Artificial intelligence in digital pathology of cutaneous lymphomas: A review of the current state and future perspectives.

Autor: Doeleman T; Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands. Electronic address: t.doeleman@lumc.nl., Hondelink LM; Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands., Vermeer MH; Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands., van Dijk MR; Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands., Schrader AMR; Department of Pathology, Leiden University Medical Centre, Leiden, the Netherlands.
Jazyk: angličtina
Zdroj: Seminars in cancer biology [Semin Cancer Biol] 2023 Sep; Vol. 94, pp. 81-88. Date of Electronic Publication: 2023 Jun 17.
DOI: 10.1016/j.semcancer.2023.06.004
Abstrakt: 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.
Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest.
(Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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