A magnetic resonance image-based deep learning radiomics nomogram for hepatocyte cytokeratin 7 expression: application to predict cholestasis progression in children with pancreaticobiliary maljunction.

IF 2.1 3区 医学 Q2 PEDIATRICS
Yang Yang, Hui-Min Mao, Shun-Gen Huang, Wan-Liang Guo
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引用次数: 0

Abstract

Background: Hepatocyte cytokeratin 7 (CK7) is a reliable marker for evaluating the severity of cholestasis in chronic cholestatic cholangiopathies. However, there is currently no noninvasive test available to assess the status of hepatocyte CK7 in pancreaticobiliary maljunction patients.

Objective: We aimed to develop a deep learning radiomics nomogram using magnetic resonance images (MRIs) to preoperatively identify the hepatocyte CK7 status and assess cholestasis progression in patients with pancreaticobiliary maljunction.

Materials and methods: In total, 180 pancreaticobiliary maljunction patients were retrospectively enrolled and were randomly divided into a training cohort (n = 144) and a validation cohort (n = 36). CK7 status was determined through immunohistochemical analysis. Pyradiomics and pretrained ResNet50 were used to extract radiomics and deep learning features, respectively. To construct the radiomics and deep learning signature, feature selection methods including the minimum redundancy-maximum relevance and least absolute shrinkage and selection operator were employed. The integrated deep learning radiomics nomogram model was constructed by combining the imaging signatures and valuable clinical feature.

Results: The deep learning signature exhibited superior predictive performance compared with the radiomics signature, as evidenced by the higher area under the curve (AUC) values in validation cohort (0.92 vs. 0.81). Further, the deep learning radiomics nomogram, which incorporated the radiomics signature, deep learning signature, and Komi classification, demonstrated excellent predictive ability for CK7 expression, with AUC value of 0.95 in the validation cohort.

Conclusion: The proposed deep learning radiomics nomogram exhibits promising performance in accurately identifying hepatic CK7 expression, thus facilitating prediction of cholestasis progression and perhaps earlier initiation of treatment in pancreaticobiliary maljunction children.

基于磁共振图像的肝细胞角蛋白7表达深度学习放射组学图:用于预测胰胆异常儿童胆汁淤积进展。
背景:肝细胞角蛋白7 (CK7)是评估慢性胆汁淤积性胆管病中胆汁淤积严重程度的可靠标志物。然而,目前尚无无创检测方法可用于评估胰胆管异常患者的肝细胞CK7状态。目的:我们旨在开发一种使用磁共振图像(mri)的深度学习放射组学图,以术前识别肝细胞CK7状态并评估胰胆管异常患者的胆汁淤积进展。材料与方法:回顾性纳入180例胰胆管畸形患者,随机分为训练组(n = 144)和验证组(n = 36)。通过免疫组化分析确定CK7状态。使用Pyradiomics和预训练的ResNet50分别提取放射组学和深度学习特征。为了构建放射组学和深度学习签名,采用了最小冗余-最大关联和最小绝对收缩的特征选择方法和选择算子。结合影像特征和有价值的临床特征,构建集成的深度学习放射组学图模型。结果:与放射组学特征相比,深度学习特征具有更好的预测性能,验证队列的曲线下面积(AUC)值更高(0.92比0.81)。此外,结合放射组学特征、深度学习特征和Komi分类的深度学习放射组学图显示出对CK7表达的出色预测能力,在验证队列中的AUC值为0.95。结论:提出的深度学习放射组学图在准确识别肝脏CK7表达方面表现出良好的性能,从而有助于预测胰胆管异常儿童的胆汁淤积进展,并可能更早地开始治疗。
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来源期刊
Pediatric Radiology
Pediatric Radiology 医学-核医学
CiteScore
4.40
自引率
17.40%
发文量
300
审稿时长
3-6 weeks
期刊介绍: Official Journal of the European Society of Pediatric Radiology, the Society for Pediatric Radiology and the Asian and Oceanic Society for Pediatric Radiology Pediatric Radiology informs its readers of new findings and progress in all areas of pediatric imaging and in related fields. This is achieved by a blend of original papers, complemented by reviews that set out the present state of knowledge in a particular area of the specialty or summarize specific topics in which discussion has led to clear conclusions. Advances in technology, methodology, apparatus and auxiliary equipment are presented, and modifications of standard techniques are described. Manuscripts submitted for publication must contain a statement to the effect that all human studies have been reviewed by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in an appropriate version of the 1964 Declaration of Helsinki. It should also be stated clearly in the text that all persons gave their informed consent prior to their inclusion in the study. Details that might disclose the identity of the subjects under study should be omitted.
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