Ultrasound-Based Radiomics for the Classification of Henoch-Schönlein Purpura Nephritis in Children.

IF 2.5 4区 医学 Q1 ACOUSTICS Ultrasonic Imaging Pub Date : 2024-03-01 Epub Date: 2023-12-22 DOI:10.1177/01617346231220000
Jie Chen, Zeying Wen, Xiaoqing Yang, Jie Jia, Xiaodong Zhang, Linping Pian, Ping Zhao
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Abstract

Henoch-Schönlein purpura nephritis (HSPN) is one of the most common kidney diseases in children. The current diagnosis and classification of HSPN depend on pathological biopsy, which is seriously limited by its invasive and high-risk nature. The aim of the study was to explore the potential of radiomics model for evaluating the histopathological classification of HSPN based on the ultrasound (US) images. A total of 440 patients with Henoch-Schönlein purpura nephritis proved by biopsy were analyzed retrospectively. They were grouped according to two histopathological categories: those without glomerular crescent formation (ISKDC grades I-II) and those with glomerular crescent formation (ISKDC grades III-V). The patients were randomly assigned to either a training cohort (n = 308) or a validation cohort (n = 132) with a ratio of 7:3. The sonologist manually drew the regions of interest (ROI) on the ultrasound images of the right kidney including the cortex and medulla. Then, the ultrasound radiomics features were extracted using the Pyradiomics package. The dimensions of radiomics features were reduced by Spearman correlation coefficients and least absolute shrinkage and selection operator (LASSO) method. Finally, three radiomics models using k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established, respectively. The predictive performance of such classifiers was assessed with receiver operating characteristic (ROC) curve. 105 radiomics features were extracted from derived US images of each patient and 14 features were ultimately selected for the machine learning analysis. Three machine learning models including k-nearest neighbor (KNN), logistic regression (LR), and support vector machine (SVM) were established for HSPN classification. Of the three classifiers, the SVM classifier performed the best in the validation cohort [area under the curve (AUC) =0.870 (95% CI, 0.795-0.944), sensitivity = 0.706, specificity = 0.950]. The US-based radiomics had good predictive value for HSPN classification, which can be served as a noninvasive tool to evaluate the severity of renal pathology and crescentic formation in children with HSPN.

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基于超声波的放射组学用于儿童白癜风肾炎的分类。
过敏性紫癜肾炎(HSPN)是儿童最常见的肾脏疾病之一。目前,HSPN 的诊断和分类依赖于病理活检,而病理活检因其侵入性和高风险性而受到严重限制。本研究旨在探索放射组学模型在基于超声(US)图像评估 HSPN 组织病理学分类方面的潜力。研究人员对 440 例经活检证实的过敏性紫癜肾炎患者进行了回顾性分析。他们按照两个组织病理学类别进行分组:无肾小球新月体形成(ISKDC I-II级)和有肾小球新月体形成(ISKDC III-V级)。患者被随机分配到训练组(308 人)或验证组(132 人),两者的比例为 7:3。超声专家在右肾的超声图像上手动绘制感兴趣区(ROI),包括皮质和髓质。然后,使用 Pyradiomics 软件包提取超声放射组学特征。利用斯皮尔曼相关系数和最小绝对缩小和选择算子(LASSO)方法缩小了放射组学特征的维数。最后,分别使用 k 近邻(KNN)、逻辑回归(LR)和支持向量机(SVM)建立了三个放射组学模型。这些分类器的预测性能通过接收者操作特征曲线(ROC)进行评估。从每位患者的衍生 US 图像中提取了 105 个放射组学特征,最终选择了 14 个特征进行机器学习分析。为 HSPN 分类建立了三种机器学习模型,包括 k 近邻(KNN)、逻辑回归(LR)和支持向量机(SVM)。在这三种分类器中,SVM分类器在验证队列中表现最佳[曲线下面积(AUC)=0.870(95% CI,0.795-0.944),灵敏度=0.706,特异性=0.950]。基于美国放射组学的 HSPN 分类具有良好的预测价值,可作为一种无创工具来评估 HSPN 儿童肾脏病理和新月体形成的严重程度。
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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