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The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS)

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Title: The surgical intelligent knife distinguishes normal, borderline and malignant gynaecological tissues using rapid evaporative ionisation mass spectrometry (REIMS)
Authors: Phelps, DL
Balog, J
Gildea, LF
Bodai, Z
El-Bahrawy, MA
Speller, AVM
Rosini, F
Kudo, H
McKenzie, JS
Brown, R
Takats, Z
Ghaem-Maghami, S
Item Type: Journal Article
Abstract: Background Survival from ovarian cancer (OC) is improved with surgery, but surgery can be complex and tumour identification, especially for borderline ovarian tumours (BOT), is challenging. The Rapid Evaporative Ionisation Mass Spectrometric (REIMS) technique reports tissue histology in real-time by analysing aerosolised tissue during electrosurgical dissection. Methods Aerosol produced during diathermy of tissues was sampled with the REIMS interface. Histological diagnosis and mass spectra featuring complex lipid species populated a reference database on which principal component, linear discriminant and leave-one-patient-out cross-validation analyses were performed. Results A total of 198 patients provided 335 tissue samples, yielding 3384 spectra. Cross-validated OC classification vs separate normal tissues was high (97·4% sensitivity, 100% specificity). BOT were readily distinguishable from OC (sensitivity 90.5%, specificity 89.7%). Validation with fresh tissue lead to excellent OC detection (100% accuracy). Histological agreement between iKnife and histopathologist was very good (kappa 0.84, P < 0.001, z = 3.3). Five predominantly phosphatidic acid (PA(36:2)) and phosphatidyl-ethanolamine (PE(34:2)) lipid species were identified as being significantly more abundant in OC compared to normal tissue or BOT (P < 0.001, q < 0.001). Conclusions The REIMS iKnife distinguishes gynaecological tissues by analysing mass-spectrometry-derived lipidomes from tissue diathermy aerosols. Rapid intra-operative gynaecological tissue diagnosis may improve surgical care when histology is unknown, leading to personalised operations tailored to the individual.
Issue Date: 19-Apr-2018
Date of Acceptance: 2-Feb-2018
URI: http://hdl.handle.net/10044/1/56732
DOI: https://dx.doi.org/10.1038/s41416-018-0048-3
ISSN: 0007-0920
Publisher: Cancer Research UK
Start Page: 1349
End Page: 1358
Journal / Book Title: British Journal of Cancer
Volume: 118
Copyright Statement: © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article ’ s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/
Sponsor/Funder: Cancer Research UK
Cancer Research UK
National Institute for Health Research
Funder's Grant Number: 13086
C17375/A16989
RDB01
Keywords: 1112 Oncology And Carcinogenesis
Oncology & Carcinogenesis
Publication Status: Published
Appears in Collections:Division of Surgery
Division of Cancer
Department of Medicine
Faculty of Medicine



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