Department of Radiology, Children's Hospital of Chongqing Medical University, The Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China.
Huiying Medical Technology Co., Ltd., Haidian District, Beijing, 100192, China.
Pediatr Radiol. 2023 Dec;53(13):2586-2596. doi: 10.1007/s00247-023-05761-z. Epub 2023 Oct 9.
Computed tomography (CT) can be used for the early detection of lymphadenitis. Radiomics is able to identify a large amount of hidden information from images. However, few CT-based radiomics studies on cervical lymphadenitis in children have been published.
This study aimed to investigate the role of visual CT analysis and CT radiomics in differentiating cervical suppurative node necrosis from tuberculous node necrosis in pediatric patients.
A total of 101 patients with cervical suppurative lymphadenitis (n=52) or cervical tuberculous lymphadenitis (n=49) were included. Clinical data and CT images were retrieved for analysis. For visual observation, 11 major CT features were identified for univariate and multivariate analyses. For radiomics analysis, image segmentation, feature value extraction, and dimension reduction, feature selection and the construction of radiomics-based models were performed through the RadCloud platform.
For the visual observation, significant differences were found between the two groups, including the short diameter of the largest necrotic lymph node (P=0.03), sharp border of the node (P=0.02), fusion of nodes (P=0.02), regular silhouette of the necrotic area (P=0.001), multilocular necrotic area (P=0.02), node calcification (P=0.004), and enhancement degree of the nodal nonnecrotic area (P=0.01). No feature was found to be an independent predictor for suppurative or tuberculous lymphadenitis (P>0.05 for all features). Concerning the radiomics analysis, after feature value extraction and dimension reduction, nine related features were selected. The support vector machine classifier achieved high diagnostic performance in distinguishing suppurative from tuberculous lymphadenitis. The area under the curve, accuracy, sensitivity, and specificity of the support vector machine model test set were 0.89 (95% confidence interval: 0.72-1.00), 0.88, 0.78, and 0.90, respectively.
Compared to observer-based CT image analyses, radiomics model-based CT image analyses exhibit better performance in the differential diagnosis of cervical suppurative and tuberculous lymphadenitis complicated with nodal necrosis in children.
计算机断层扫描（CT）可用于早期发现淋巴结炎。放射组学能够从图像中识别出大量隐藏信息。然而，关于儿童颈部淋巴结炎的基于 CT 的放射组学研究很少。
本研究旨在探讨视觉 CT 分析和 CT 放射组学在鉴别儿童化脓性淋巴结坏死与结核性淋巴结坏死中的作用。
共纳入 101 例颈部化脓性淋巴结炎（n=52）或颈部结核性淋巴结炎（n=49）患者。分析临床资料和 CT 图像。进行单变量和多变量分析时，观察 11 项主要 CT 特征。进行放射组学分析时，通过 RadCloud 平台进行图像分割、特征值提取和降维、特征选择以及放射组学模型构建。
对于视觉观察，两组间存在显著差异，包括最大坏死淋巴结短径（P=0.03）、淋巴结边界锐利（P=0.02）、淋巴结融合（P=0.02）、坏死区轮廓规则（P=0.001）、多腔坏死区（P=0.02）、淋巴结钙化（P=0.004）和淋巴结非坏死区强化程度（P=0.01）。没有特征被发现是化脓性或结核性淋巴结炎的独立预测因子（所有特征 P>0.05）。放射组学分析方面，经过特征值提取和降维后，选择了 9 个相关特征。支持向量机分类器在鉴别化脓性与结核性淋巴结炎方面具有较高的诊断性能。支持向量机模型测试集的曲线下面积、准确率、敏感度和特异度分别为 0.89（95%置信区间：0.72-1.00）、0.88、0.78 和 0.90。
与基于观察者的 CT 图像分析相比，基于放射组学模型的 CT 图像分析在鉴别儿童化脓性和结核性淋巴结炎并发淋巴结坏死方面具有更好的性能。
