Identification of Biliary Atresia in Infantile Cholestasis: Integrating Radiomics With MRCP for Unobservable Extrahepatic Biliary Systems.

IF 1.3 4区 医学 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Computer Assisted Tomography Pub Date : 2025-09-01 Epub Date: 2025-03-17 DOI:10.1097/RCT.0000000000001729
Jianning Hou, Weiqiang Xiao, Siyin Zhou, Hongsheng Liu
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Abstract

Purpose: Magnetic resonance cholangiopancreatography (MRCP) may assist in the workup of infantile cholestasis as nonvisualization of the biliary tree is seen with biliary atresia (BA). However, this finding can also be seen with other causes of infantile cholestasis. The purpose of this study is to differentiate BA from other causes of infantile cholestasis using a classification tool integrating MRCP-based radiomics and clinical signatures in patients with nonvisualization of the extrahepatic biliary tree on MRCP.

Methods: Data from infants with cholestasis due to BA, cytomegalovirus infection, or idiopathic neonatal hepatitis (INH) from 2 sites was collected. Radiomics features from MRCP images were selected using Spearman and LASSO methods, followed by applying the optimal machine learning model to develop a radiomics signature. Clinical factors showing significant differences between BA and non-BA groups in training cohort were used to develop a clinical signature using the model. A nomogram model incorporating the signatures was developed. The nomogram model and signatures' performance were assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score. The DeLong test, decision curve analysis (DCA), calibration curves, and the Hosmer-Lemeshow test were utilized to evaluate the nomogram model.

Results: The training cohort consisted of 112 cases (62 BA and 50 non-BA) from site 1, while the external validation cohort included 35 cases (20 BA and 15 non-BA) from site 2. After screening, 2 clinical factors and 8 radiomics features were included. The signatures were fitted using the K-Nearest Neighbors model. The nomogram model showed an AUC of 0.981 in the training cohort and 0.913 in the external validation cohort, significantly outperforming both the signatures in the training cohort and the clinical signature in the external validation cohort, as confirmed by the DeLong test. The DCA indicated the clinical utility of the model. The Calibration curves and the Hosmer-Lemeshow test confirmed the model's adequate fit.

Conclusion: The nomogram model may hold clinical utility. In our cohorts, it was effective for identifying BA among cases with infantile cholestasis attributed to BA, cytomegalovirus infection, or INH in scenarios where the extrahepatic biliary system is not visualized on MRCP.

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鉴别婴儿胆汁淤积症的胆道闭锁:结合放射组学和MRCP观察肝外胆道系统。
目的:磁共振胆管胰胆管造影(MRCP)可能有助于小儿胆汁淤积症的检查,因为胆道闭锁(BA)时看不到胆道树。然而,这一发现也可以在婴儿胆汁淤积症的其他原因中看到。本研究的目的是利用基于MRCP的放射组学和MRCP未显示肝外胆道树的患者的临床特征的分类工具,将BA与其他原因的婴儿胆汁沉积症区分开来。方法:收集来自2个地点的BA、巨细胞病毒感染或特发性新生儿肝炎(INH)所致胆汁淤积婴儿的资料。使用Spearman和LASSO方法从MRCP图像中选择放射组学特征,然后应用最佳机器学习模型来开发放射组学签名。将训练队列中BA组与非BA组之间存在显著差异的临床因素用于建立该模型的临床特征。开发了包含签名的nomogram模型。采用曲线下面积(AUC)、准确度、灵敏度、特异性、精密度和F1评分来评估nomogram模型和签名的性能。采用DeLong检验、决策曲线分析(DCA)、校正曲线和Hosmer-Lemeshow检验对模态图模型进行评价。结果:培训队列包括来自站点1的112例(62例BA和50例非BA),外部验证队列包括来自站点2的35例(20例BA和15例非BA)。筛选后纳入2个临床因素和8个放射组学特征。这些特征是用k近邻模型拟合的。模态图模型显示,训练队列的AUC为0.981,外部验证队列的AUC为0.913,显著优于训练队列的签名和外部验证队列的临床签名,经DeLong检验证实。DCA显示了该模型的临床应用价值。校正曲线和Hosmer-Lemeshow检验证实模型拟合良好。结论:该模型具有一定的临床应用价值。在我们的队列中,在MRCP未显示肝外胆道系统的情况下,在BA、巨细胞病毒感染或INH引起的婴儿胆汁淤积病例中,该方法可有效识别BA。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).
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