Tissue Identification From Surgical Smoke by Differential Mobility Spectrometry: An in Vivo Study

The increasing number of breast cancer survivors and their longevity has emphasized the importance of esthetic and functional outcomes of cancer surgery and increased pressure for the surgical treatment to achieve negative margins with minimal removal of healthy tissue. Surgical smoke has been successfully utilized in tissue identification in laboratory conditions by using a system based on differential mobility spectrometry (DMS) that could provide a seamless margin assessment method. In this study, a DMS-based tissue analysis system was used intraoperatively in 20 breast cancer surgeries to assess its feasibility in tissue identification. The effect of the system on complications and duration of surgeries was also studied. The surgeries were recorded with a head-worn camera system for visual annotation of the operated tissue types to enable classification of the measurement files by supervised learning. There were statistically significant differences among the DMS spectra of the tissue types. The classification of four tissue types (skin, fat, glandular tissue, and connective tissue) yielded a cross-validated accuracy of 44% and exhibited high variation between surgeries. The low accuracies can be attributed to the limitations and uncertainty of the visual annotation, high-within class variance due to the heterogeneity of tissues as well as environmental conditions, and delays of the real-time analysis of the smoke samples. Differences between tissues encountered in breast surgery were identified and the technology can be implemented in surgery workflow. However, in its current state, the DMS-based system is not yet applicable to a clinical setting to aid in margin assessment.