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Multiresolution nondestructive 3D pathology of whole lymph nodes for breast cancer staging

Significance: For breast cancer patients, the extent of regional lymph node (LN) metastasis influences the decision to remove all axillary LNs. Metastases are currently identified and classified with visual analysis of a few thin tissue sections with conventional histology that may underrepresent th...

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Published in:Journal of biomedical optics 2022-03, Vol.27 (3), p.036501-036501
Main Authors: Barner, Lindsey A, Glaser, Adam K, Mao, Chenyi, Susaki, Etsuo A, Vaughan, Joshua C, Dintzis, Suzanne M, Liu, Jonathan T. C
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container_title Journal of biomedical optics
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creator Barner, Lindsey A
Glaser, Adam K
Mao, Chenyi
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Dintzis, Suzanne M
Liu, Jonathan T. C
description Significance: For breast cancer patients, the extent of regional lymph node (LN) metastasis influences the decision to remove all axillary LNs. Metastases are currently identified and classified with visual analysis of a few thin tissue sections with conventional histology that may underrepresent the extent of metastases. Aim: We sought to enable nondestructive three-dimensional (3D) pathology of human axillary LNs and to develop a practical workflow for LN staging with our method. We also sought to evaluate whether 3D pathology improves staging accuracy in comparison to two-dimensional (2D) histology. Approach: We developed a method to fluorescently stain and optically clear LN specimens for comprehensive imaging with multiresolution open-top light-sheet microscopy. We present an efficient imaging and data-processing workflow for rapid evaluation of H&E-like datasets in 3D, with low-resolution screening to identify potential metastases followed by high-resolution localized imaging to confirm malignancy. Results: We simulate LN staging with 3D and 2D pathology datasets from 10 metastatic nodes, showing that 2D pathology consistently underestimates metastasis size, including instances in which 3D pathology would lead to upstaging of the metastasis with important implications on clinical treatment. Conclusions: Our 3D pathology method may improve clinical management for breast cancer patients by improving staging accuracy of LN metastases.
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We present an efficient imaging and data-processing workflow for rapid evaluation of H&amp;E-like datasets in 3D, with low-resolution screening to identify potential metastases followed by high-resolution localized imaging to confirm malignancy. Results: We simulate LN staging with 3D and 2D pathology datasets from 10 metastatic nodes, showing that 2D pathology consistently underestimates metastasis size, including instances in which 3D pathology would lead to upstaging of the metastasis with important implications on clinical treatment. 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subjects Axilla - pathology
Breast cancer
Breast Neoplasms - pathology
Data processing
Datasets
Female
Fluorescence microscopy
Histology
Humans
Image resolution
Labeling
Light sheets
Lymph nodes
Lymph Nodes - diagnostic imaging
Lymph Nodes - pathology
Lymphatic Metastasis - diagnostic imaging
Lymphatic system
Malignancy
Medical imaging
Metastases
Metastasis
Methods
Microscopy
Neoplasm Staging
Pathology
Patients
Stains & staining
Surgery
Three dimensional imaging
Workflow
title Multiresolution nondestructive 3D pathology of whole lymph nodes for breast cancer staging
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