This study aimed to evaluate the prognostic significance of dynamic changes in 123 I-Metaiodobenzylguanidine (MIBG) SPECT/CT imaging uptake patterns in pediatric patients with high-risk neuroblastoma (HR-NB) receiving post-consolidation anti-disialoganglioside 2 (anti-GD2) monoclonal antibody immunotherapy. We conducted a retrospective cohort study of 105 children with HR-NB who completed consolidation therapy and underwent standardized 123 I-MIBG SPECT/CT within 30 days prior to initiating anti-GD2 immunotherapy (dinutuximab beta or naxitamab). The pre-treatment CS was calculated, andΔCS (%) was defined as [(CS pre -CS post )/CS pre ]×100%. Univariate and multivariate Cox proportional hazards regression models were used to identify independent predictors of progression-free survival (PFS). Optimal CS cutoffs were determined using the Youden index. PFS was estimated via the Kaplan-Meier method; survival differences were assessed using the log-rank test. Categorical variables were compared with the chi-square test. Patients with pre-immunotherapy CS > 3 exhibited significantly inferior 1-year and 3-year PFS rates compared with those with CS ≤ 3 (65.0% ± 7.8% vs. 87.9% ± 4.0%, P < 0.001; and 21.9% ± 7.4% vs. 59.2% ± 8.1%, P < 0.001, respectively). Axial skeletal and skull metastases on baseline MIBG imaging were associated with reduced 3-year PFS ( P = 0.012), though they did not retain independent significance in multivariate analysis. Multivariate modeling confirmed that CS > 3, absence of objective remission (OR) after immunotherapy, MYCN amplification, 1p36 deletion, 11q23 deletion, and elevated serum neuron-specific enolase (NSE) were independent adverse prognostic factors for PFS (all P < 0.05). Pre-immunotherapy 123 I-MIBG CS > 3, failure to achieve OR, MYCN amplification, 1p36/11q23 deletions, and abnormal NSE levels constitute robust, clinically actionable independent predictors of poor PFS in pediatric HR-NB treated with anti-GD2 immunotherapy. Integrating semi-quantitative MIBG imaging with molecular and biochemical markers may enhance risk stratification and support individualized treatment decisions.
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Wang et al. (2026) studied this question.
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