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

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

Syöpäkudoksen luokittelu leikkaussavumittauksista konvoluutioneuroverkon avulla

One of the main reasons for cancer recurrence after operation is deficient surgical margin clearance. The surgical margin assessment could be improved if the whole surgical margin could be analysed intraoperatively. This master's thesis is part of a project that aims to develop an automatic tissue analysis system, where the type of the tissue is identified in real time from surgical smoke produced by electrosurgery and the cancerous tissue can be removed completely during the operation. The chemical composition of the surgical smoke depends on the tissue type, which enables tissue identification based on the surgical smoke. A diathermic blade cuts tissue by burning it with electrical current, and the produced surgical smoke is conducted into a measuring device. The device measures properties of the smoke components with differential mobility spectrometry (DMS) technique, outputting a set of spectra. This matrix form data can also be represented as images, and the variation of the measurement patterns the image based on measured sample is visible for human eye. However, it is not yet possible to interpret the dispersion image without artificial intelligence. In this thesis, a popular image recognition technique called convolutional neural networks (CNN) is used to classify the DMS measurements by tissue type. The CNN classifier trained within the framework of this thesis was able to distinguish breast tumour tissue from healthy breast tissue with 90\% accuracy, exceeding previous results (87\%) on the same data with a linear discriminant analysis classifier. The results demonstrate that convolutional neural networks can be reliably used to classify tissue samples from surgical smoke without any preprocessing of the DMS data, and training the classifier is possible even with a small dataset of only a few hundred measurements. Even though further research is required due to limited amount of data in this thesis, convolutional neural networks are a promising new method for DMS data analytics. Real-time surgical margin assessment with the automatic tissue analysis system utilizing CNNs could save cancer patients from unnecessary re-operations in the future.Yksi suurimmista syistä syövän uusiutumiseen leikkauksen jälkeen on puutteellinen tervekudosmarginaalin määritys. Kudosmarginaalin määritystä voitaisiin parantaa, jos koko marginaalialuetta voitaisiin tutkia suoraan leikkauksen aikana. Tämä diplomityö on osa projektia, jonka tavoitteena on kehittää automaattinen kudoksentunnistusjärjestelmä, jossa leikattavan kudoksen tyyppi tunnistetaan reaaliaikaisesti sähkökirurgisesti syntyneestä leikkaussavusta, jotta syöpäkudos voitaisiin poistaa kokonaisuudessaan tarkemmin leikkauksessa. Leikkaussavun kemiallinen koostumus riippuu kudostyypistä, mikä mahdollistaa kudoksen tunnistamisen savun perusteella. Diatermiaveitsi leikkaa kudosta polttamalla sitä sähkövirran avulla, ja polttamisen yhteydessä haihtuva savu johdetaan mittalaitteeseen. Mittalaite tuottaa savusta dispersiokuvan differentiaali-ioniliikkuvuusspektrometrian (DMS) avulla, ja dispersiokuvassa havaittava kuvio vaihtelee silminnähden savutyypistä riippuen. Dispersiokuvia on kuitenkin toistaiseksi mahdotonta tulkita ilman tekoälyä, joten luokittelussa voidaan hyödyntää esimerkiksi kuvantunnistuksessa laajalti käytettyä koneoppimismenetelmää, konvoluutioneuroverkkoja. Tämän diplomityön puitteissa opetettu konvoluutioneuroverkko kykeni erottamaan rintasyöpäkudoksen terveestä rintakudoksesta 90% tarkkuudella, mikä ylittää aiempien tutkimusten lineaarisella erotteluanalyysilla (LDA) tuotetun luokittelutarkkuuden (87%). Tulokset osoittavat, että konvoluutioneuroverkkoja voidaan käyttää kudosnäytteiden DMS-mittausten automaattiseen luokitteluun ilman aineiston esikäsittelyä. Kudostunnistusneuroverkkojen opetus onnistuu luotettavasti jo pienen, vain muutamia satoja mittauksia sisältävän aineiston perusteella. Aineiston rajallisuuden vuoksi lisätutkimusta tarvitaan löydösten vahvistamiseksi, mutta konvoluutioneuroverkkoja voi joka tapauksessa pitää lupaavana uutena menetelmänä DMS-mittausten analysointiin. Reaaliaikainen kudosmarginaalin arviointi konvoluutioneuroverkkoja hyödyntävällä automaattisella kudostunnistusjärjestelmällä voisi tulevaisuudessa säästää syöpäpotilaita tarpeettomilta uusintaleikkauksilta

Real Time Tissue Identification from Diathermy Smoke by Differential Mobility Spectrometry

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.acceptedVersionPeer reviewe

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.publishedVersionPeer reviewe

Observational study on the evolution of systemic treatments for advanced renal cell carcinoma in Southwest Finland between 2010 and 2021

Background: Novel receptor tyrosine kinase inhibitors and immune checkpoint inhibitors have been introduced to the treatment of advanced renal cell carcinoma (aRCC) during the past decade. However, the adoption of novel treatments into clinical practice has been unknown in Finland. Objectives: Our aim was to evaluate the use of systemic treatments and treatment outcomes of aRCC patients in Southwest Finland during 2010–2021. Design and Methods: Clinical characteristics, treatments for aRCC, healthcare resource utilization, and overall survival (OS) were retrospectively obtained from electronic medical records. Patients were stratified using the International Metastatic RCC Database Consortium (IMDC) risk classification. Results: In total, 1112 RCC patients were identified, 336 (30%) patients presented with aRCC, and 57% of them ( n  = 191) had received systemic treatment. Pre-2018, sunitinib (79%) was the most common first-line treatment, and pazopanib (17%), axitinib (17%), and cabozantinib (5%) were frequently used in the second-line. Post-2018, sunitinib (52%), cabozantinib (31%), and the combination of ipilimumab and nivolumab (10%) were most commonly used in the first-line, and cabozantinib (23%) in the second-line. Median OS for patients with favorable, intermediate, and poor risk were 61.9, 28.6, and 8.1 months, respectively. A total of 73%, 74%, and 35% of the patients with favorable, intermediate, and poor risk had received second-line systemic treatment. In poor-risk patients, the number of hospital inpatient days was twofold higher compared to intermediate and fourfold higher compared to favorable-risk patients. Conclusion: New treatment options were readily adopted into routine clinical practice after becoming reimbursed in Finland. OS and the need for hospitalization depended significantly on the IMDC risk category. Upfront combination treatments are warranted for poor-risk patients as the proportion of patients receiving second-line treatment is low. Registration: Clinical trial identifier: ClinicalTrials.gov NCT05363072

Laser desorption tissue imaging with Differential Mobility Spectrometry

Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and enable focused sampling. Differential Mobility Spectrometry (DMS) is a rapid and affordable technology for complex gas mixture analysis. We present an automated tissue laser analysis system for imaging approaches (iATLAS), which utilizes a computer-controlled laser evaporator unit coupled with a DMS gas analyzer. The system is demonstrated in the classification of porcine tissue samples and three human breast carcinomas. Tissue samples from eighteen landrace pigs were classified with the system based on a pre-designed matrix (spatial resolution 1–3 mm). The smoke samples were analyzed with DMS, and tissue classification was performed with several machine learning approaches. Porcine skeletal muscle (n = 1030), adipose tissue (n = 1329), normal breast tissue (n = 258), bone (n = 680), and liver (n = 264) were identified with 86% cross-validation (CV) accuracy with a convolutional neural network (CNN) model. Further, a panel tissue that comprised all five tissue types was applied as an independent validation dataset. In this test, 82% classification accuracy with CNN was achieved. An analogous procedure was applied to demonstrate the feasibility of iATLAS in breast cancer imaging according to 1) macroscopically and 2) microscopically annotated data with 10-fold CV and SVM (radial kernel). We reached a classification accuracy of 94%, specificity of 94%, and sensitivity of 93% with the macroscopically annotated data from three breast cancer specimens. The microscopic annotation was applicable to two specimens. For the first specimen, the classification accuracy was 84% (specificity 88% and sensitivity 77%). For the second, the classification accuracy was 72% (specificity 88% and sensitivity 24%). This study presents a promising method for automated tissue imaging in an animal model and lays foundation for breast cancer imaging.publishedVersionPeer reviewe