Tissue Identification in a Porcine Model by Differential Ion Mobility Spectrometry Analysis of Surgical Smoke
Electrosurgery is widely used in various surgical operations. When tissue is cut with high-frequency current, the cell contents at the incision area evaporate and together with water and possible soot particles, form surgical smoke. The smoke contains cell metabolites, and therefore, possible biomarkers for cancer or bacterial infection. Thus, the analysis of surgical smoke could be used in intraoperative medical diagnostics. We present a method that can be used to detect the characteristics of various tissue types by means of differential ion mobility spectrometry (DMS) analysis of surgical smoke. We used our method to test tissue identification with ten different porcine tissues. We classified the DMS responses with cross-validated linear discriminant analysis models. The classification accuracy in a measurement set with ten tissue types was 95%. The presented tissue identification by DMS analysis of surgical smoke is a proof-of-concept, which opens the possibility to research the method in diagnosing human tissues and diseases in the future.
KeywordsFAIMS Electrosurgery LDA VOC
Differential ion mobility spectrometry
High-field asymmetric waveform ion mobility spectrometry
Linear discriminant analysis
Rapid evaporative ionization mass spectrometry
Automatic tissue analysis system
Voltage amplitude of the asymmetric waveform
Voltage of the DC compensation field
Volatile organic compound
This study was supported by grants from the following foundations: Finnish Cultural Foundation, South Savo Regional fund, Finnish Foundation for Technology Promotion (TES), Tampereen Tuberkuloosisäätiö (Tampere Tuberculosis Foundation), Emil Aaltonen foundation and Pirkanmaan sairaanhoitopiiri (PSHP) Grants 9s045, 151B03, 9T044, 9U042 and 150618. The study material used in the research was slaughterhouse offal. Ethical approval from the Ethics Committee of the Tampere Region was not needed for conducting this research, as confirmed from the committee itself. Authors Markus Karjalainen, Antti Roine, Niku Oksala, and Jukka Lekkala are shareholders of Olfactomics Ltd (www.olfactomics.fi), a company developing applications for eNose technology. The authors would like to thank D.Sc. Ville Rantanen, D.Sc. Pekka Kumpulainen, and Assistant Professor Antti Vehkaoja for their comments and contributions to the final manuscript.
- 1.Allweis, T. M., Z. Kaufman, S. Lelcuk, I. Pappo, T. Karni, S. Schneebaum, R. Spector, A. Schindel, D. Hershko, M. Zilberman, J. Sayfan, Y. Berlin, A. Hadary, O. Olsha, H. Paran, M. Gutman, and M. Carmon. A prospective, randomized, controlled, multicenter study of a real-time, intraoperative probe for positive margin detection in breast-conserving surgery. Am. J. Surg. 196:483–489, 2008.CrossRefPubMedGoogle Scholar
- 2.Baggish, M. S., R. F. Valle, and H. Guedj. Hysteroscopy: Visual Perspectives of Uterine Anatomy, Physiology and Pathology. London: Lippincott Williams & Wilkins, 2007, 125 pp.Google Scholar
- 3.Balog, J., D. Perenyi, C. Guallar-Hoyas, A. Egri, S. D. Pringle, S. Stead, O. P. Chevallier, C. T. Elliott, and Z. Takats. Identification of the species of origin for meat products by rapid evaporative ionization mass spectrometry. J. Agric. Food Chem. 64:4793–4800, 2016.CrossRefPubMedGoogle Scholar
- 4.Balog, J., L. Sasi-Szabó, J. Kinross, M. R. Lewis, L. J. Muirhead, K. Veselkov, R. Mirnezami, B. Dezso, L. Damjanovich, A. Darzi, J. K. Nicholson, and Z. Takáts. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci. Transl. Med. 5:194ra93, 2013.CrossRefPubMedGoogle Scholar
- 6.Bardin-Monnier, N., and D. Thomas. Initial Pressure Drop for Fibrous Media BT—Aerosol Filtration. Amsterdam: Elsevier, 2017, pp. 49–78.Google Scholar
- 7.Eiceman, G. A., Z. Karpas, and H. H. Hill, Jr. Ion Mobility Spectrometry. CRC Press, 2013, pp. 1–293.Google Scholar
- 8.Izenman, A. J. Recursive partitioning and tree-based methods BT. In: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, edited by A. J. Izenman. New York: Springer New York, 2008, pp. 281–314. https://doi.org/10.1007/978-0-387-78189-1_9.
- 10.Munro, M. G. Fundamentals of electrosurgery, Part I: principles of radiofrequency energy for surgery BT. In: The SAGES Manual on the Fundamental Use of Surgical Energy (FUSE), edited by L. Feldman, P. Fuchshuber, and D. B. Jones. New York: Springer New York, 2012, pp. 15–59.Google Scholar
- 14.Quadrianto, N., and W. L. Buntine. Linear discriminant. In: Encyclopedia of Machine Learning, edited by C. Sammut, and G. I. Webb. Boston: Springer US, 2010, pp. 601–603. DOI: https://doi.org/10.1007/978-0-387-30164-8_475.
- 18.St John, E. R., J. Balog, J. S. McKenzie, M. Rossi, A. Covington, L. Muirhead, Z. Bodai, F. Rosini, A. V. M. Speller, S. Shousha, R. Ramakrishnan, A. Darzi, Z. Takats, and D. R. Leff. Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery. Breast Cancer Res. 19(1):59, 2017.CrossRefPubMedPubMedCentralGoogle Scholar
- 21.Verplanken, K., S. Stead, R. Jandova, C. V. Poucke, J. Claereboudt, J. V. Bussche, S. D. Saeger, Z. Takats, J. Wauters, and L. Vanhaecke. Rapid evaporative ionization mass spectrometry for high-throughput screening in food analysis: the case of boar taint. Talanta 169:30–36, 2017.CrossRefPubMedGoogle Scholar
- 22.Watson, J. T., and O. D. Sparkman. Introduction to Mass Spectrometry: Instrumentation, Applications, And strategies for Data Interpretation. Chichester: Wiley, 2007, pp. 25–26.Google Scholar