Liu Jonathan T C, Glaser Adam K, Bera Kaustav, True Lawrence D, Reder Nicholas P, Eliceiri Kevin W, Madabhushi Anant
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
Nat Biomed Eng. 2021 Mar;5(3):203-218. doi: 10.1038/s41551-020-00681-x. Epub 2021 Feb 15.
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.
用于对全活检组织和手术标本进行无玻片三维(3D)病理分析的高通量方法有望使传统组织学工作流程现代化,并提高诊断性能。先进的光学方法现在能够对比以前多几个数量级的组织进行检测,其中体积成像能够增强对具有预后和预测性的细胞分布和组织结构的定量分析。非破坏性成像过程可以简化实验室工作流程,潜在地降低成本,并能确保样本可用于后续的分子检测。然而,它们生成的丰富特征数据集规模巨大,给数据管理和计算机辅助分析带来了挑战。在这篇观点文章中,我们概述了实现3D病理学的成像技术,以及用于图像处理和解读的计算工具,特别是机器学习。我们还讨论了各种其他诊断方式与3D病理学的整合,以及临床应用和监管批准方面的挑战与机遇。