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Breast Cancer Research
Published in: Breast Cancer Research 1/2017

Open Access 01-12-2017 | Research article

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery

Authors: Edward R. St John, Julia Balog, James S. McKenzie, Merja Rossi, April Covington, Laura Muirhead, Zsolt Bodai, Francesca Rosini, Abigail V. M. Speller, Sami Shousha, Rathi Ramakrishnan, Ara Darzi, Zoltan Takats, Daniel R. Leff

Published in: Breast Cancer Research | Issue 1/2017

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Abstract

Background

Re-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.

Methods

Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.

Results

A classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).

Conclusions

The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.
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Metadata
Title
Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery
Authors
Edward R. St John
Julia Balog
James S. McKenzie
Merja Rossi
April Covington
Laura Muirhead
Zsolt Bodai
Francesca Rosini
Abigail V. M. Speller
Sami Shousha
Rathi Ramakrishnan
Ara Darzi
Zoltan Takats
Daniel R. Leff
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2017
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-017-0845-2

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