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Updated: Aug 21, 2026
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Clinical application of deep learning-based measurement of glomerular morphometry in IgAN
Lin-Lin Xu1, Xing-Kuo Zhang2, Zhen-Zheng Dong2
1Renal Division, Peking University First Hospital; Kidney Genetics Center, Peking University Institute of Nephrology; Key Laboratory of Renal Disease, Ministry of Health of China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China.
Background:
Accurate glomerular pathology assessment is crucial for predicting outcomes and guiding treatment in IgA nephropathy (IgAN). Although the Oxford classification provides a standardized histological assessment, it is semi-quantitative and subjective. Identifying objective and interpretable renal pathological features could enhance prognostic prediction.
Methods:
We developed and validated a deep learning-based computational pathology pipeline for glomerular analysis in IgAN. A glomerular segmentation and classification model was developed based on deep convolutional neural networks in 183 periodic acid-Schiff (PAS)-stained whole slide images (WSIs) of renal pathology from IgAN patients, and was further applied to WSIs of 886 IgAN patients from a prospective cohort. Associations of glomerular morphometry with clinical phenotypes and prognosis were determined.
Results:
The models demonstrated glomerular segmentation performance with a Dice coefficient of 0.91 and classification performance with an AUC of 0.94. Through a comprehensive correlation analysis of quantitative morphometric characteristics and clinical parameters, we found that age, gender, and body mass index significantly influenced glomerular morphology. No association was found between proteinuria and glomerular size. Notably, a nonlinear relationship was observed between glomerular size and estimated glomerular filtration rate (eGFR), with patients having an eGFR between 30 and 60 ml/min/1.73m2 exhibiting larger glomerular size compared to those with eGFR<30 or eGFR≥60 ml/min/1.73m². A biphasic relationship between mean glomerular area and poor prognosis of IgAN was identified. The AUC of the prognosis prediction model increased from 0.79 to 0.84 when integrating glomerular histomorphometric into the International IgAN Prediction Tool.
Conclusions:
In conclusion, we developed an end-to-end pipeline for glomerular segmentation, classification, and morphometry in WSIs. These objective and highly reproducible quantitative characteristics of glomeruli may serve as a valuable tool for therapeutic guidance and prognostic assessment in IgAN.
