Tissue Identification by Differential Mobility Spectrometry

Perimämme muuttuu jatkuvasti luonnollisten mutaatioiden sekä ulkoisten tekijöiden vaikutuksesta. Muutosten kumuloituessa elämämme aikana hallitsemattomasti jakaantuvien ja ympäröiviin kudoksiin sekä lymfaattiseen järjestelmään ja verenkiertoon tunkeutuvien solujen syntymisen todennäköisyys kasvaa. Tämän tyyppistä pahanlaatuista solukasvua kutsutaan syöväksi. Syöpä vaikuttaa joko suoraan tai epäsuorasti suurimpaan osaan ihmisistä yhtenä yleisimmistä kuolinsyistä. Syöpä on monimuotoinen tauti, joka voi syntyä käytännössä mihin kehon osaan tahansa. Riippuen syövän kohdekudoksesta ja kasvun aggressiivisuudesta mahdolliset hoitomuodot, selviytymisennusteet ja kuolleisuus vaihtelevat huomattavasti. Yleisesti syövän rooli kuolinsyynä kuitenkin korostuu jatkuvasti, ja huomattavasta rahallisesta panostuksesta ja vuosikymmenten tutkimustyöstä huolimatta uusille ja paremmille hoito- ja diagnosointimenetelmille on jatkuva tarve. Kiinteiden syöpien leikkaushoito on yksi erityisalue, joka hyötyisi uusista, hoitoa tehostavista innovaatioista. Syövän leikkaushoidossa on yleisesti tavoitteena poistaa kasvain elimistöstä täydellisesti ja täten saavuttaa negatiivinen tervekudosmarginaali. Huomattavassa osassa syöpäleikkauksia poisto on kuitenkin epätäydellinen. Tällöin potilaaseen jääneet syöpäsolut vaativat jatkohoitotoimenpiteitä, joihin yleensä sisältyy myös syövän uusintaleikkaus. Uusintaleikkauksen tarve on erittäin vahingollista potilaan yleiselle hyvinvoinnille ja tuo mukanaan huomattavia lisäterveydenhuoltokustannuksia. Jos vältettävissä olevien uusintaleikkausten määrä voitaisiin puolittaa nykyisestä, säästöjä mitattaisiin jo miljardeissa. Selkeästä säästöpotentiaalista huolimatta syöpien turhat uusintaleikkaukset ovat edelleen ratkaisematon ongelma johtuen etenkin leikkauksenaikaisista haasteista erottaa hyvänlaatuinen kudos pahanlaatuisesta. Solujen rakenteen ja toiminnan määräävä molekulaarinen sisältö eroaa riippuen solujen syntykudoksesta, ja samankaltaisia eroja havaitaan myös pahanlaatuisten ja hyvänlaatuisten solujen välillä. Biomolekyylejä, jotka mahdollistavat kudostyyppien erojen havaitsemisen, kutsutaan biomarkkereiksi tai bioilmaisimiksi, ja tutkimuksissa onkin löydetty satoja proteiineja, rasva-aineita ja aineenvaihduntatuotteita, joiden pitoisuus solussa vaihtelee hyvänlaatuisen ja pahanlaatuisen kudoksen välillä. Tiettyjen biomarkkereiden pitoisuuksien vaihteluvälit hyvänlaatuisissa ja pahanlaatuisissa kudoksissa ovat kuitenkin erittäin suuria, ja molekyylitason erot kudosten välillä aiheuttavat harvoin selkeää makroskooppisesti näkyvää muutosta. Siksi syöpäkudoksen ja tervekudoksen välisen rajan silmämääräinen arvioiminen on erittäin haastavaa. Silti lähes kaikki syöpäkirurgit käyttävät ainoastaan visuaalista arviointia ja tunnustelua operaatiotilanteessa. Lisää haasteellisuutta syövän kokonaispoistoon tuovat myös nykysuositukset, joiden mukaan syövän ympäriltä poistetun tervekudoksen määrä pyritään minimoimaan. Tämä tavoite ja rajan subjektiivinen arviointi johtavat suureen hajontaan eri maiden ja sairaaloiden positiivisten marginaalien määrissä sekä yleisesti korkeaan uusintaleikkausten määrään. Positiivisista tervekudosmarginaalilöydöksistä johtuvia uusintaleikkauksia on pyritty vähentämään tutkimalla ja ottamalla käyttöön useita erilaisia leikkauksenaikaista kudostunnistusta auttavia menetelmiä, mutta niiden kliininen käyttö on ollut rajallista johtuen kunkin menetelmän rajoitteista ja haitoista. Tässä väitöskirjassa esitellään kudostunnistusjärjestelmä, jota voidaan mahdollisesti tulevaisuudessa hyödyntää leikkauksenaikaisessa tervekudosmarginaalin arvioinnissa. Järjestelmän kehitystä ja soveltuvuutta kudostunnistukseen tarkastellaan viiden osatyön kautta. Järjestelmä pohjautuu sähkökirurgiassa tuotetun kudossavun mittaamiseen liikkuvuuserospektrometrialla (differential mobility spectrometry, DMS). DMS on normaali-ilmanpaineessa toimiva mittausteknologia, joka tuottaa informaatiota kaasumaisen näytteen molekulaarisesta rakenteesta erottamalla ionisoidut molekyylit toisistaan voimakkaassa, epäsymmetrisesti muuttuvassa sähkökentässä. DMS vertautuu massaspektrometriaan (MS) mutta on analyyttiseltä suorituskyvyltään sitä heikompi. DMS-teknologian etuna on kuitenkin sen yksinkertaisuus, pienempi koko sekä pienemmät kustannukset MS-teknologiaan verrattuna. DMS-teknologiaa on aiemmin käytetty itsenäisenä mittausmenetelmänä erilaisissa kaasumittaussovelluksissa sekä biolääketieteellisessä käytössä muun muassa hengitysilman mittaamiseen. Nämä sovellukset ovat kuitenkin aina sallineet kontrolloidun ympäristön ja suhteellisen pitkän mittauksen keston. Siksi reaaliaikainen DMS-pohjainen sovellus vaatii ympärilleen lisälaitteistoa ja järjestelmän parametrien optimointia. Lisäksi DMS-data ei suoraan tuota määrällistä tietoa näytteessä olevista biomolekyyleista vaan luo pikemminkin kokonaiskuvan näytteen sisältämien aineiden seoksesta. Spektrin tulkinta ja kudostyypin määritys ei siis ole suoraviivaista, ja yhdestä näytteestä saatavan suuren datamäärän vuoksi analysointi soveltuu parhaiten koneoppimismenetelmille. Järjestelmän poikkitieteellinen näkökulma sekä kokonaisuuden toiminnan ja suorituskyvyn tutkiminen kudostunnistuksessa ovat tämän väitöskirjan pääsisältö. Väitöskirjan kolmessa ensimmäisessä osatyössä tavoitteena oli tutkia menetelmän soveltuvuutta kudostunnistukseen eläinkudosnäytteillä sekä ihmisen rintasyöpänäytteillä. Tulokset laboratorio-olosuhteissa hallitulla näytteentuotolla olivat lupaavia, ja diagnostinen suorituskyky osoitti teknologian potentiaalin kudostunnistuksessa. Neljännessä osatyössä laitteistoa muokattiin mahdollistamaan reaaliaikaiset mittaukset sekä luokittelutuloksen esitys välittömästi mittauksen jälkeen. Tulokset osoittivat, että järjestelmä soveltuu reaaliaikaiseen kudostunnistukseen vähintään eläinnäytteillä laboratorio-olosuhteissa. Viidennessä osatyössä järjestelmää käytettiin rintasyöpäleikkauksissa. Diagnostisen suorituskyvyn osalta tulokset eivät olleet vertailukelpoisia laboratoriotutkimuksiin, mutta tutkimus osoitti, että järjestelmän integroiminen osaksi syöpäkirurgiaa onnistuu käyttäjiä häiritsemättä ja että se pystyy tuottamaan informaatiota leikatusta kudoksesta operaation aikana. Kokonaisuudessaan väitöskirjatutkimuksen tulokset osoittavat DMS-pohjaisen kudostunnistusjärjestelmän potentiaalin ja soveltuvuuden reaaliaikaiseen käyttöön riittävällä diagnostisella suorituskyvyllä. Tulevaisuudessa tässä työssä esitetty järjestelmä voi jatkokehityksen jälkeen toimia syöpäkirurgin apuna tervekudosmarginaalin tunnistuksessa ja auttaa suojelemaan syöpäpotilaiden hyvinvointia vähentämällä tarpeettomia syövän uusintaleikkauksia.The human genome is constantly changing due to natural mutations and environmental exposure. As these changes accumulate over our lifetime, it increases the likelihood of the creation of cells that proliferate uncontrollably and ultimately invade surrounding tissue and the blood circulation or the lymphatic system. This type of malignant neoplasm, more commonly known as cancer, is a disease that either directly or indirectly affects the majority of the population as one of the leading causes of death. Cancer is a versatile disease that can affect practically any part of the body. Depending on the tissue of origin and the aggressiveness of the malignancy, the treatment options, prognosis and mortality rates can vary significantly. In general, the role of cancer as a cause of death is constantly increasing, and despite significant global financial investments and decades of research, new and better methods of treatment and diagnosis are in continuous demand. One particular area that requires more attention and innovation is the surgical treatment of solid cancers. The general aim of surgical treatment is to remove all malignant cells from the patient’s body – that is to say, to achieve a negative surgical margin. The resected tumour has a negative margin, when the outermost surface area has no cancerous cells. However, in a considerable number of surgeries, the removal is incomplete. The resulting residual cancer almost always triggers additional treatment steps, which often involve a reoperation. The need for a reoperation is a major detriment for the well-being of the patient, and the added healthcare costs are substantial. If the number of avoidable reoperations could be halved from their current level, the saving potential in annual global healthcare costs would already be measured in billions of dollars. The reason why the problem of reoperations persists despite the notable financial incentives lies in the difficulty of discriminating malignant tissue from benign, especially during a surgical procedure. The molecular contents that define the structure and function of a cell are different depending on the organ of origin, and similar differences are also present between malignant and benign cells. The biomolecules that enable the identification of the types of tissues are called biomarkers, and the research on this area has revealed hundreds of proteins, fatty acids and metabolic products that exhibit differences in quantities based on tissue malignancy. However, the variation of specific marker molecules is often high, and the molecular differences rarely translate into clear macroscopic differences. This means that visual assessment of the margin between benign and cancerous tissue is extremely challenging. Still, almost all surgeons rely only on visual assessment and palpation in cancer surgeries. The challenge of complete excision is further accentuated by the current resection guidelines that instruct surgeons to preserve as much non-cancerous tissue as possible. This aim and its subjective execution lead not only to high variation in positive margin rates between institutions and regions, but also to a high number of required reoperations in general. To reduce the reoperations caused by positive surgical margins, several technologies have been studied and introduced to aid in intraoperative tissue identification, but the clinical adoption has been limited due to various impeding factors involved in their use. In this thesis, a concept that could potentially be used in the assessment of the intraoperative surgical margin is introduced through five scientific publications that concentrate on the evolution and feasibility of the technology in tissue identification. The basis of the technology is the measurement of surgical smoke with differential mobility spectrometry (DMS). DMS is a measurement technology that provides information on the molecular content of a gaseous sample in atmospheric pressure by means of ionisation and subsequent differentiation of the ions in a high-strength asymmetric electric field. DMS is comparable to mass spectrometry (MS), and even though the analytical performance of MS is better, the reduced complexity, smaller size and lower cost of DMS make it an advantageous option. DMS has been used as a standalone measurement instrument in many types of general gas measurement applications and in some biomedical applications, such as breath analysis, but the context of use has always permitted a controlled environment and a relatively long measurement duration. Thus, the real-time application of surgical smoke measurement requires additional hardware and parameter optimisation. In addition, raw DMS measurement data do not provide directly quantifiable information on certain biomolecules, but rather a comprehensive spectrum of all contents in the sample combined. This means that the interpretation and identification of tissue type from the DMS output spectra is not trivial and involves a high number of dimensions that are most effectively analysed by means of machine learning. The interdisciplinary aspects of the system and their combined function and performance in tissue identification are the focus of this thesis. In the first three publications included in the thesis, the focus was on studying the overall feasibility of tissue identification and its possibilities with animal tissues and clinically relevant breast cancer samples. The results in laboratory conditions with controlled sampling were promising, and the diagnostic performance demonstrated the potential of the technology in tissue identification. In Publication IV, the system was modified to accommodate real-time measurements and to relay the classification information immediately after the measurement. The results demonstrated the feasibility of real-time tissue identification with the system, albeit in laboratory conditions and in a porcine model. In the final study, a prototype system was used intraoperatively during breast cancer surgeries. The results of this study were not comparable to the laboratory results in respect to diagnostic performance but indicated that the system can be adapted to the surgical workflow with minimal intrusiveness to provide information on the operated tissue. Overall, the results of this study indicate that a DMS-based tissue identification system has the potential to be used in real-time applications to identify tissue types with adequate diagnostic performance. With further development, the system presented in this thesis could fulfil the need for a surgical margin assessment device that would reduce avoidable reoperations of solid cancers and thus protect the well-being of cancer patients

Diatermiasavun analysointiin perustuva leikkauksenaikainen tervekudosmarginaalin tunnistava mittausjärjestelmä

In this thesis, a method and system capable of intraoperative cancer margin detection, with potential to improve the current methodology, is introduced, tested and validated. The system is based on diathermy smoke analysis by differential ion mobility spectrometry (DMS). Three large measurement sets with different objectives were executed. The first measurement set concentrated on validating the function of a novel filtration device, which is an essential part of the full system proposed in this thesis, and was patented during the thesis work. In the second measurement set, a proof-of-concept study with porcine tissues was conducted to elucidate, whether healthy tissue identification with the system is possible. The third measurement set was a pilot test with two types of clinical human brain tumor samples, with the aim to achieve actual reliable cancer identification with the proposed system. Regarding the objectives, all the three measurement sets were successful. Based on the results, we state that the patented filtration solution works with a high efficiency without compromising tissue identification, and that cancer detection based on the ion mobility spectrometer analysis of tissue smoke can be achieved with our system

Method for the Intraoperative Detection of IDH Mutation in Gliomas with Differential Mobility Spectrometry

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.Peer reviewe

The Detection of Bacteria in the Maxillary Sinus Secretion of Patients With Acute Rhinosinusitis Using an Electronic Nose : A Pilot Study

Objective: Detecting bacteria as a causative pathogen of acute rhinosinusitis (ARS) is a challenging task. Electronic nose technology is a novel method for detecting volatile organic compounds (VOCs) that has also been studied in association with the detection of several diseases. The aim of this pilot study was to analyze maxillary sinus secretion with differential mobility spectrometry (DMS) and to determine whether the secretion demonstrates a different VOC profile when bacteria are present. Methods: Adult patients with ARS symptoms were examined. Maxillary sinus contents were aspirated for bacterial culture and DMS analysis. k-Nearest neighbor and linear discriminant analysis were used to classify samples as positive or negative, using bacterial cultures as a reference. Results: A total of 26 samples from 15 patients were obtained. After leave-one-out cross-validation, k-nearest neighbor produced accuracy of 85%, sensitivity of 67%, specificity of 94%, positive predictive value of 86%, and negative predictive value of 84%. Conclusions: The results of this pilot study suggest that bacterial positive and bacterial negative sinus secretion release different VOCs and that DMS has the potential to detect them. However, as the results are based on limited data, further conclusions cannot be made. DMS is a novel method in disease diagnostics and future studies should examine whether the method can detect bacterial ARS by analyzing exhaled air.publishedVersionPeer reviewe

Detection of cultured breast cancer cells from human tumor-derived matrix by differential ion mobility spectrometry

Publisher Copyright: © 2022 The AuthorsThe primary treatment of breast cancer is the surgical removal of the tumor with an adequate healthy tissue margin. An intraoperative method for assessing surgical margins could optimize tumor resection. Differential ion mobility spectrometry (DMS) is applicable for tissue analysis and allows for the differentiation of malignant and benign tissues. However, the number of cancer cells necessary for detection remains unknown. We studied the detection threshold of DMS for cancer cell identification with a widely characterized breast cancer cell line (BT-474) dispersed in a human myoma-based tumor microenvironment mimicking matrix (Myogel). Predetermined, small numbers of cultured BT-474 cells were dispersed into Myogel. Pure Myogel was used as a zero sample. All samples were assessed with a DMS-based custom-built device described as “the automated tissue laser analysis system” (ATLAS). We used machine learning to determine the detection threshold for cancer cell densities by training binary classifiers to distinguish the reference level (zero sample) from single predetermined cancer cell density levels. Each classifier (sLDA, linear SVM, radial SVM, and CNN) was able to detect cell density of 3700 cells μL−1 and above. These results suggest that DMS combined with laser desorption can detect low densities of breast cancer cells, at levels clinically relevant for margin detection, from Myogel samples in vitro.Peer reviewe

Differentiation of aspirated nasal air from room air using analysis with a differential mobility spectrometry-based electronic nose : a proof-of-concept study

Over the last few decades, breath analysis using electronic nose (eNose) technology has become a topic of intense research, as it is both non-invasive and painless, and is suitable for point-of-care use. To date, however, only a few studies have examined nasal air. As the air in the oral cavity and the lungs differs from the air in the nasal cavity, it is unknown whether aspirated nasal air could be exploited with eNose technology. Compared to traditional eNoses, differential mobility spectrometry uses an alternating electrical field to discriminate the different molecules of gas mixtures, providing analogous information. This study reports the collection of nasal air by aspiration and the subsequent analysis of the collected air using a differential mobility spectrometer. We collected nasal air from ten volunteers into breath collecting bags and compared them to bags of room air and the air aspirated through the device. Distance and dissimilarity metrics between the sample types were calculated and statistical significance evaluated with Kolmogorov-Smirnov test. After leave-one-day-out cross-validation, a shrinkage linear discriminant classifier was able to correctly classify 100% of the samples. The nasal air differed (p < 0.05) from the other sample types. The results show the feasibility of collecting nasal air by aspiration and subsequent analysis using differential mobility spectrometry, and thus increases the potential of the method to be used in disease detection studies.acceptedVersionPeer reviewe

Characterization of signal kinetics in real time surgical tissue classification system

Effective surgical margin assessment is paramount for good oncological outcomes and new methods are in active development. One emerging approach is the analysis of the chemical composition of surgical smoke from tissues. Surgical smoke is typically removed with a smoke evacuator to protect the operating room staff from its harmful effects to the respiratory system. Thus, analysis of the evacuated smoke without disturbing the operation is a feasible approach. Smoke transportation is subject to lags that affect system usability. We analyzed the smoke transportation delay and evaluated its effects to tissue classification with differential mobility spectrometry in a simulated setting using porcine tissues. With a typical smoke evacuator setting, the front of the surgical plume reaches the analysis system in 380 ms and the sensor within one second. For a typical surgical incision (duration 1.5 s), the measured signal reaches its maximum in 2.3 s and declines to under 10% of the maximum in 8.6 s from the start of the incision. Two-class tissue classification was tested with 2, 3, 5, and 11 s repetition rates resulting in no significant differences in classification accuracy, implicating that signal retention from previous samples is mitigated by the classification algorithm.publishedVersionPeer reviewe

Effects of sensor type and sensor location on signal quality in bed mounted ballistocardiographic heart rate and respiration monitoring

Sleeping is a crucial part of our circadian rhythm and the quality of sleep has substantial impact on the quality of life in general and the overall well-being of a person. That is why sleep related physiological measurements have been in the focus of many scientific studies along the years, and why a large number of different measurement methods have been developed for this purpose. The ability to monitor heart rate respiration without any sensors or electrodes being directly attached to the body is extremely useful especially in long-term monitoring and it allows automated daily measurements without any medical staff present. This is the reason why ballistocardiographic force sensors and accelerometers have been introduced alongside electrocardiography (ECG) and thermistors or respiration belts as a means to monitor the heart rate and respiration during sleep. While ECG remains as the most reliable and accurate method for heart rate monitoring, the development of unobtrusive monitoring methods has improved to the point where the commercialization of such sleep monitoring systems has been possible. In this paper, the signals of five sensors and sensor placement combinations for measuring physiological parameters from a sleeping person are evaluated and compared in terms of their measurement sensitivities and waveform quality. The sensors are accelerometer and film type force sensors made of PVDF and EMFi material placed under the mattress topper and PVDF and EMFi sensors placed under the bed posts.acceptedVersionPeer reviewe