O102 Rapid evaporative ionisation mass spectrometry (REIMS) coupled with the hydrosurgical debridement system and the carbon dioxide surgical laser: a proof of concept study in porcine tissues

Abstract

Abstract Introduction Rapid evaporative ionisation mass spectrometry (REIMS) is a tissue profiling technology that allows for rapid information. The successful integration of this technology with surgical devices can be beneficial for diagnostic, prognostic, and individualised treatment purposes. Objectives To access the feasibility of coupling REIMS with the hydrosurgical debridement device and the carbon dioxide surgical laser. Methods The 3D printing attachments and circuit connections were designed for device couplings. The aerosols generated from the hydrosurgical debridement device in terms of mist (applying to porcine liver vs kidney tissue samples), and the carbon dioxide laser in terms of smoke (applying to porcine liver vs kidney cortex vs kidney medulla) were collected for REIMS analysis. The abstract models for tissue classification were built from the obtained mass spectrometry data using the linear discriminant analysis (LDA) dimensional reduction technique. The abstract models were cross-validated and the real-time classifications of different tissue types were tested in both coupling platforms. Results The real-time spectral data signals were reliably achieved from both coupling platforms. For REIMS-hydrosurgical debridement coupling, the abstract model was successfully built from 804 spectra obtained from multiple porcine liver and kidney samples. The cross-validation resulted in 98.01% correct classification rate between the two types of tissue. For REIMS-carbon dioxide laser platform, the abstract model built from 1,454 spectra of porcine tissue samples (liver vs kidney cortex vs kidney medulla) demonstrated 94.36% correct classification rate of the three tissue types. Conclusion REIMS can be coupled with the hydrosurgical debridement device and the carbon dioxide surgical laser with accurate real-time classification of porcine tissues. These platforms have the potential for future translational applications in the clinical arena.