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Potential for intraoperative assessment of excised lung samples by X-ray histology

Potential for intraoperative assessment of excised lung samples by X-ray histology
Potential for intraoperative assessment of excised lung samples by X-ray histology
More than 24,000 operations to remove all or part of a patient’s lung take place in Europe each year. The full extent and the type of changes in lung tissue microstructure due to disease may not be evident until surgery is underway, therefore, immediate and informative microanatomical assessment of excised tissue can guide the surgical team or assist with diagnosis.

We explored the feasibility of using micro-Computed Tomography (µCT) based X-ray histology (XRH) for timely 3D histological imaging during surgery. Key criteria considered include: (1) visualisation of relevant microanatomical structures (2) results within the 20-minute intraoperative ‘window’ (3) rapid and comprehensive presentation of data-rich 3D images and (4) compatibility with current intraoperative and post-operative pathology workflows.

For protocol development, porcine lung was used due to its structural similarities to human lung. Using optimised µCT scan conditions at our XRH facility in Southampton, consistent 3D imaging of fresh, unfixed, peripheral lung samples was demonstrated (n=6), providing relevant microanatomical information in less than 10 minutes (Fig. 1). Short scan times (under 4 minutes) minimised sample movement artefacts in the images. Snap-freezing and/or air-inflation of lung samples were also feasible within the intraoperative window. However, both required additional sample preparation steps and introduced artefacts (e.g. cracks, movement, cryo-damage of tissue).

Complementing our rapid XRH imaging protocol, a system for fast, automated generation of user-friendly reports was also developed. This brings together sample images and details for assessment, patient records and clinical team discussions. To streamline the integration of XRH with existing protocols we scanned samples in standard containers used to transfer surgical samples to the pathology lab. This allows fast, non-invasive, non-contact scanning in a sealed container, thus reducing sample handling and avoiding contamination.

Pathologists judged the XRH data generated to be of potential diagnostic quality. This approach provides opportunities for both research and diagnostic use.


To find out more and see if 3D X-ray histology can benefit you, please visit www.xrayhistology.org.
Konstantinopoulou, Elena
Katsamenis, Orestis L.
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Chatelet, David
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Broadbent, Bethany
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Basford, Philip J
efd8fbec-4a5f-4914-bf29-885b7f4677a7
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Roche, William
a5135b2d-cab5-481b-887a-78611fa00bff
Alzetani, Aiman
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Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Konstantinopoulou, Elena
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Chatelet, David
6371fd7a-e274-4738-9ccb-3dd4dab32928
Broadbent, Bethany
0a3d8c25-ddd0-44da-98f7-7d86a85a6dad
Basford, Philip J
efd8fbec-4a5f-4914-bf29-885b7f4677a7
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Roche, William
a5135b2d-cab5-481b-887a-78611fa00bff
Alzetani, Aiman
04d65796-5c8e-4c5b-aeeb-ea093c118f03
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a

Konstantinopoulou, Elena, Katsamenis, Orestis L., Chatelet, David, Broadbent, Bethany, Basford, Philip J, Haig, Ian, Roche, William, Alzetani, Aiman, Schneider, Philipp and Lackie, Peter (2020) Potential for intraoperative assessment of excised lung samples by X-ray histology. 7th Digital Pathology & AI Congress: Europe, , Online. 03 - 04 Dec 2020.

Record type: Conference or Workshop Item (Poster)

Abstract

More than 24,000 operations to remove all or part of a patient’s lung take place in Europe each year. The full extent and the type of changes in lung tissue microstructure due to disease may not be evident until surgery is underway, therefore, immediate and informative microanatomical assessment of excised tissue can guide the surgical team or assist with diagnosis.

We explored the feasibility of using micro-Computed Tomography (µCT) based X-ray histology (XRH) for timely 3D histological imaging during surgery. Key criteria considered include: (1) visualisation of relevant microanatomical structures (2) results within the 20-minute intraoperative ‘window’ (3) rapid and comprehensive presentation of data-rich 3D images and (4) compatibility with current intraoperative and post-operative pathology workflows.

For protocol development, porcine lung was used due to its structural similarities to human lung. Using optimised µCT scan conditions at our XRH facility in Southampton, consistent 3D imaging of fresh, unfixed, peripheral lung samples was demonstrated (n=6), providing relevant microanatomical information in less than 10 minutes (Fig. 1). Short scan times (under 4 minutes) minimised sample movement artefacts in the images. Snap-freezing and/or air-inflation of lung samples were also feasible within the intraoperative window. However, both required additional sample preparation steps and introduced artefacts (e.g. cracks, movement, cryo-damage of tissue).

Complementing our rapid XRH imaging protocol, a system for fast, automated generation of user-friendly reports was also developed. This brings together sample images and details for assessment, patient records and clinical team discussions. To streamline the integration of XRH with existing protocols we scanned samples in standard containers used to transfer surgical samples to the pathology lab. This allows fast, non-invasive, non-contact scanning in a sealed container, thus reducing sample handling and avoiding contamination.

Pathologists judged the XRH data generated to be of potential diagnostic quality. This approach provides opportunities for both research and diagnostic use.


To find out more and see if 3D X-ray histology can benefit you, please visit www.xrayhistology.org.

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More information

Published date: 3 December 2020
Venue - Dates: 7th Digital Pathology & AI Congress: Europe, , Online, 2020-12-03 - 2020-12-04

Identifiers

Local EPrints ID: 448078
URI: http://eprints.soton.ac.uk/id/eprint/448078
PURE UUID: 71f39dc1-31c8-46a2-b226-843a1ee2cbf6
ORCID for Orestis L. Katsamenis: ORCID iD orcid.org/0000-0003-4367-4147
ORCID for Philip J Basford: ORCID iD orcid.org/0000-0001-6058-8270
ORCID for Philipp Schneider: ORCID iD orcid.org/0000-0001-7499-3576
ORCID for Peter Lackie: ORCID iD orcid.org/0000-0001-7138-3764

Catalogue record

Date deposited: 01 Apr 2021 15:41
Last modified: 22 Nov 2021 03:06

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Contributors

Author: Elena Konstantinopoulou
Author: David Chatelet
Author: Bethany Broadbent
Author: Ian Haig
Author: William Roche
Author: Aiman Alzetani
Author: Peter Lackie ORCID iD

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