Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
BMC Med Imaging. 2021 Jul 23;21(1):115. doi: 10.1186/s12880-021-00647-8.
The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy.
A total of 623 renal ultrasound images from 46 membranous nephropathy (MN) and 22 IgA nephropathy patients were collected. The cases and images were divided into a training group (51 cases with 470 images) and a test group (17 cases with 153 images). A total of 180 dimensional features were designed and extracted from the renal parenchyma in the ultrasound images. Least absolute shrinkage and selection operator (LASSO) logistic regression was then applied to these normalized radiomics features to select the features with the highest correlations. Four machine learning classifiers, including logistic regression, a support vector machine (SVM), a random forest, and a K-nearest neighbour classifier, were deployed for the classification of MN and IgA nephropathy. Subsequently, the results were assessed according to accuracy and receiver operating characteristic (ROC) curves.
Patients with MN were older than patients with IgA nephropathy. MN primarily manifested in patients as nephrotic syndrome, whereas IgA nephropathy presented mainly as nephritic syndrome. Analysis of the classification performance of the four classifiers for IgA nephropathy and MN revealed that the random forest achieved the highest area under the ROC curve (AUC) (0.7639) and the highest specificity (0.8750). However, logistic regression attained the highest accuracy (0.7647) and the highest sensitivity (0.8889).
Quantitative radiomics imaging features extracted from digital renal ultrasound are fully capable of distinguishing IgA nephropathy from MN. Radiomics analysis, a non-invasive method, is helpful for histological classification of glomerulopathy.
本研究旨在探讨肾脏超声影像学特征在肾小球病组织学分类中的潜在应用。
共收集了 46 例膜性肾病（MN）和 22 例 IgA 肾病患者的 623 例肾脏超声图像。病例和图像被分为训练组（51 例，470 例图像）和测试组（17 例，153 例图像）。从超声图像的肾实质中设计并提取了 180 个维度的特征。然后，应用最小绝对收缩和选择算子（LASSO）逻辑回归对这些归一化的放射组学特征进行筛选，以选择相关性最高的特征。采用逻辑回归、支持向量机（SVM）、随机森林和 K 最近邻分类器等 4 种机器学习分类器对 MN 和 IgA 肾病进行分类。然后根据准确性和接收者操作特征（ROC）曲线评估结果。
MN 患者比 IgA 肾病患者年龄更大。MN 主要表现为肾病综合征，而 IgA 肾病主要表现为肾炎综合征。对 4 种分类器对 IgA 肾病和 MN 的分类性能进行分析，结果显示随机森林获得的 ROC 曲线下面积（AUC）最高（0.7639），特异性最高（0.8750）。然而，逻辑回归的准确性最高（0.7647），敏感性最高（0.8889）。
从数字肾脏超声中提取的定量放射组学成像特征完全能够区分 IgA 肾病和 MN。放射组学分析作为一种非侵入性方法，有助于肾小球病的组织学分类。
