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基于F-FDG PET/CT的影像组学列线图可预测儿童神经母细胞瘤的骨髓受累情况。

F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma.

作者信息

Feng Lijuan, Yang Xu, Lu Xia, Kan Ying, Wang Chao, Sun Dehui, Zhang Hui, Wang Wei, Yang Jigang

机构信息

Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.

Sinounion Medical Technology (Beijing) Co., Ltd., Beijing, 100192, China.

出版信息

Insights Imaging. 2022 Sep 4;13(1):144. doi: 10.1186/s13244-022-01283-8.

Abstract

OBJECTIVE

To develop and validate an F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma.

METHODS

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).

RESULTS

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.

CONCLUSION

The F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma.

摘要

目的

开发并验证一种基于氟脱氧葡萄糖(FDG)正电子发射断层扫描/计算机断层扫描(PET/CT)的放射组学列线图,用于无创预测小儿神经母细胞瘤的骨髓受累(BMI)情况。

方法

回顾性纳入133例神经母细胞瘤患者,并随机分为训练集(n = 93)和测试集(n = 40)。从CT和PET图像中提取放射组学特征。构建放射组学特征。通过单因素和多因素逻辑回归分析确定独立的临床危险因素,以构建临床模型。使用多因素逻辑回归分析构建整合放射组学特征和独立临床危险因素的临床-放射组学模型,最终呈现为放射组学列线图。通过受试者操作特征曲线、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。

结果

选择25个放射组学特征构建放射组学特征。将诊断时年龄、神经元特异性烯醇化酶和香草扁桃酸确定为独立预测因子,以建立临床模型。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对0.900、0.875),随后在测试集中得到证实(AUC:0.925对0.893、0.910)。校准曲线和DCA表明,放射组学列线图具有良好的一致性和临床实用性。

结论

基于F-FDG PET/CT的放射组学列线图结合了放射组学特征和独立临床危险因素,可无创预测小儿神经母细胞瘤的BMI。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0e7/9440965/0818c7a58ac1/13244_2022_1283_Fig1_HTML.jpg

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