Intelligent three-dimensional imaging in pathology: Applications and developments

Conventional two-dimensional (2D) pathology relies on a limited number of tissue sections and therefore provides information from isolated planes, which can introduce sampling bias and lead to the loss of axial details, making it difficult to accurately capture the full structural organization of tumors and their microenvironment. In recent years, the integration of three-dimensional (3D) imaging technologies, such as tissue clearing and light-sheet fluorescence microscopy, combined with virtual staining, artificial intelligence, and spatial omics, has enabled the acquisition and analysis of volumetric biological information while preserving the overall architecture of the tissue. Following the theme of "advancing from planar observation to 3D quantitative analysis", this paper outlines the major technical pathways of 3D pathology, from sample clearing and fluorescent labeling to 3D imaging and visualization and then to an analytical framework for 3D pathological data. Based on this, we further examined representative applications, including tumor staging, lesion detection, and immune microenvironment profiling, to illustrate the advantages and potential clinical value of 3D pathology over conventional 2D approaches in precision diagnosis, prognostic assessment, and therapeutic decision-making. We also highlight key challenges that remain for 3D pathology, such as storage and computational costs, workflow and diagnostic standardization, and clinical implementation. Overall, AI-enabled 3D pathological imaging holds promise as an important direction for pathology, although its integration into routine clinical decision-making will require continued technological development and translational research.