Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.806431
Title: Rapid evaporative ionisation mass spectrometry for breast tissue characterisation towards intraoperative margin assessment in breast surgery
Author: St John, Edward
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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Abstract:
Background: A fifth of patients undergoing breast conserving surgery (BCS) require re-operative intervention for positive margins. Re-operation is costly, negatively affects cosmesis and increases morbidity. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) uses chemical analysis of lipid profiles from electrosurgical aerosol to classify breast tissues in real-time. Methods: Electrosurgical aerosol derived from breast tissues was aspirated into a mass spectrometer (MS). Spectral databases were built from malignant and non-malignant breast tissues with classification models generated using statistical methods. Significantly different peak intensities between tissue classes were identified using univariate statistics and tandem MS aided identification of lipid species. REIMS was performed during breast surgery and classification models were used for identification of cancer at the margin. Results: REIMS was optimised for analysis of heterogeneous breast tissue. An ex-vivo classification model built from n=359 fresh ex-vivo samples allowed for recognition of n=260 new breast tissue samples, achieving 90.9% sensitivity and 98.8% specificity. 112 possible glycerophospholipids were identified following univariate analysis and tandem MS of significant peaks. A combined ex-vivo model of fresh and frozen, cut and coag samples (n=369 tumour, n=467 normal) provided good diagnostic accuracy [sensitivity=94.9% and specificity=94.4%] from which intraoperative recognition models were built. Intraoperatively, continuous high intensity spectra were obtained (n=85 patients, 511 margins) with rapid onscreen results (1.57 seconds, SD +/-0.26), and invasive tumour margins were identified [sensitivity=77.7% (7/9), specificity=92.9% (460/495)]. Conclusions: REIMS has been optimised for analysis of heterogeneous breast tissues based on alterations in lipid metabolism. Multivariate statistical recognition models accurately classify ex-vivo breast tissue. REIMS has potential to be further developed as an intelligent knife (iKnife), capable of rapid data collection and analysis. Pilot data suggests the system shows promise as an intraoperative, real-time, margin assessment tool for breast cancer surgery. A multicentre clinical trial [REI-EXCISE, CRUK/16/021] of REIMS during BCS will provide further validation.
Supervisor: Darzi, Ara ; Leff, Daniel ; Takats, Zoltan Sponsor: Waters Corporation ; European Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.806431  DOI:
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