Open AccessHarnessing non-destructive 3D pathology
Author(s) -
Jonathan Liu,
Adam K. Glaser,
Kaustav Bera,
Lawrence D. True,
Nicholas P. Reder,
Kevin W. Eliceiri,
Anant Madabhushi
Publication year - 2021
Publication title -
nature biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.961
H-Index - 56
ISSN - 2157-846X
DOI - 10.1038/s41551-020-00681-x
Subject(s) - workflow , computer science , digital pathology , modalities , feature (linguistics) , artificial intelligence , pathology , medical physics , medicine , linguistics , philosophy , database , sociology , social science
High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.