(18)F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma.
(18)F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma.
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DOI：
10.1186/s13244-022-01283-8
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发表时间：
2022-09-04
影响因子：
4.7
通讯作者：
Yang, Jigang
中科院分区：
文献类型：
--
作者：
Feng, Lijuan;Yang, Xu;Lu, Xia;Kan, Ying;Wang, Chao;Sun, Dehui;Zhang, Hui;Wang, Wei;Yang, Jigang
开发并验证基于18f -氟脱氧葡萄糖（FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）的放射组学图，用于无创预测小儿神经母细胞瘤骨髓受累（BMI）。回顾性研究共纳入133例神经母细胞瘤患者，随机分为训练组（n = 93）和测试组（n = 40）。从CT和PET图像中提取放射组学特征。开发了放射组学特征。采用单因素和多因素logistic回归分析确定独立的临床危险因素，构建临床模型。结合放射组学特征和独立临床危险因素，采用多因素logistic回归分析构建临床-放射组学模型，最终以放射组学形态图的形式呈现。通过受试者工作特征曲线、校准曲线和决策曲线分析（DCA）评估临床-放射组学模型的预测性能。选取25个放射组学特征构建放射组学签名。确定诊断年龄、神经元特异性烯醇化酶和香草扁桃酸为独立预测因子，建立临床模型。在训练集中，临床-放射组学模型在预测BMI方面优于放射组学模型或临床模型（AUC: 0.924 vs. 0.900, 0.875），然后在测试集中得到证实（AUC: 0.925 vs. 0.893, 0.910）。校正曲线和DCA表明放射组学图具有良好的一致性和临床应用价值。基于18F-FDG PET/ ct的放射组学图结合放射组学特征和独立临床危险因素，可以无创预测小儿神经母细胞瘤的BMI。在线版本包含补充材料，可在10.1186/s13244-022-01283-8获得。
To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma. A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility. The 18F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma. The online version contains supplementary material available at 10.1186/s13244-022-01283-8.
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