Application of pretreatment (18)F-FDG PET metabolic parameters in children with Langerhans cell histiocytosis

(18)F-FDG PET代谢参数在朗格汉斯细胞组织细胞增生症患儿中的应用

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Abstract

To evaluate whether pre-treatment (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) metabolic parameters can predict cfBRAF V600E mutation status and prognosis in pediatric Langerhans cell histiocytosis (LCH). We retrospectively analyzed 64 newly diagnosed pediatric LCH patients who underwent pre-treatment (18)F-FDG PET/CT between September 2020 and March 2024. Metabolic parameters were measured, including maximum standardized uptake value (SUVmax), tumor-to-liver standardized uptake value ratio (SUVR(liver)), tumor-to-bone marrow standardized uptake value ratio (SUVR(BM)), total metabolic tumor volume (tMTV), and total lesion glycolysis (tTLG). Associations between metabolic parameters and cfBRAF V600E mutation status were assessed using ROC curve analysis. PFS was evaluated using Kaplan-Meier analysis and Cox proportional hazards regression. The cfBRAF V600E mutation was detected in 22 patients (34%). All metabolic parameters were significantly higher in mutation-positive patients (all P < 0.05). SUVR(liver) demonstrated the highest diagnostic performance (AUC 0.806, optimal cut-off 6.9), followed by tMTV (AUC 0.792, optimal cut-off 6.2 cm(3)). Multivariate logistic regression identified three independent predictors of mutation status: younger age (odds ratio [OR] = 0.595, P = 0.001), higher tMTV (OR = 26.760, P = 0.001), and advanced clinical stage (OR = 7.199, P = 0.005). During a median follow-up of 25.2 months, tMTV was the strongest independent predictor of PFS in multivariate Cox regression analysis. Patients with tMTV ≥ 14.66 cm(3) had significantly worse outcomes than those with lower values (log-rank P < 0.001). Pre-treatment (18)F-FDG PET/CT metabolic parameters can predict both cfBRAF V600E mutation status and clinical outcomes in pediatric LCH patients. SUVR(liver) and tMTV show excellent diagnostic performance for mutation prediction, while tMTV independently predicts prognosis. These non-invasive biomarkers can aid in risk stratification and treatment planning.

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