Advances in Tissue‐Clearing and Volumetric Imaging: A Technical Framework Toward Three‐Dimensional Pathology
Three-dimensional (3D) pathological diagnosis based on tissue-clearing technology has emerged as a promising extension of conventional slide-based pathology by enabling volumetric evaluation of intact or thick specimens. This review summarizes advances from 2020 to 2026 in five key areas: tissue-clearing methodologies, fluorescence labeling, imaging modalities, intelligent data processing, and preclinical and clinical applications. Recent progress has improved clearing speed, deep labeling capability, imaging throughput, and computational analysis, allowing pathological observation to shift from two-dimensional (2D) sections to continuous tissue volumes. In particular, compatible light-sheet and open-top volumetric imaging platforms, together with AI-enabled reconstruction, segmentation, virtual staining, and risk modeling, are accelerating the transition from image acquisition to interpretable 3D pathology. Preclinical studies have expanded the applicability of tissue clearing to complex and hard tissues, whereas clinical studies in human specimens have begun to demonstrate diagnostically relevant 3D histology, compatibility with routine workflows, and quantitative features associated with clinical outcomes. Nevertheless, substantial challenges remain in workflow standardization, deep and multiplex labeling, platform accessibility, large-scale data integration, and clinically validated interpretation criteria. Future progress will depend on establishing integrated end-to-end workflows to advance 3D pathology toward a clinically deployable diagnostic platform.
Authors
- J ZHU
- Hui Li (ORCID: https://orcid.org/0000-0001-9198-3951)
- Yating Yi (ORCID: https://orcid.org/0000-0003-2283-1959)
- Jiawei Hong
Institutions
- Sichuan University (CN)
- National Clinical Research (US)
Publication Details
- Journal
- Chemistry - A European Journal
- Published
- 2026-07-18
- DOI
- https://doi.org/10.1002/chem.71425
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Sichuan University
- Sichuan Medical Association