydrogenase (LDH) has been confirmed in many malignant tumors, but the role of serum LDH in primary central nervous system germ cell tumor (GCT) remains unknown. This study aimed to assess the prognostic value of LDH in GCT patients and develop a nomogram to predict prognosis in patients undergoing chemoradiotherapy. METHODS: A total of 161 patients with GCT were included in this study. Using a restricted cubic spline (RCS) model, the optimal cutoff point for LDH was determined to be 217 U/L. The survival of GCT patients was evaluated using the Kaplan-Meier method and log-rank test to analyze the effects of LDH levels. Univariate Cox regression, multivariate Cox regression, and LASSO Cox regression were conducted to identify prognostic factors, which were incorporated into a nomogram for predicting overall survival (OS). The predictive accuracy of the nomogram was assessed using the C-index, calibration curve, area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and risk group stratification. The net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). RESULTS: The high-LDH group had significantly shorter OS compared to the low-LDH group (P = 0.016). Based on the SYSUCC cohort, three variables were shown to be significant factors for OS and were incorporated in the nomogram: LDH, histopathology, and dissemination. It showed good discrimination ability, with C-index of 0.789 (95% CI, 0.671-0.907). Additionally, the clinical usefulness of the nomogram was confirmed by calibration curves and time-dependent AUC. DCA further highlighted the potential of the nomogram to guide clinical treatment strategies for patients. Moreover, there was a significant difference in OS among patients categorized into different risk groups (P < 0.001). CONCLUSION: LDH levels may serve as a reliable predictor for assessing the therapeutic effect of chemoradiotherapy in GCT. The developed nomogram exhibits high accuracy in predicting survival outcomes, aiding in the classification of prognostic groups, and supporting informed clinical decision-making.