Utilizing Surgical Smoke to Improve Cancer Surgery

Leikkaus on yleinen ja vakiintunut kiinteiden syöpäkasvaimien hoitomuoto, jossa kasvain pyritään poistamaan siten, että sen ulkoreunaan jää kaistale tervettä kudosta eli poisto tapahtuu ns. tervekudosmarginaalilla. Mikäli marginaalia ei ole tai se ei ole riittävän leveä, kasvain uusiutuu helposti, sillä on erittäin todennäköistä, että elimistöön jää syöpäsoluja, jotka voivat muodostaa uuden kasvaimen. Kuitenkin esimerkiksi aivokasvaimia leikataan pyrkien minimoimaan tervekudoksen poisto, jottei leikkaus aiheuttaisi aivovauriota. Silmämääräisesti tämän rajan tunnistaminen on haastavaa. Verenvuodon vähentämiseksi leikkauksissa käytetään sähköveistä, joka tuottaa savukaasuja. Savukaasut ovat leikkaussalihenkilöstölle haitallisia, ja siksi ne poistetaan savuimulla. Savuimusta voidaan ottaa näytteitä, jotka sisältävät kemiallista informaatiota leikatusta kudoksesta. Niiden avulla on mahdollista optimoida poistettavan kudoksen määrää ja valvoa kasvaimen tervekudosmarginaalia. Menetelmä voi vähentää uusintaleikkauksien tarvetta merkittävästi. Väitöskirjassa tutkitaan savukaasun koostumusta ja sen mahdollisia vaikutuksia leikkausalueen lähellä työskenteleviin ihmisiin sekä kaasun ominaisuuksien käyttöä tervekudosmarginaalin valvonnassa. Tarkemmin tarkastelemme eri kudostyyppien kuormittavuuseroja henkilökunnalle. Tutkimme DMS:n käyttökelpoisuutta aivokasvaimia tunnistettaessa ex vivo. Matala- asteinen gliooma (luokka II) kyettiin tunnistamaan 94 %:n tarkkuudella verrokkiin nähden. Tutkimme eri putkimateriaalien, lämpötilojen ja mittojen vaikutusta laitteiston kemiallisen signaalin siirtonopeuteen mittausjärjestelmässä. Kemiallisen signaalin viiveet vaikuttavat huomattavasti reaaliaikaisen järjestelmän käytettävyyteen. Siksi tutkimmekin kemiallisen signaalin siirtonopeutta ja siirtonopeuden vaikutusta järjestelmän käytettävyyteen.A surgical operation is common practice when treating solid cancer tumours. The tumour is removed with a layer of healthy tissue around it to achieve a so-called negative tissue margin. Without the margin or with an incomplete margin, the cancer will likely recur, since some of the leftover cells can regrow into a new tumour. Still, in some tumours, such as brain tumours, minimizing the removal of healthy tissue takes priority over a negative margin to avoid brain damage. It is often difficult to visually recognize the border of the tumour. To decrease bleeding, these operations are performed with an electric knife. The process produces smoke that is harmful to the operating theatre personnel, and the smoke is thus recommended to be removed with a surgical smoke evacuator. During surgical removal, it is possible to distinguish benign from malignant tissue based on the smoke. This makes it possible to detect when the knife hits the tumour and to alert the surgeon, which enables the surgeon to optimize the amount of removed tissue and ensure a sufficient healthy tissue margin. This process should notably decrease the need for reoperations. In this dissertation, two topics are studied: the structure of surgical smoke as well as the type of risk that operating theatre personnel could be exposed to; and, on the other hand, the possibilities of utilizing the smoke to monitor the tumour resection margin and factors in the analysis equipment affecting its technical performance. More precisely, the studied aspects are the surgical smoke load for the theatre personnel and the classification of tissue types based on surgical smoke. We studied the utilization of differential mobility spectrometry (DMS) to diagnose brain tumours ex vivo. Low-grade glioma (class II) was classified with an accuracy of 94% when compared to control samples. Furthermore, we studied the effect of different tube materials, tube dimensions and temperature on the recovery speed of the measuring system. Delays in the chemical pathway affect the usability of the whole system. Thus, we also studied the chemical signal delays and their effects on system usability

Electrosurgical vessel sealing

Electrosurgical vessel sealing devices have been demonstrated to reduce patient blood loss and operative time during surgery. Whilst the benefits of such devices are widely reported there is still a large variation in the quality of the seal produced, with factors such as vessel size known to effect seal quality. The study aimed to investigate parameters affecting device performance and improve the seal quality. The burst pressure test was used to assess the seal quality and tissue adhesion was measured using a peel test. Additionally histology techniques were used to quantify vessel morphology and found that with an increase in elastin content there was a reduction in seal quality. A number of device modifications were made, testing a selection of non-stick coatings and surface features of the shims. No coating reduced the level of tissue adhesion to the device, but results found that with a greater level of adhesion there was a reduction in seal quality. Considering the different surface features one design, a combination of longitudinal and transverse grooves, resulted in a seal failure rate of 0.0%, a significant improvement in device performance. Two FEM’s were produced to further investigate the device modifications; one in FEBio investigating the mechanical aspects of vessel sealing and the second a multiphysics model to investigate the thermal aspects of vessel sealing. Results from both FEM’s showed a difference in shim performance, with the addition of surface features effecting the stress distribution within the vessel wall and the heat distribution. Additionally DIC was used to capture the vessel sealing process, with results showing each seal was produced in a different way with different levels of tissue contraction. Research conducted demonstrated a number of significant relationships between seal quality and vessel properties, but did not find an explanation for all variation occurring

Mass spectral imaging of clinical samples using deep learning

A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics. Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues. Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required. Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces

Low-temperature heat transfer mediums for cryogenic applications

Copyright © 2023 The Author(s). Background: Researchers and industrialists have grown interested in cryogenic technologies over the years. Cryogenic heat transfer has enabled new applications due to material properties and behaviour at very low temperatures. This domain is still underdeveloped and unfamiliar in various applications. Methods: This work discusses the recent progress on cryogenic mediums and their respective use in different heat transfer applications. After identifying what is commonly designated as a cryogenic medium, i.e., those with a boiling point below -150 °C, the different characteristics and features of such mediums are critically discussed. Significant findings: Liquid He and N2 were found to be the most used cryogenic mediums, mainly due to the very low temperature attained by liquid He, as the closest to the absolute zero, along with the low cost and high availability of liquid N2. The use of liquid-phase cryogenic in a single-phase state was found to be the most common application method. Two-phase applications of the cryogenic medium are mainly for use in a heat pipe, in which both latent and sensible heat is utilized. Cryogenic mediums are essential for critical and niche applications such as in aerospace, superconductivity, advanced machining and manufacturing methods, and more critically in many healthcare applications and advanced scientific research.Air Products PLC under grant agreement: 216-206-P-F