Context.—: Anatomic pathologists render diagnosis on tissue samples sectioned onto glass slides and viewed under a bright-field microscope. This approach is destructive to the sample, which can limit its use for ancillary assays that can inform patient management. Furthermore, the subjective interpretation of a relatively small number of 2D tissue sections per sample contributes to low interobserver agreement among pathologists for the assessment (diagnosis and grading) of various lesions.
Objective.—: To evaluate 3D pathology data sets of thick formalin-fixed Barrett esophagus specimens imaged nondestructively with open-top light-sheet (OTLS) microscopy.
Design.—: Formalin-fixed, paraffin-embedded Barrett esophagus samples (N = 15) were deparaffinized, stained with a fluorescent analog of hematoxylin-eosin, optically cleared, and imaged nondestructively with OTLS microscopy. The OTLS microscopy images were subsequently compared with archived hematoxylin-eosin histology sections from each sample.
Results.—: Barrett esophagus samples, both small endoscopic forceps biopsies and endoscopic mucosal resections, exhibited similar resolvable structures between OTLS microscopy and conventional light microscopy with up to a ×20 objective (×200 overall magnification). The 3D histologic images generated by OTLS microscopy can enable improved discrimination of cribriform and well-formed gland morphologies. In addition, a much larger amount of tissue is visualized with OTLS microscopy, which enables improved assessment of clinical specimens exhibiting high spatial heterogeneity.
Conclusions.—: In esophageal specimens, OTLS microscopy can generate images comparable in quality to conventional light microscopy, with the advantages of providing 3D information for enhanced evaluation of glandular morphologies and enabling much more of the tissue specimen to be visualized nondestructively.
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http://dx.doi.org/10.5858/arpa.2022-0133-OA | DOI Listing |
Open-top light-sheet (OTLS) microscopy offers rapid 3D imaging of large optically cleared specimens. This enables nondestructive 3D pathology, which provides key advantages over conventional slide-based histology including comprehensive sampling without tissue sectioning/destruction and visualization of diagnostically important 3D structures. With 3D pathology, clinical specimens are often labeled with small-molecule stains that broadly target nucleic acids and proteins, mimicking conventional hematoxylin and eosin (H&E) dyes.
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May 2024
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address:
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features.
View Article and Find Full Text PDFArXiv
July 2023
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation, with concomitant risks of sampling bias and misdiagnosis. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities.
View Article and Find Full Text PDFJ Pathol
August 2023
Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images.
View Article and Find Full Text PDFMethods Mol Biol
May 2023
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Organoids have emerged as a promising advancement of the two-dimensional (2D) culture systems to improve studies in organogenesis, drug discovery, precision medicine, and regenerative medicine applications. Organoids can self-organize as three-dimensional (3D) tissues derived from stem cells and patient tissues to resemble organs. This chapter presents growth strategies, molecular screening methods, and emerging issues of the organoid platforms.
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