Validation of digital microscopy: Review of validation methods and sources of bias. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Bertram, Christof A.; Stathonikos, Nikolas; Donovan, Taryn A.; Bartel, Alexander; Fuchs-Baumgartinger, Andrea; Lipnik, Karoline; van Diest, Paul J.; Bonsembiante, Federico; Klopfleisch, Robert
• Is Part Of: Veterinary pathology. Volume 59:Issue 1(2022)
• Page Start: 26
• Page End: 38
• Publication Date: 2022-01
• Subjects: accuracy -- concordance rate -- digital pathology -- digital microscopy -- noninferiority -- review -- study design -- validation -- virtual microscopy -- whole-slide images; Veterinary pathology -- Periodicals; Pathology, Veterinary -- Periodicals; 636.089607
• DOI: 10.1177/03009858211040476
• Languages: English
• ISSNs: 0300-9858

Abstract

Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.