Supplementary Material for: Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis

Background: 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. Methods: 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. Results: 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 (r = 0.480, p < 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%. Conclusion: 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.

背景：对肾小球病变和结构的评估在理解肾小球肾炎的病理诊断以及众多肾脏疾病预后评估方面扮演着至关重要的角色。肾脏病理生理学评估需要新型的高通量工具以进行定量、无偏倚和可重复的分析，这构成了核心的评估输出。深度学习可能是肾小球肾炎病理分析的有效工具。方法：本研究开发了小鼠肾脏病理系统（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，以客观化病理评估。使用卷积神经网络模型对来自健康小鼠和狼疮性肾炎小鼠的苏木精-伊红染色肾脏组织的肾小球和肾小球细胞进行精确分割。为了实现定量评估，我们从所有特征中筛选出五个独立的预测因子作为图像生物标志物，并开发了评分模型的公式。结果：周长、形状系数、最小内部直径、最小夹持直径和对象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（r = 0.480，p < 0.001）呈正相关，并在区分不同评分等级方面表现出优异的诊断性能（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%，准确率为74–94%。结论：本研究开发的针对MRL/lpr狼疮性肾炎小鼠肾脏全切片图像定量评估的MRPS，能够进行精确的病理学分析，具有高可重复性，可能成为肾小球肾炎诊断和预后评估的有用工具。