南方医科大学学报

南方医科大学学报 ›› 2021, Vol. 41 ›› Issue (10): 1569-1576.doi: 10.12122/j.issn.1673-4254.2021.10.17

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CT影像组学鉴别儿童腹膜后神经母细胞瘤和节细胞神经母细胞瘤的价值

王浩入，陈 欣，刘 欢，余春霖，何 玲   

  1. 重庆医科大学附属儿童医院放射科，国家儿童健康与疾病临床医学研究中心，儿童发育疾病研究教育部重点实验室，儿科学重庆市重点实验室，重庆 400014；通用电气药业有限公司，精准医学研究院，上海 201203
  • 出版日期:2021-10-20 发布日期:2021-11-11

Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children

WANG Haoru, CHEN Xin, LIU Huan, YU Chunlin, HE Ling   

  1. Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China; GE Healthcare, Shanghai 201203, China
  • Online:2021-10-20 Published:2021-11-11

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摘要： 目的 基于平扫和增强CT的影像组学分析在鉴别儿童腹膜后神经母细胞瘤（NB）和节细胞性神经母细胞瘤（GNB）中的价值。方法 纳入172例NB和48例GNB患儿，按7∶3的比例分层随机抽样划分为训练集和测试集。分别从平扫期、动脉期和静脉期CT图像中提取并筛选影像组学特征，基于最优特征子集采用多变量回归模型建立各期以及三期复合的影像组学模型，绘制模型ROC曲线，计算并比较各期模型的AUC、准确度、灵敏度及特异性等评价指标。结果 从平扫期、动脉期和静脉期CT图像中分别提取了1218个影像组学特征，最终筛选出平扫期模型4个特征、动脉期模型3个特征、静脉期模型2个特征以及三期复合模型 5 个特征。平扫期模型在训练集中的 AUC 为 0.840（95%CI: 0.778~0.902），测试集中 AUC 为 0.804（95%CI: 0.699~ 0.899）。动脉期模型在训练集中的AUC为0.819（95%CI: 0.759~0.877），测试集中AUC为0.815（95%CI: 0.697~0.915）。静脉期模型在训练集中的AUC为0.730（95%CI: 0.649~0.803），测试集中AUC为0.751（95%CI: 0.619~0.869）。三期复合模型在训练集中的AUC为0.861（95%CI: 0.809~0.910），测试集中AUC为0.827（95%CI: 0.726~0.915）。结论 基于平扫和增强CT的影像组学特征有助于区分儿童腹膜后NB和GNB，纹理特征相对于一阶直方图特征能更好的反映病灶的差异。平扫期、动脉期和静脉期影像组学模型均可较好鉴别儿童腹膜后NB和GNB。三期复合模型与平扫期、动脉期模型效能相似，但优于静脉期模型。

关键词: 儿童；神经母细胞瘤；节细胞神经母细胞瘤；计算机断层扫描；影像组学

Abstract: Objective To explore the value of CT-based radiomics in differential diagnosis of retroperitoneal neuroblastoma (NB) and ganglioneuroblastoma (GNB) in children. Methods A total of 172 children with NB and 48 children with GNB were assigned into the training set and testing set at the ratio of 7∶3 using a random stratified sampling method. Radiomics features were extracted and selected from non-enhanced and post-enhanced CT images. Based on the subset of optimal features, a multivariate regression model was used to establish the radiomics models for each phase and the combined radiomics models. The ROC curves of the models were drawn, and the evaluation indexes such as AUC, accuracy, sensitivity and specificity of these models were calculated and compared. Results A total of 1218 radiomics features were extracted from the CT images acquired in non-enhanced (NP), arterial (AP) and venous phases (VP), from which 4 features from the NP model, 3 features from the AP model, 2 features from the VP model and 5 features from the combined model were selected. The AUC of the NP model in the training set and testing set was 0.840 (95% CI: 0.778-0.902) and 0.804 (95% CI: 0.699-0.899), respectively, as compared with 0.819 (95%CI: 0.759-0.877) and 0.815 (95%CI: 0.697-0.915) for the AP model, 0.730 (95%CI: 0.649-0.803) and 0.751 (95%CI: 0.619-0.869) for the VP model, and 0.861 (95%CI: 0.809-0.910) and 0.827 (95%CI: 0.726-0.915) for the combined model. Conclusion Radiomics signature based on non-enhanced and post-enhanced CT images can be helpful for distinguishing retroperitoneal NB and GNB in children. Compared with the first-order histogram features, textural features can better reflect the difference of the lesions. NP, AP and VP models have similar classification efficacy in differentiating retroperitoneal NB and GNB. The efficacy of the combined model is similar to that of the NP and AP models, but superior to that of the VP model.

Key words: children; neuroblastoma; ganglioneuroblastoma; computed tomography; radiomics