Inter-observer variability of the histological classification of lupus glomerulonephritis in children

Abstract

The gold standard for the classification of lupus nephritis is renal histology but reporting variation exists. The aim of this study was to assess the inter-observer variability of the 2003 International Society of Nephrology/Royal Pathology Society (ISN/RPS) lupus nephritis histological classification criteria in children. Histopathologists from a reference centre and three tertiary paediatric centres independently reviewed digitalized renal histology slides from 55 children with lupus nephritis. Histological ISN/RPS Class was assigned and features scored; lupus nephritis-activity [scored 0-24], lupus nephritis-chronicity [0-12] and tubulointerstitial activity [0-21]. In the cohort (73% females), the age at the time of biopsy was 15.5 ± 0.39 (mean ± standard error) years. Based on the reference centre, 42% (23/55) had ISN/RPS Class IV with lupus nephritis-activity score 4.23 ± 0.50, lupus nephritis-chronicity 1.81 ± 0.18 and tubulointerstitial activity 4.45 ± 0.35. There were 4-54 (mean 16.7) glomeruli per biopsy. Pathologists had fair agreement for ISN/RPS assignment (kappa; 0.26 ± 0.12), lupus nephritis-chronicity (intra-class correlation 0.36 ± 0.09) and tubulointerstitial activity (0.22 ± 0.09) scores. There was good agreement for lupus nephritis-activity scores (intra-class correlation 0.69 ± 0.06). When categorized into proliferative and non-proliferative disease, poor agreement among sites remained (kappa 0.24 ± 0.11). Despite unified criteria for the interpretation of histological features of lupus nephritis, marked reporting variation remains in clinical practice. As proliferative lupus nephritis is managed more intensively, this may influence renal outcomes.

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

“…Indeed, any medical imaging technique involving human reporting is conditioned by a significant interpretative subjectivity. This poor reproducibility has been repeatedly documented in the old kidney histopathological literature [26][27][28][29] and has been recently confirmed in studies concerning the application of Deep Learning in kidney pathology 1,2 . The indices of agreement between human pathologists vary according to the metrics used, the preparation of the specimen, the histological parameters assessed, and the kidney disease considered; overall, reported agreement rate between human kidney pathologists is fair to moderate with agreement ratio ranging between 0.3 and 0.6.…”

Chronic histological changes in deceased donor kidneys at implantation do not predict graft survival: a single‐centre retrospective analysis

“…Thirdly, a single histological scoring system was used, and it is possible that using different scoring systems may have led to different results. Fourthly, pathology slides were not re‐read by a single histopathologist; it is possible that interpathologist interpretations , even amongst specialist renal pathologists , may vary sufficiently to mask an association between chronic changes and graft outcome. However, multiple histopathologists would be necessary to provide a ‘round‐the‐clock’ PIKB service, and therefore our analysis is pragmatic.…”

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

“…and visual quantification by pathologists are time-consuming and limited by poor intra-and interreader reproducibility. [4][5][6][7] The introduction of digital pathology in nephrology clinical trials 8 has provided an unprecedented opportunity to test machine learning approaches for large-scale tissue quantification efforts. Standardization of pathology material acquisition has allowed worldwide consortia to establish digital pathology repositories containing thousands of digital renal biopsies for the evaluation of kidney diseases in adults and children, across diverse populations and pathology laboratories.…”