Department of radiology, West China Second University Hospital, Sichuan University, No.1416, Section 1, Chenglong Road, 610066, Chengdu, Sichuan, China.
Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610041, Sichuan, China.
BMC Med Imaging. 2023 Aug 24;23(1):112. doi: 10.1186/s12880-023-01075-6.
On the basis of visual-dependent reading method, radiological recognition and assessment of neonatal hyperbilirubinemia (NH) or acute bilirubin encephalopathy (ABE) on conventional magnetic resonance imaging (MRI) sequences are challenging. Prior studies had shown that radiomics was possible to characterize ABE-induced intensity and morphological changes on MRI sequences, and it has emerged as a desirable and promising future in quantitative and objective MRI data extraction. To investigate the utility of radiomics based on T1-weighted sequences for identifying neonatal ABE in patients with hyperbilirubinemia and differentiating between those with NH and the normal controls.
A total of 88 patients with NH were enrolled, including 50 patients with ABE and 38 ABE-negative individuals, and 70 age-matched normal neonates were included as controls. All participants were divided into training and validation cohorts in a 7:3 ratio. Radiomics features extracted from the basal ganglia of T1-weighted sequences on magnetic resonance imaging were evaluated and selected to set up the prediction model using the K-nearest neighbour-based bagging algorithm. A receiver operating characteristic curve was plotted to assess the differentiating performance of the radiomics-based model.
Four of 744 radiomics features were selected for the diagnostic model of ABE. The radiomics model yielded an area under the curve (AUC) of 0.81 and 0.82 in the training and test cohorts, with accuracy, precision, sensitivity, and specificity of 0.82, 0.80, 0.91, and 0.69 and 0.78, 0.8, 0.8, and 0.75, respectively. Six radiomics features were selected in this model to distinguish those with NH from the normal controls. The AUC for the training cohort was 0.97, with an accuracy of 0.92, a precision of 0.92, a sensitivity of 0.93, and a specificity of 0.90. The performance of the radiomics model was confirmed by testing the test cohort, and the AUC, accuracy, precision, sensitivity, and specificity were 0.97, 0.92, 0.96, 0.89, and 0.95, respectively.
The proposed radiomics model based on traditional TI-weighted sequences may be used effectively for identifying ABE and even differentiating patients with NH from the normal controls, which can provide microcosmic information beyond experience-dependent vision and potentially assist in clinical diagnosis and treatment.
基于视觉依赖的阅读方法，在常规磁共振成像（MRI）序列上对新生儿高胆红素血症（NH）或急性胆红素脑病（ABE）进行放射学识别和评估具有挑战性。先前的研究表明，放射组学有可能对 MRI 序列上 ABE 引起的强度和形态变化进行特征描述，并且它已经成为定量和客观 MRI 数据提取的一种理想且有前途的方法。本研究旨在探讨基于 T1 加权序列的放射组学在识别高胆红素血症患者中的 ABE 并区分 NH 患者与正常对照组中的应用价值。
共纳入 88 例 NH 患者，其中 50 例 ABE 患者，38 例 ABE 阴性患者，70 例年龄匹配的正常新生儿作为对照组。所有参与者均以 7:3 的比例分为训练和验证队列。从 MRI 的 T1 加权序列的基底节中提取放射组学特征，并使用基于 K-最近邻的装袋算法来评估和选择预测模型。绘制受试者工作特征曲线以评估放射组学模型的区分性能。
从 744 个放射组学特征中选择了 4 个用于 ABE 的诊断模型。放射组学模型在训练组和测试组中的曲线下面积（AUC）分别为 0.81 和 0.82，其准确性、精度、敏感度和特异性分别为 0.82、0.80、0.91 和 0.69，以及 0.78、0.80、0.80、0.75。该模型还选择了 6 个放射组学特征来区分 NH 患者与正常对照组。训练队列的 AUC 为 0.97，准确率为 0.92，精度为 0.92，敏感度为 0.93，特异性为 0.90。通过对测试队列进行测试，验证了放射组学模型的性能，其 AUC、准确性、精度、敏感度和特异性分别为 0.97、0.92、0.96、0.89 和 0.95。
本研究提出的基于传统 T1 加权序列的放射组学模型可有效用于识别 ABE，甚至区分 NH 患者与正常对照组，为经验依赖视觉之外提供微观信息，有可能辅助临床诊断和治疗。
