摘要
目的 探讨^(18)氟-氟代脱氧葡萄糖PET/MRI(^(18)F-FDG PET/MRI)参数对高危神经母细胞瘤(HR-NB)患者MYCN扩增的预测价值。方法 对2018年12月—2024年12月接受^(18)F-FDG PET/MRI检查的72例HR-NB患者进行回顾性分析。根据MYCN基因检测结果,将患者分为MYCN扩增组和非扩增组。分析患者的临床特征及原发灶的影像学特征,利用GE后处理工作站识别病灶并半自动提取PET/MRI图像上的定量参数。通过多变量逻辑回归分析确定其独立预测因素,并使用受试者工作特征(ROC)曲线评估其诊断性能,计算曲线下面积(AUC)、敏感度和特异度来评估模型的辨别能力。分别使用校准曲线和决策曲线分析(DCA)确定三个模型的校准和临床实用性。组合模型在列线图中进行可视化分析。结果 使用多变量逻辑回归分析表明,肿瘤坏死(P=0.039,OR=5.52,95%CI:1.091~27.916)、年龄(P=0.042,OR=0.959,95%CI:0.920~0.999)、糖酵解总量(TLG)(P=0.011,OR=1.004,95%CI:0.982~1.008)是HR-NB中MYCN扩增的独立预测因子。ROC曲线分析表明,与单独使用坏死(AUC 0.648,95%CI 0.526~0.757,敏感度0.621,特异度0.674)、年龄(AUC 0.724,95%CI 0.606~0.823,敏感度0.517,特异度0.930)和TLG(AUC 0.791,95%CI 0.679~0.878,敏感度0.724,特异度0.837)相比,三者的组合模型具有更高的诊断性能(AUC0.858,95%CI 0.756~0.929,敏感度0.724,特异度0.930)。校准曲线和DCA进一步验证了组合模型具有最佳的临床效用。结论 通过结合肿瘤坏死、TLG和年龄构建的预测模型可无创性区分HR-NB中的MYCN扩增状态。
Objective To investigate the clinical value of 18F-fluorodeoxyglucose(FDG)PET/MRI parameters in predicting the MYCN gene amplification status in patients with high-risk neuroblastoma(HR-NB).Methods A retrospective analysis was conducted on 72 HR-NB patients,who underwent 18F-FDG PET/MRI examinations at our institution between December 2018 and December 2024.Based on the MYCN genetic testing results,the patients were classified into the MYCN amplification and non-amplification groups.The clinical data of the patients and the imaging characteristics of the primary tumors were collected.The GE post-processing workstation was used to identify the lesions,and quantitative parameters on the PET/MRI images were semi-automatically extracted.The multivariable logistic regression analysis was employed to screen for independent predictive factors.The diagnostic performance was assessed using receiver operating characteristic(ROC)curves by calculating the areas under the curves(AUC),sensitivity,and specificity.Calibration plot and decision curve analysis(DCA)were used to evaluate the calibration and clinical utility of the models,respectively.A combined model was visualized using a nomogram.Results The multivariate logistic regression analysis identified tumor necrosis(P=0.039,OR=5.52,95%CI:1.09127.916),patient's age(P=0.042,0R=0.959,95%CI:0.920-0.999),and total lesion glycolysis(TLG,P=0.011,OR=1.004,95%CI:0.982-1.008)as independent predictive factors of MYCN amplification in HR-NB.The ROC curve analysis demonstrated that the diagnostic performance of the combined model had superior diagnostic performance(AUC:0.858,95%CI:0.756-0.929,sensitivity:0.724,specificity:0.930)compared to the models using only tumor necrosis(AUC:0.648,95%CI:0.5260.757,sensitivity:0.621,specificity:0.674),patient's age(AUC:0.724,95%CI:0.6060.823,sensitivity:0.517,specificity:0.930),or TLG(AUC:0.791,95%CI:0.6790.878,sensitivity:0.724,specificity:O.837).Calibration curves and DCA confirmed the optimal clinical utility of the combined model.Conclusion The prediction model integrating tumor necrosis,patient's age,and TLG can effectively and non-invasively predict the MYCN amplification status in HR-NB,exhibiting good diagnostic efficacy and clinical application potential.
作者
梁江涛
李峰
冯琪
颜兵
朱艳芳
王芳晓
许远帆
LIANG Jiangtao;LI Feng;FENG Qi;YAN Bing;ZHU Yanfang;WANG Fangxiao;XU Yuanfan(Hangzhou Universal Medical Imaging Diagnostic Center,Zhejiang 310000,China)
出处
《影像诊断与介入放射学》
2026年第1期41-48,共8页
Diagnostic Imaging & Interventional Radiology
基金
浙江省医药卫生科技计划项目(2022KY1047、2024KY1428)。
