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

Abstract

<b><i>Background:</i></b> 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. <b><i>Methods:</i></b> 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. <b><i>Results:</i></b> 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 (<i>r</i> = 0.480, <i>p</i> < 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%. <b><i>Conclusion:</i></b> 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.

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“…The advent of deep learning in recent years has even further highlighted the significance and prospective applications of neural networks in various directions of medical studies. Deep learning and the feature engineering techniques have enabled obtaining deeper insight into the data that were not easily obtainable earlier [47,48]. These networks are the simple mathematical formulation of the superfast and overcomplicated learning process which occurs in the human brain [49,50].…”

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

“…For example, current nephropathological classification of lupus nephritis focuses on the proliferation of mesangial cells and endocapillary cells as well as the proportion of glomeruli with inflammatory lesions -whether they are local or diffuse [34]. Many of the recent studies using machine learning to evaluate lupus nephritis kidney biopsies focused on automating and thereby standardizing the classification of features from glomerular lesions biopsy [28][29][30][31]. However, tubulointerstitial inflammation, not glomerular inflammation, is a known predictor for endstage renal disease (ESRD), but not all patients with this feature progress [32 && ].…”

“…Machine learning can also be used to evaluate histologic findings [28–31,32 ▪▪ ,33]. As with MR image analysis, the use of the machine learning paradigm to evaluate histology could lead to better standardization of diagnoses, and, additionally, allow for the recognition or incorporation of features that were previously not considered in lupus nephritis or other lupus tissue pathologies.…”