Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

Yijiang Chen*, Jarcy Zee, Abigail Smith, Catherine Jayapandian, Jeffrey Hodgin, David Howell, Matthew Palmer, David Thomas, Clarissa Cassol, Alton B. Farris, Kathryn Perkinson, Anant Madabhushi, Laura Barisoni, Andrew Janowczyk

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Inconsistencies in the preparation of histology slides and whole-slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e. systematic technical artifacts unrelated to biological variability) may introduce biases to machine learning algorithms. To date, manual quality control (QC) has been the de facto standard for dataset curation, but remains highly subjective and is too laborious in light of the increasing scale of tissue slide digitization efforts. This study aimed to evaluate a computer-aided QC pipeline for facilitating a reproducible QC process of WSI datasets. An open source tool, HistoQC, was employed to identify image artifacts and compute quantitative metrics describing visual attributes of WSIs to the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository. A comparison in inter-reader concordance between HistoQC aided and unaided curation was performed to quantify improvements in curation reproducibility. HistoQC metrics were additionally employed to quantify the presence of batch effects within NEPTUNE WSIs. Of the 1814 WSIs (458 H&E, 470 PAS, 438 silver, 448 trichrome) from n = 512 cases considered in this study, approximately 9% (163) were identified as unsuitable for subsequent computational analysis. The concordance in the identification of these WSIs among computational pathologists rose from moderate (Gwet's AC1 range 0.43 to 0.59 across stains) to excellent (Gwet's AC1 range 0.79 to 0.93 across stains) agreement when aided by HistoQC. Furthermore, statistically significant batch effects (p < 0.001) in the NEPTUNE WSI dataset were discovered. Taken together, our findings strongly suggest that quantitative QC is a necessary step in the curation of digital pathology cohorts.

Original languageEnglish (US)
Pages (from-to)268-278
Number of pages11
JournalJournal of Pathology
Issue number3
StatePublished - Mar 2021


  • batch effects
  • computational pathology
  • computer vision
  • digital pathology
  • inter-reader variability
  • kidney biopsies
  • machine learning
  • quality control
  • whole-slide image

ASJC Scopus subject areas

  • Pathology and Forensic Medicine


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