Shen Luping, Sun Wenyi, Zhang Qixiang, Wei Mengru, Xu Huanke, Luo Xuan, Wang Guangji, Zhou Fang
Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.
School of Pharmacy, China Pharmaceutical University, Nanjing, China.
Kidney Dis (Basel). 2022 Jun 7;8(4):347-356. doi: 10.1159/000524880. eCollection 2022 Jul.
Assessment of glomerular lesions and structures plays an essential role in understanding the pathological diagnosis of glomerulonephritis and prognostic evaluation of many kidney diseases. Renal pathophysiological assessment requires novel high-throughput tools to conduct quantitative, unbiased, and reproducible analyses representing a central readout. Deep learning may be an effective tool for glomerulonephritis pathological analysis.
We developed a murine renal pathological system (MRPS) model to objectify the pathological evaluation via the deep learning method on whole-slide image (WSI) segmentation and feature extraction. A convolutional neural network model was used for accurate segmentation of glomeruli and glomerular cells of periodic acid-Schiff-stained kidney tissue from healthy and lupus nephritis mice. To achieve a quantitative evaluation, we subsequently filtered five independent predictors as image biomarkers from all features and developed a formula for the scoring model.
Perimeter, shape factor, minimum internal diameter, minimum caliper diameter, and number of objects were identified as independent predictors and were included in the establishment of the MRPS. The MRPS showed a positive correlation with renal score ( = 0.480, < 0.001) and obtained great diagnostic performance in discriminating different score bands (Obuchowski index, 0.842 [95% confidence interval: 0.759, 0.925]), with an area under the curve of 0.78-0.98, sensitivity of 58-93%, specificity of 72-100%, and accuracy of 74-94%.
Our MRPS for quantitative assessment of renal WSIs from MRL/lpr lupus nephritis mice enables accurate histopathological analyses with high reproducibility, which may serve as a useful tool for glomerulonephritis diagnosis and prognosis evaluation.
肾小球病变和结构的评估在理解肾小球肾炎的病理诊断及多种肾脏疾病的预后评估中起着至关重要的作用。肾脏病理生理学评估需要新型高通量工具来进行定量、无偏倚且可重复的分析，这是核心读数。深度学习可能是用于肾小球肾炎病理分析的有效工具。
我们开发了一种小鼠肾脏病理系统（MRPS）模型，通过深度学习方法对全切片图像（WSI）进行分割和特征提取，从而客观化病理评估。使用卷积神经网络模型对健康小鼠和狼疮性肾炎小鼠的高碘酸-希夫染色肾脏组织中的肾小球和肾小球细胞进行准确分割。为了实现定量评估，我们随后从所有特征中筛选出五个独立预测因子作为图像生物标志物，并开发了评分模型公式。
周长、形状因子、最小内径、最小卡尺直径和物体数量被确定为独立预测因子，并被纳入MRPS的建立中。MRPS与肾脏评分呈正相关（= 0.480，< 0.001），在区分不同评分区间时具有良好的诊断性能（Obuchowski指数，0.842 [95%置信区间：0.759，0.925]），曲线下面积为0.78 - 0.98，敏感性为58 - 93%，特异性为72 - 100%，准确性为74 - 94%。
我们用于定量评估MRL/lpr狼疮性肾炎小鼠肾脏WSIs的MRPS能够进行准确的组织病理学分析，具有高重复性，这可能成为肾小球肾炎诊断和预后评估的有用工具。
