Anton Kontunen

Background: Basal cell carcinoma (BCC) is a nonmelanocytic skin cancer and the most common malignancy in Caucasians. Diagnostics and treatment of BCC cause significant health‐related stress for many patients and costs for public health care systems. Differential mobility spectrometry (DMS) is a sensitive method for detection of gaseous molecules. The DMS‐derived automatic tissue analysis system (ATAS) utilises diathermy‐generated surgical smoke to distinguish cancerous tissue from normal tissue based on lipid profiling between the tissues. Objectives: The aim of this study was to create a surrogate porcine model to test the feasibility of the ATAS in lipid detection of skin. Another objective was to determine whether BCC of human skin could be identified from healthy skin using lipid profiling. Methods: Porcine ear skin was used to establish a three‐group porcine model for lipid profile detection. Lecithin was chosen as a marker to demonstrate elevated phospholipid levels in one of the groups. We also recruited five BCC patients to collect BCC tumour biopsies and healthy skin biopsies to test the model in human samples. In both models, all samples were processed with the ATAS to test the accuracy of lipid profiling and resolution between the groups. Results: In the porcine model , the classification accuracy was 74.5% for three groups (unprocessed porcine skin, fine‐grained porcine skin, and lecithin‐marked fine‐grained porcine skin) and 91.8% for two groups (unprocessed porcine skin and fine‐grained porcine skin combined into one group in comparison to lecithin‐marked fine‐grained porcine skin). The support vector machine (SVM) classifier model trained on porcine surrogate samples was then used to analyse a small number of human BCC and healthy skin samples with 95% accuracy. Conclusion: DMS‐based differentiation of porcine skin samples based on surgical smoke is possible. This study is a step towards a method to distinguish human BCC from healthy skin from surgical smoke by the ATAS. The presented skin identification of DMS analysis of surgical smoke opens the possibility to research the method in a larger sample number of human BCC and healthy skin samples as well as develop the method and ATAS towards a clinical tool for margin assessment.

Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or vibrational energy to heat tissues, which causes intra- and extracellular water to expand rapidly and degrade cell structures, bursting the cells, which causes the formation of a tissue aerosol or smoke depending on the amount of energy used. This gas phase analyte can then be analyzed via gas analysis methods. Differential mobility spectrometry (DMS) is a method that can be used to differentiate malignant tissue from benign tissues in real time via the analysis of surgical smoke produced by energy instruments. Previously, the DMS identification of cancer tissue was based on a ‘black box method’ by differentiating the 2D dispersion plots of samples. This study sets out to find datapoints from the DMS dispersion plots that represent relevant target molecules. We studied the ability of DMS to differentiate three subclasses of phospholipids (phosphatidylcholine, phosphatidylinositol, and phosphatidylethanolamine) from a control sample using a bovine skeletal muscle matrix with a 5 mg addition of each phospholipid subclass to the sample matrix. We trained binary classifiers using linear discriminant analysis (LDA) and support vector machines (SVM) for sample classification. We were able to identify phosphatidylcholine, -inositol, and -ethanolamine with SVM binary classification accuracies of 91%, 73%, and 66% and with LDA binary classification accuracies of 82%, 74%, and 72%, respectively. Phosphatidylcholine was detected with a reliable classification accuracy, but ion separation setups should be adjusted in future studies to reliably detect other relevant phospholipids such as phosphatidylinositol and phosphatidylethanolamine and improve DMS as a microanalysis method and identify other phospholipids relevant to cancer tissue.

The aim was to investigate how the congruence of odors and visual objects in virtual reality (VR) affects later memory recall of the objects. Participants (N = 30) interacted with 12 objects in VR. The interaction was varied by odor congruency (i.e., the odor matched the object’s visual appearance, the odor did not match the object’s visual appearance, or the object had no odor); odor quality (i.e., an authentic or a synthetic odor); and interaction type (i.e., participants could look and manipulate or could only look at objects). After interacting with the 12 objects, incidental memory performance was measured with a free recall task. In addition, the participants rated the pleasantness and arousal of the interaction with each object. The results showed that the participants remembered significantly more objects with congruent odors than objects with incongruent odors or odorless objects. Furthermore, interaction with congruent objects was rated significantly more pleasant and relaxed than interaction with incongruent objects. Odor quality and interaction type did not have significant effects on recall or emotional ratings. These results can be utilized in the development of multisensory VR applications.

Isocitrate dehydrogenase (IDH) mutation status is an important factor for surgical decision-making: patients with IDH-mutated tumors are more likely to have a good long-term prognosis, and thus favor aggressive resection with more survival benefit to gain. Patients with IDH wild-type tumors have generally poorer prognosis and, therefore, conservative resection to avoid neurological deficit is favored. Current histopathological analysis with frozen sections is unable to identify IDH mutation status intraoperatively, and more advanced methods are therefore needed. We examined a novel method suitable for intraoperative IDH mutation identification that is based on the differential mobility spectrometry (DMS) analysis of the tumor. We prospectively obtained tumor samples from 22 patients, including 11 IDH-mutated and 11 IDH wild-type tumors. The tumors were cut in 88 smaller specimens that were analyzed with DMS. With a linear discriminant analysis (LDA) algorithm, the DMS was able to classify tumor samples with 86% classification accuracy, 86% sensitivity, and 85% specificity. Our results show that DMS is able to differentiate IDH-mutated and IDH wild-type tumors with good accuracy in a setting suitable for intraoperative use, which makes it a promising novel solution for neurosurgical practice.

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.

Current methods for intraoperative surgical margin assessment are inadequate in terms of diagnostic accuracy, ease-of-use, and speed of analysis. Molecular analysis of tissues could potentially overcome these issues. A system based on differential ion mobility spectrometry (DMS) analysis of surgical smoke has been proposed as one potential method, but to date, it has been able to function in a relatively slow and heavily controlled manner that is inadequate for clinical use. In this study, we present an integrated sensor system that can measure a surgical smoke sample in seconds and relay the information of the tissue type to the user in near real time in simulated surgical use. The system was validated by operating porcine adipose tissue and muscle tissue. The differentiation of these tissues based on their surgical smoke profile with a cross-validated linear discriminant analysis model produced a classification accuracy of 93.1% (N = 1059). The measurements were also classified with a convolutional neural network model, resulting in a classification accuracy of 93.2%. These results indicate that the DMS-based smoke analysis system is capable of rapid tissue identification from surgical smoke produced in freehand surgery.

Common challenge in gas analyzers such as Ion Mobility Spectrometers (IMS) integrated into a measurement system is the reduced analysis speed that is partially limited by the temporal carry-over of sample molecules. It is caused by adsorption and absorption of the molecules into the gas tubes of the analyzer. We studied the recovery times of common tube materials: polyether ether ketone (PEEK), polytetrafluoroethylene (PTFE), fluorinated ethylene propylene (FEP), polyethylene (PE), steel 316 L, parylene C coated steel and Silconert® coated steel from organic combustion products. The tests were performed in two temperatures, at 25 °C and at 70 °C. In addition, detailed analysis was performed for PTFE tube material at 33, 50, 70 and 100 °C to observe the temperature relation of desorption. Uncoated steel was found to have the best performance in increased temperature applications due lack of absorption. Major advantages from coatings compared to plane steel were not found. Plastics were found suitable materials in lower temperatures where adsorption exceeds absorption.

OBJECTIVE: There is a need for real-time, intraoperative tissue identification technology in neurosurgery. Several solutions are under development for that purpose, but their adaptability for standard clinical use has been hindered by high cost and impracticality issues. The authors tested and preliminarily validated a method for brain tumor identification that is based on the analysis of diathermy smoke using differential mobility spectrometry (DMS).METHODS: A DMS connected to a special smoke sampling system was used to discriminate brain tumors and control samples ex vivo in samples from 28 patients who had undergone neurosurgical operations. They included meningiomas (WHO grade I), pilocytic astrocytomas (grade I), other low-grade gliomas (grade II), glioblastomas (grade IV), CNS metastases, and hemorrhagic or traumatically damaged brain tissue as control samples. Original samples were cut into 694 smaller specimens in total.RESULTS: An overall classification accuracy (CA) of 50% (vs 14% by chance) was achieved in 7-class classification. The CA improved significantly (up to 83%) when the samples originally preserved in Tissue-Tek conservation medium were excluded from the analysis. The CA further improved when fewer classes were used. The highest binary classification accuracy, 94%, was obtained in low-grade glioma (grade II) versus control.CONCLUSIONS: The authors’ results show that surgical smoke from various brain tumors has distinct DMS profiles and the DMS analyzer connected to a special sampling system can differentiate between tumorous and nontumorous tissue and also between different tumor types ex vivo.