Re: Vapours
Towanda Tuning
In archaic usage, the vapours (or vapors) is a mental, psychological, or physical state,[1] such as hysteria, mania, clinical depression, bipolar disorder, lightheadedness, fainting, flush, withdrawal syndrome, mood swings, or PMS in which a sufferer loses mental focus. Ascribed primarily to women and thought to be caused by internal emanations (vapours) from the womb, it was related to the concept of female hysteria. The word "vapours" was subsequently used to describe a depressed or hysterical nervous condition.[2]
Over 4000 years of history, this condition was considered from two perspectives: scientific and demonological. It was treated with herbs, sex or sexual abstinence, punished and purified with fire for its association with sorcery and finally, clinically studied as a disease and treated with innovative therapies. However, even at the end of 19th century, scientific innovation had still not reached some places, where the only known therapies were those proposed by Galen. The evolution of these diseases seems to be a factor linked with social "westernization", and examining under what conditions the symptoms first became common in different societies became a priority for recent studies over risk factor.[3]
Before the Victorian era, a variety of conditions which affected women were referred to as "a case of the vapours". A Treatise of Vapours or Hysterick Fits,[4] by John Purcell, published in 1707, describes the various conditions described as "vapours", with treatments.
Electrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.
The REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.
Breast cancer is the commonest cancer in women, with the fifth highest mortality rates worldwide [1]. Breast conserving surgery (BCS) is the most commonly performed surgical technique for the treatment of women with early stage breast cancer in both the United States of America (USA) and the United Kingdom (UK) [2, 3]. On both sides of the Atlantic, over a fifth of patients undergoing BCS require re-operation for inadequate margins [3,4,5,6,7]. Meta-analytical data suggest that a positive resection margin following BCS more than doubles the chance of ipsilateral breast tumour regional recurrence (IBTR) [8]. The risk of relapse is not eliminated by the use of radiotherapy, systemic chemotherapy or endocrine therapy [9].
Re-operation is associated with physical and psychological morbidity and has economic implications. Re-operative intervention is associated with greater patient anxiety, impaired cosmesis [10] and a higher incidence of post-operative wound complications [11], and may delay receipt of adjuvant therapies [12]. Moreover, re-operation increases healthcare costs. For example, an economic model of re-excision of breast margins in the USA predicted that in comparison to positive margins, re-excision of close margins (
Intraoperative margin assessment (IMA) techniques aim to provide the surgeon with actionable information about margin status in the midst of the index procedure to reduce the need for re-operation. Pathological techniques, frozen section and cytology (i.e. imprint, touch and scrape) are demonstrably accurate [14] and may reduce re-operation rates for positive margins [15, 16]. For example, frozen section has been demonstrated to significantly reduce reoperation rates from 13.2% to 3.6% [17] and its uptake may be cost-effective [18]. However, the limitations of pathological techniques, including slow turnaround times, manpower requirements, and the potential for false positive interpretation, have limited international adoption. Specimen radiology (SR) and intraoperative ultrasound (IOUS) can be used to assess margin status and can be performed within the operating theatre providing direct feedback to the surgeon without the need for specially trained personnel [19, 20]. However, compared to pathological techniques they have inferior accuracy [14], and hence do not parallel the observed reductions in re-operation rates [21].
Mass spectrometry (MS) is an innovative addition to the field of margin detection technologies. Molecules are analysed by measuring the mass-to-charge ratio (m/z) of molecular ions and their charged fragments. MS is well-established as a tool for quantifying small molecules and is also valuable for identifying metabolites and biomarkers [35, 36]. Over the last decade, advances in MS instrumentation have resulted in the ability to detect proteins and metabolites directly from tissues via imaging applications. A variety of MS platforms including matrix-assisted laser desorption/ionisation (MALDI) [37] and desorption electrospray ionisation (DESI) [38] show promise in differentiating tissue types with potential applications in rapid tissue diagnostics [39].
There is clearly a need to develop a reliable, effective, and rapid IMA method for neoplastic tissue characterization with accuracy competitive with standard histological assessment that can guide resection in vivo and that improves quality in breast surgical oncology. The REIMS system or intelligent knife (iKnife), capable of providing intuitive feedback on real-time tissue characterisation at the point of dissection, offers a potential solution. Here, we test the hypothesis that malignant breast tissues exhibit different metabolic profiles compared to normal breast tissues, and that these changes can be exploited using REIMS. Finally, we demonstrate proof of concept that the iKnife method is capable of intra-operative analysis of electrosurgical vapours.
A single-centre, prospective observational study was performed at Imperial College Healthcare NHS Trust (London, UK). Ethical approval was gained from South East London Research Ethics Committee Reference 11/LO/0686, the East of England - Cambridge East Research Ethics Committee Reference 14/EE/0024 and the project was registered under the Imperial College Tissue Bank. Patients (>18 years of age) undergoing breast surgery for benign and malignant disease were recruited. Data were only obtained on patients who had consented to utilization of tissue for research.
Ex-vivo and intraoperative workflows. a Ex-vivo workflow from generation of spectra by mass spectrometry (MS) analysis of surgical aerosol through to model building by multivariate statistics leading to ex-vivo recognition of tissue in real time. b Intraoperative workflow from generation of spectra in real time by on-line MS analysis, through to determination of margin status by histopathological assessment and correlation to iKnife results
To determine if the ex-vivo method was applicable to the intraoperative environment we ran an intraoperative proof-of-principle study. A modified Xevo G2-XS mass spectrometer (Waters, UK) was installed in the operating theatre and a commercially available sterile (Surg-N-Vac or AccuVac, Covidien, UK) hand-piece was connected to the instrument. Aerosol produced as a result of electrosurgical tissue manipulation was continuously aspirated into the instrument throughout the operation. Video footage was simultaneously recorded, capturing all activities occurring at the operative scene (GoPro, CA, USA) in a time-synchronized manner with the acquisition of spectral data. Video recordings enabled retrospective orientation of spectral data with regard to three-dimensional margins. However, presently sample size is inadequate for interpretation of diagnostic accuracy. An optimised iKnife intraoperative workflow is displayed in Fig. 1b.
Ex-vivo validation of recognition software with new samples. a Flow diagram of samples used in the ex-vivo validation experiment. N normal tissue, T tumour tissue. b Confusion matrix demonstrating diagnostic accuracy of the combined electrosurgical model with a validation set of new fresh and frozen tissues. On analysis of diagnostic accuracy, sensitivity was 90.9% and specificity 98.8%
Ex-vivo validation case study. An electrosurgical hand-piece was moved through the mastectomy specimen in coag mode from normal breast tissue, into tumour and out through normal tissue. A simultaneous video recording reveals the position of the hand-piece in relation to the specimen and the generated spectra and demonstrates good correlation with the recognition software compared to macroscopic findings
Collection of intraoperative mass spectral data with comparison to ex-vivo spectra. Spectral intensity over time obtained throughout entire surgery (14 minutes), one spectra obtained per second. Intraoperative spectral differences highlighted in cut (right) and coag (left) modalities observed in normal tissue and compared to similar spectra observed in two ex-vivo examples
A REIMS-based histological identification method has been successfully optimised for the real-time analysis of heterogeneous breast tissues. The construction of an ex-vivo database from fresh breast tissues demonstrated significant differences in spectra related to disparity in lipid metabolism between normal breast tissues and breast cancer. Spectral differences observed between cut and coag electrosurgical modalities have been combined to create a multivariate statistical model allowing the use of both modes interchangeably. Leave-one-patient-out cross-validation demonstrates this model can detect tumour with a sensitivity of 94.9% and exclude tumour with a specificity of 93.4%, results that rival other IMA techniques [34].
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