Zsolt Bodai (Presenter)
Imperial College London
Bio: I did my PhD in analytical chemistry in Hungary, in the field of food analytical chemistry using HPLC-MS/MS. I started working in the United Kingdom at Imperial College London (department of Surgery and Cancer) as a postdoc with Prof. Zoltan Takats at the beginning of September 2015. As a post doc I work on different applications (e.g. early detection of cancer, discovering biomarkers from different biological samples, identification of bacteria based on lipidomic profiles, metabolic phenotyping of lung injury etc.) using various mass spectrometric methods (UPLC-MS, direct injection nano-ESI-MS, REIMS MS, etc.). In 2016, as an appreciation of my enthusiasm and hardworkingness Prof. Takats promoted me to coordinate and lead the iKnife team.
Authorship: Zsolt Bodai (1), Edward St John (1), Emma White (1), Burak Temelkuran (1), Haixing Wang (1), Ho Hui-Yu (1), James McKenzie (1), Francesca Rosini (1), Julia Balog (2), Daniel Leff (1), Guang-Zhong Yang (1), Zoltan Takats (1)
(1) Imperial College London, London, UK (2) Waters Research Centre, Budapest, Hungary
| Short Abstract Intraoperative tissue identification has been a long-standing problem in cancer surgery. Both surgical diathermy and LASER disection coupled with REIMS can provide tissue specific signal from cancerous and healthy breast tissue with excellent tissue classification and identification rate. Spectrum acquired from the surgical plume from both techniques were similar and the found the same significant biomarkers. This potentially means that the previously built REIMS databases constructed from the diathermy technique can be used for LASER dissections. Laser REIMS using a breast cancer diathermy model was tested on normal and cancerous patient samples and provided 100% correct tissue identification. |
Long Abstract
Introduction
Intraoperative tissue identification has been a long-standing problem in cancer surgery, both in context of margin detection and identification of unknown tissue features. While existing intraoperative margin assessment techniques are largely limited to frozen section histology, a number of alternatives have been proposed in course of the last decade, ranging from the fluorescent labelling of cancer cells [1] to the Raman spectroscopic [2] characterisation of surgically exposed tissues. Mass spectrometric methods have also been put forward as a potential solution and mass spectrometric imaging (MSI) approaches were found to provide excellent histological specificity [3-5]. While MSI techniques are complex and time consuming, the recently developed Rapid Evaporative Ionization MS (REIMS) method solves these problems by the on-line chemical analysis of aerosols produced by surgical electrosurgical devices, thereby harnessing the existing role of the electrosurgical tool as a tissue dissection device [6].
REIMS coupled with electrosurgical radiofrequency devices, “diathermy”, have already proved excellent selectivity and specificity on different type of tissues [6-8]; however, some surgical intervention requires better precision and better spatial resolution during the dissection. Surgical LASER disection is able to offer these benefits and due to the recently developed hollow core waveguide optic system [9] the beam is easily controlled and can reach difficult areas. CO2 laser energy is rapidly absorbed by water so it has less thermal spread around the tissue. The lower thermal damage results in clean margins, faster healing and also allows surgeon to work safe resections near critical structures.
The aim of this study was to test a surgical LASER system to determine if it is suitable for real-time tissue identification on healthy and cancerous breast tissue. For this purpose an ex-vivo database was built of histologically annotated REIMS spectrums. We also aimed to compare the spectrums acquired with standard diathermy and the LASER with univariate and multivariate statistic methods.
Methods
A CO2 LASER (l=10.6 µm, Omniguide, Lexington, MA, USA) and a Covidien ForceTriad generator was used for tissue sampling. The LASER was placed into a surgical handpiece equipped with a PTFE tube toward to the mass spectrometer for the `surgical smoke` evacuation. The captured aerosol was aspirated into a custom-built Xevo G2-XS quadrupole – time-of-flight (Q-TOF) MS (Waters, Wilmslow, UK). During the mass spectrometry analysis 2-propanol was used as a matrix solvent to enhance the ionisation, for lock mass introduction and also to prevent the contamination of the mass spectrometer.
Healthy and cancerous breast tissue were sampled with both the surgical diathermy and LASER. To compare the methods samples were analysed with univariate and multivariate statistical methods. ANOVA test was applied in Matlab (MathWorks, Massachusetts, USA) to discover significant biomarker which are different in cancerous and healthy spectrums.
ANOVA was used to assess the spectral differences between LASER and diathermy. Significant biomarkers were identified based on their MS/MS spectrum and exact mass.
Tissue identification was achieved by using a multivariate statistical method-based machine learning algorithm and a database of histologically annotated REIMS spectra. Offline model builder (Waters Research Centre, Budapest, Hungary) was used for these analysis. After the data collection combined principal component analysis (PCA) and linear discriminant analysis (LDA) are used for dimension reduction and classification. Leave one patient out cross validation was applied to compare the diathermy and LASER tissue classification. Models were imported to OMB recognition software to test the methods for real-time tissue identification. LASER data was also tested with diathermy database in order to test the necessity of the database rebuilding.
All of the sampled tissue were analysed either fresh following excision or frozen and stored at -80°C and thawed prior the analysis. For the sampling ethical approval was obtained from the Research Ethics Committee.
Results and discussion
Surgical plume formed with LASER or diathermy was transferred into the mass spectrometer. Acquired spectrums with both dissection tool were similar; however, LASER spectrums looked cleaner due to the better signal to noise ratio. To compare the LASER and diathermy methods from diagnostic point of view, significant peaks were looked for with both technique between cancerous and normal tissue. After the ANOVA tests same peaks were founded with both methods. In general, adipose tissues mainly contained triglyceride signals whereas cancer tissues had more phospholipids.
Data was also analysed with multivariate statistic methods. Separation on PCA score plot was clear between cancerous and normal tissues with the LASER. LASER data was also plotted together with the diathermy data on the same score plots and did show clear separation between the diathermy and the LASER but also good separation between cancerous and normal tissue types. Since the LASER spectrums looked similar (with better signal to noise ratio) to the diathermy spectrums, LASER samples were run in OMB recognition software using the diathermy LDA model. Testing the LASER samples using the diathermy model provided excellent (100%) correct tissue identification with 6 normal and 4 cancerous patient samples (total 25 sampling point) which means rebuilding the model on large number of ex-vivo sample is not really necessary.
Conclusion
The iKnife technology is a promising novel tool in cancer surgery. This data demonstrates that either the surgical diathermy or the LASER coupled with REIMS can provide tissue specific signal from cancerous and healthy breast tissue with excellent tissue classification and identification rate. Spectrum acquired from the surgical plume were similar and same significant biomarker were found with both surgical devices. This potentially can mean that the previously built database with the handheld diathermy can be used for surgical LASER applications. LASER surgery has other benefits compared to the diathermy e.g. higher spatial resolution and lower thermal damage which may indicate faster healing and better outcome for the patient.
References & Acknowledgements:
References
1. Gao, X., et al., In vivo cancer targeting and imaging with semiconductor quantum dots. Nat Biotech, 2004. 22(8): p. 969-976.
2. Haka, A.S., et al., In vivo margin assessment during partial mastectomy breast surgery using raman spectroscopy. Cancer Res, 2006. 66(6): p. 3317-22.
3. Guenther, S., et al., Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry. Cancer Res, 2015. 75(9): p. 1828-37.
4. Gerbig, S., et al., Analysis of colorectal adenocarcinoma tissue by desorption electrospray ionization mass spectrometric imaging. Anal Bioanal Chem, 2012. 403(8): p. 2315-25.
5. Abbassi-Ghadi, N., et al., Imaging of Esophageal Lymph Node Metastases by Desorption Electrospray Ionization Mass Spectrometry. Cancer Res, 2016.
6. Schafer, K.C., et al., In vivo, in situ tissue analysis using rapid evaporative ionization mass spectrometry. Angew Chem Int Ed Engl, 2009. 48(44): p. 8240-2.
7. Balog, J., et al., Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med, 2013. 5(194): p. 194ra93.
8. Balog, J., et al., Identification of biological tissues by rapid evaporative ionization mass spectrometry. Anal Chem, 2010. 82(17): p. 7343-50.
9. Temelkuran, B., et al., Wavelength-scalable hollow optical fibres with large photonic bandgaps for CO2 laser transmission. Nature, 2002. 420(6916): p. 650-653.
Acknowledgments
We are grateful for both technical and financial support for Waters Corporation.
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