Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies
62
Zitationen
14
Autoren
2020
Jahr
Abstract
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. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Ähnliche Arbeiten
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
2021 · 111.406 Zit.
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2018 · 87.481 Zit.
Global cancer statistics
2011 · 55.023 Zit.
Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
2014 · 28.992 Zit.
Global cancer statistics, 2012
2015 · 27.335 Zit.
Autoren
Institutionen
- Case Western Reserve University(US)
- Arbor Research Collaborative for Health(US)
- University of Michigan(US)
- Duke University(US)
- University of Pennsylvania(US)
- University of Tennessee Health Science Center(US)
- The Ohio State University Wexner Medical Center(US)
- Arkana Laboratories(US)
- The Ohio State University(US)
- Emory University(US)
- Louis Stokes Cleveland VA Medical Center(US)
- University of Lausanne(CH)