Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer

Ashley L. Kiemen, Alexander Ioannis Damanakis, Alicia M. Braxton, Jin He, Daniel Laheru, Elliot K. Fishman, Patrick Chames, Cristina Almagro Pérez, Pei Hsun Wu, Denis Wirtz, Laura D. Wood, Ralph H. Hruban

Research output: Contribution to journalReview articlepeer-review

Abstract

Pancreatic cancer is currently the third leading cause of cancer death in the United States. The clinical hallmarks of this disease include abdominal pain that radiates to the back, the presence of a hypoenhancing intrapancreatic lesion on imaging, and widespread liver metastases. Technologies such as tissue clearing and three-dimensional (3D) reconstruction of digitized serially sectioned hematoxylin and eosin-stained slides can be used to visualize large (up to 2- to 3-centimeter cube) tissues at cellular resolution. When applied to human pancreatic cancers, these 3D visualization techniques have provided novel insights into the basis of a number of the clinical characteristics of this disease. Here, we describe the clinical features of pancreatic cancer, review techniques for clearing and the 3D reconstruction of digitized microscope slides, and provide examples that illustrate how 3D visualization of human pancreatic cancer at the microscopic level has revealed features not apparent in 2D microscopy and, in so doing, has closed the gap between bench and bedside. Compared with animal models and 2D microscopy, studies of human tissues in 3D can reveal the difference between what can happen and what does happen in human cancers.

Original languageEnglish (US)
Pages (from-to)75-91
Number of pages17
JournalMed
Volume4
Issue number2
DOIs
StatePublished - Feb 10 2023

Keywords

  • CODA
  • artificial intelligence
  • clearing
  • digital pathology
  • machine learning
  • pancreatic cancer
  • three dimensions

ASJC Scopus subject areas

  • General Medicine

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