Identifying brain tumors by differential mobility spectrometry analysis of diathermy smoke

Mass spectrometry for neurosurgery: Intraoperative support in decision-making

Ambient ionization mass spectrometry was proved to be a powerful tool for oncological surgery. Still, it remains a translational technique on the way from laboratory to clinic. Brain surgery is the most sensitive to resection accuracy field since the balance between completeness of resection and minimization of nerve fiber damage determines patient outcome and quality of life. In this review, we summarize efforts made to develop various intraoperative support techniques for oncological neurosurgery and discuss difficulties arising on the way to clinical implementation of mass spectrometry-guided brain surgery.

Knowledge mapping of surgical smoke from 2003 to 2022: a bibliometric analysis

PURPOSE

The purpose of this study is to identify and characterize the literature on surgical smoke, visualize the data and sketch a certain trending outline.

METHODS

In the Web of Science Core Collection (WoSCC), all the data were acquired from January 1st 2003 to December 31st 2022. VOSviewer and CiteSpace were employed to visualize data, based on publications, bibliographic coupling, co-citation, or co-authorship relations. Microsoft Excel 2019 was used to comb and categorize all the statistics.

RESULT

A total 363 of journal papers were retrieved. The publication number was in a slow but steady growth between 2003 and 2019, followed by a sharp surge in 2020, and then the publication kept in a productive way. Surgical endoscopy and other interventional techniques was the most active journal on surgical smoke. USA played an important role among all the countries/regions. There were 1847 authors for these 363 papers, among whom 44 authors published more than three articles on surgical smoke. "Surgical smoke", "covid-19" and "surgery" were the top 3 appeared keywords, while the latest hot-spot keywords were "COVID-19", "virus", "transmission", "exposure" and "risk". There were 1105 co-cited references and 3786 links appeared in all 363 articles. Among them, 38 references are cited more than 10 times. The most co-cited article was "Detecting hepatitis B virus in surgical smoke emitted during laparoscopic surgery." Based on the titles of references and calculated by CiteSpace, the top 3 cluster trend network are "laparoscopic surgery", "COVID-19 pandemic" and "surgical smoke".

CONCLUSION

According to bibliometric analysis, the research on surgical smoke has been drawing attention of more scholars in the world. Increasing number of countries or regions added in this field, and among them, USA, Italy, and China has been playing important roles, however, more wide and intense cooperation is still in expectation.

Critical Assessment of Cancer Characterization and Margin Evaluation Techniques in Brain Malignancies: From Fast Biopsy to Intraoperative Flow Cytometry

Brain malignancies, given their intricate nature and location, present significant challenges in both diagnosis and treatment. This review critically assesses a range of diagnostic and surgical techniques that have emerged as transformative tools in brain malignancy management. Fast biopsy techniques, prioritizing rapid and minimally invasive tissue sampling, have revolutionized initial diagnostic stages. Intraoperative flow cytometry (iFC) offers real-time cellular analysis during surgeries, ensuring optimal tumor resection. The advent of intraoperative MRI (iMRI) has seamlessly integrated imaging into surgical procedures, providing dynamic feedback and preserving critical brain structures. Additionally, 5-aminolevulinic acid (5-ALA) has enhanced surgical precision by inducing fluorescence in tumor cells, aiding in their complete resection. Several other techniques have been developed in recent years, including intraoperative mass spectrometry methodologies. While each technique boasts unique strengths, they also present potential limitations. As technology and research continue to evolve, these methods are set to undergo further refinement. Collaborative global efforts will be pivotal in driving these advancements, promising a future of improved patient outcomes in brain malignancy management.

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.

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.