Whole slide images for primary diagnostics in dermatopathology: A feasibility study
Abstract
BACKGROUND: During the last decade, whole slide images (WSI) have been used in many areas of pathology such as teaching, research, digital archiving, teleconsultation and quality assurance testing. However, WSI have not regularly been used for routine diagnosis, because of the lack of validation studies. AIM: To test the validity of using WSI for primary diagnosis of skin diseases. MATERIALS AND METHODS: 100 skin biopsies and resections which had been diagnosed light microscopically one year previously were scanned at 20× magnification, and rediagnosed by six pathologists (every pathologist assessed his own cases), having the original clinical information available, but blinded to the original diagnoses. The WSI diagnoses were compared to the initial light microscopy diagnosis and classified as concordant, slightly discordant (without clinical consequences) or discordant. RESULTS: The light microscopy and the WSI based diagnosis were concordant in 94% of the cases. The light microscopy and WSI diagnosis were slightly discordant in 6% of the cases. For one of the slightly discrepant cases the WSI diagnosis was considered better, while the original diagnosis was preferred for the other five cases. There were no discordant cases with clinical or prognostic implications. CONCLUSION: Primary histopathological diagnosis of skin biopsies and resections can be done digitally using WSI.
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WSIs allow comprehensive evaluation of tissue architecture and cellular morphology while facilitating storage and data sharing across institutions.
“WSIs allow comprehensive evaluation of tissue architecture and cellular morphology while facilitating storage and data sharing across institutions [41].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [41]
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“WSIs allow comprehensive evaluation of tissue architecture and cellular morphology while facilitating storage and data sharing across institutions [41].”
- Authors
- Al-Janabi, S., Huisman, A., Vink, A., Leguit, R. J., Offerhaus, G. J. A., ten Kate, F. J. W., van Dijk, M. R., & van Diest, P. J.
- Affiliations
- University Medical Center Utrecht (NL)
- Venue
- Journal of Clinical Pathology
- Publisher
- BMJ
- Published
- 2011-10-26
- Volume
- 65
- Issue
- 2
- Pages
- 152-158
- ISSN
- 0021-9746 (ISSN-L)
- 1472-4146
- OpenAlex
- W1971399443
- Cited by
- 81
- References
- 36
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 5 · 2020: 5 · 2019: 6 · 2018: 9 · 2017: 11 · 2016: 7 · 2015: 6 · 2014: 8 · 2013: 5 · 2012: 4
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Pathology (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Dermatology › Medicine and Dermatology Studies History
- Keywords
- whole slide images, dermatopathology, digital histopathology, primary diagnosis, skin biopsy, diagnostic agreement, light microscopy comparison, diagnostic accuracy, skin diseases
- MeSH terms
- Biopsy, Feasibility Studies, Humans, Image Interpretation, Computer-Assisted, Microscopy, Pathology, Clinical, Skin, Skin Diseases, Observer Variation, Single-Blind Method
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