Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives
Open accessAbstract
Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients' whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL.
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Doeleman et al.'s weakly supervised models for differentiation of early MF and BIDs on H&E-stained sections achieved mean slide-level classification accuracy around 76%, comparable with the performance of expert pathologists.
“Recently, Doeleman et al. trained weakly supervised models for the differentiation of early MF and BIDs on H&E-stained sections. The results are encouraging: the mean slide-level classification accuracy was around 76%, comparable with the performance of expert pathologists [37,38].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [37]
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Artificial intelligence in dermatopathology: Updates, strengths, and challenges
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“The results are encouraging: the mean slide-level classification accuracy was around 76%, comparable with the performance of expert pathologists [37,38].”
- Authors
- Doeleman, T., Hondelink, L. M., Vermeer, M. H., van Dijk, M. R., & Schrader, A. M. R.
- Affiliations
- Leiden University Medical Center (NL)
- Corresponding author
- Doeleman, Thom
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 94
- Pages
- 81-88
- ISSN
- 1044-579X (ISSN-L)
- OpenAlex
- W4380988494
- Cited by
- 26
- References
- 75
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 10 · 2023: 1
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Seminars in Cancer Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Dermatology › Cutaneous lymphoproliferative disorders research
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- primary cutaneous lymphoma, artificial intelligence, digital pathology, whole slide images, T-cell lymphoma, B-cell lymphoma, histopathology, diagnostic biomarkers, lymphoma subclassification, skin cancer, outcome prediction
- MeSH terms
- Artificial Intelligence, Humans, Lymphoma, Skin Neoplasms, Biomarkers, Lymphoma, B-Cell
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