A multiparameter diagnostic model based on 2-[18F]FDG PET/CT metabolic parameters and clinical variables can differentiate high-risk and non-high-risk pediatric neuroblastoma under the revised Children's Oncology Group classification system.

Abstract

Background: It is crucial to assist neuroblastoma (NB) pediatric patients in accurate risk stratification based on the revised Children's Oncology Group (COG) classification system through non-invasive examinations. This study assessed the diagnostic efficacy of integrating multiparametric 2-[¹⁸F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) metabolic parameters with clinical variables to differentiate between high- and non-high-risk pediatric NB according to the revised COG classification system.

Methods: A retrospective study was conducted involving a total of 89 pediatric NB patients, including 71 high-risk and 18 non-high-risk patients, who underwent pre-treatment 2-[¹⁸F]FDG PET/CT imaging. All patients were confirmed by pathology, and clinical variables were collected. The metabolic parameters of 2-[¹⁸F]FDG PET/CT were evaluated, including maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The differences in diagnostic efficacy were evaluated by comparing the differences between receiver operating characteristic (ROC) curves. The DeLong test, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were utilized to assess the enhancement in diagnostic performance. The clinical utility of the diagnostic model was evaluated through decision curve analysis (DCA).

Results: The ROC curve analysis of TLG showed the highest differentiating diagnostic value [sensitivity =0.620, 95% confidence interval (CI): 0.496-0.730; specificity =0.833, 95% CI: 0.577-0.956; area under the curve (AUC) 0.764, 95% CI: 0.648-0.881; cut-off =234.70] among metabolic parameters of 2-[¹⁸F]FDG PET/CT. After multivariate forward stepwise logistic regression (LR) analysis, the combined diagnostics model of age, gender, the International Neuroblastoma Risk Group Staging System (INRGSS) stage (L1/L2 vs. M/MS) and TLG resulted in the highest AUC of 0.932 (95% CI: 0.867-0.998; sensitivity =0.901, 95% CI: 0.802-0.956; specificity =0.889, 95% CI: 0.604-0.978). Compared to TLG, the diagnostic efficiency of the model demonstrated a significant improvement [Z=3.089, P<0.001; IDI =0.388, P<0.001; NRI (categorical) =0.736, P<0.001]. The DCA further validated the clinical efficacy of the model.

Conclusions: The multiparameter diagnosis model based on 2-[¹⁸F]FDG PET/CT metabolic parameters and clinical parameters had excellent value in the differential diagnosis of high- and non-high-risk pediatric NB under the revised COG classification system.