AccScience Publishing / TD / Online First / DOI: 10.36922/TD026030007
PERSPECTIVE ARTICLE

From mimicry to mechanism: Digital pathology and multi-omics perspectives on cutaneous T-cell lymphoma

Mansak Shishak1* Somesh Gupta2
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1 Department of Dermatology, Fortis Hospital, New Delhi, India
2 Department of Dermatology, All India Institute of Medical Sciences, New Delhi, India
Tumor Discovery, 026030007 https://doi.org/10.36922/TD026030007
Received: 18 January 2026 | Revised: 15 April 2026 | Accepted: 17 April 2026 | Published online: 12 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Primary cutaneous lymphoma, particularly cutaneous T-cell lymphoma (CTCL), represents a malignancy long defined by protean manifestations, diagnostic delay, clinical deception, and therapeutic ambiguity. Despite being confined to the skin at presentation, CTCL exhibits marked biological heterogeneity and phenotypic variation. Diagnostic workflows reliant on serial biopsies and stage-based classifications fail to adequately capture this complexity, contributing to suboptimal intervention. In this perspective, we position CTCL as a discovery problem, highlighting the limitations of single-layer diagnostics in distinguishing malignant disease from inflammatory mimics. We discuss how emerging digital pathology, artificial intelligence-enabled imaging, and integrative interdisciplinary approaches can interrogate disease across spatial and molecular scales. By reframing CTCL through a systems-level lens, such strategies have the potential to enable earlier biological stratification, inform mechanism-driven therapeutic targeting, and guide the rational use of skin-directed and systemic treatments. Without such integrative frameworks, innovation in CTCL treatment risks remaining fragmented and incomplete.

Keywords
Cutaneous lymphoma
Cutaneous T-cell lymphoma
Digital histology
Artificial intelligence
Multi-omics
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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Tumor Discovery, Electronic ISSN: 2810-9775 Print ISSN: 3060-8597, Published by AccScience Publishing