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目的:探讨基于n 18F-脱氧葡萄糖(FDG)PET/CT的影像组学预测神经母细胞瘤(NB)美国儿童肿瘤协作组(COG)危险度分层的价值。n 方法:回顾性分析2018年3月至2019年11月间于北京友谊医院病理证实为NB的125例患儿(男51例、女74例，年龄：0.5～10.5岁)的n 18F-FDG PET/CT图像。根据COG分层系统将患儿分为高危组和非高危组(包括中危和低危)。分别从PET和CT图像中提取影像组学特征并进行特征筛选。利用logistic回归构建基于影像组学特征的模型(R_model)，计算影像组学评分(Rad_score)；基于Rad_score和人口学特征构建第2个模型(RD_model)；最后基于Rad_score、人口学特征和临床特征构建第3个模型(RDC_model)。采用受试者工作特征(ROC)曲线评估各模型的预测性能。n 结果:训练集包含94例NB患儿(高危63例，非高危31例)，验证集包含31例NB患儿(高危21例，非高危10例)。通过筛选得到4个影像组学特征，其中2个特征基于CT图像，另外2个特征基于PET图像。在训练集和验证集中，R_model、RD_model、RDC_model预测NB患儿COG危险度分层的曲线下面积(AUC)分别为0.91和0.86、0.94和0.92、0.98和0.95；准确性分别为86%(81/94)和84%(26/31)、89%(84/94)和84%(26/31)、93%(87/94)和87%(27/31)。结论:基于n 18F-FDG PET/CT的影像组学可准确地预测NB患儿COG危险度分层，联合人口学特征和临床特征，可进一步提高预测COG危险度分层的准确性，为NB个性化精准治疗方案的制定提供帮助。n “,”Objective:To explore the value of radiomics based on n 18F-fluorodeoxyglucose (FDG) PET/CT in predicting the Children′s Oncology Group (COG) risk stratification of neuroblastoma (NB).n Methods:From March 2018 to November 2019, the n 18F-FDG PET/CT images of 125 NB children (51 males, 74 females, age: 0.5-10.5 years) confirmed pathologically in Beijing Friendship Hospital were retrospectively analyzed. According to the COG classification, patients were divided into high-risk group and non-high-risk group (including low- and intermediate-risk). Imaging radiomics features were extracted from PET and CT images and screened. Logistic regression was used to build the first model based on radiomics features (R_model) and calculate radiomics score (Rad_score), then build the second model (RD_model) based on Rad_score and demographic features and at last build the third model (RDC_modle) based on Rad_score, demographic features and clinical features. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of these models.n Results:The training set contained 94 NB cases (63 high-risk cases, 31 non-high-risk cases), and the validation set contained 31 NB cases (21 high-risk cases, 10 non-high-risk cases). Four radiomics features were obtained by screening, of which two features were based on CT images and the other two features were based on PET images. The area under the curves (AUCs) of the R_model, RD_model and RDC_model in training or validation set were 0.91, 0.94, 0.98 or 0.86, 0.92, 0.95, respectively. The accuracies of the R_model, RD_model and RDC_model in training or validation set were 86%(81/94), 89%(84/94), 93%(87/94) or 84%(26/31), 84%(26/31), 87%(27/31), respectively.Conclusions:Radiomics based on n 18F-FDG PET/CT can accurately predict the COG risk stratification of NB. Prediction model of radiomics features combined with demographic and clinical characteristics can further improve the accuracy of predicting NB COG risk stratification, which can help personalized and precise therapy protocol management in NB.n