The poor prognosis of oral cavity squamous cell carcinoma (OCSCC) patients is associated with residual tumor after surgery. Raman spectroscopy has the potential to provide an objective intra-operative evaluation of the surgical margins. Our aim was to understand the discriminatory basis of Raman spectroscopy at a histological level. In total, 127 pseudocolor Raman images were generated from unstained thin tissue sections of 25 samples (11 OCSCC and 14 healthy) of 10 patients. These images were clearly linked to the histopathological evaluation of the same sections after hematoxylin and eosin-staining. In this way, Raman spectra were annotated as OCSCC or as a surrounding healthy tissue structure (i.e., squamous epithelium, connective tissue (CT), adipose tissue, muscle, gland, or nerve). These annotated spectra were used as input for linear discriminant analysis (LDA) models to discriminate between OCSCC spectra and healthy tissue spectra. A database was acquired with 88 spectra of OCSCC and 632 spectra of healthy tissue. The LDA models could distinguish OCSCC spectra from the spectra of adipose tissue, nerve, muscle, gland, CT, and squamous epithelium in 100%, 100%, 97%, 94%, 93%, and 75% of the cases, respectively. More specifically, the structures that were most often confused with OCSCC were dysplastic epithelium, basal layers of epithelium, inflammation-and capillary-rich CT, and connective and glandular tissue close to OCSCC. Our study shows how well Raman spectroscopy enables discrimination between OCSCC and surrounding healthy tissue structures. This knowledge supports the development of robust and reliable classification algorithms for future implementation of Raman spectroscopy in clinical practice.
Human bone marrow stromal-derived mesenchymal stem cells (hBMSCs) will differentiate into chondrocytes in response to defined chondrogenic medium containing transforming growth factor-β (TGFβ). Results in the literature suggest that the three mammalian subtypes of TGFβ (TGFβ1, TGFβ2 and TGFβ3) provoke certain subtype-specific activities. Therefore, the aim of our study was to investigate whether the TGFβ subtypes affect chondrogenic differentiation of in vitro cultured hBMSCs differently. HBMSC pellets were cultured for 5 weeks in chondrogenic media containing either 2.5, 10 or 25 ng/ml of TGFβ1, TGFβ2 or TGFβ3. All TGFβ subtypes showed a comparable dose-response curve, with significantly less cartilage when 2.5 ng/ml was used and no differences between 10 and 25 ng/ml. Four donors with variable chondrogenic capacity were used to evaluate the effect of 10 ng/ml of either TGFβ subtype on cartilage formation. No significant TGFβ subtype-dependent differences were observed in the total amount of collagen or glycosaminoglycans. Cells from a donor with low chondrogenic capacity performed equally badly with all TGFβ subtypes, while a good donor overall performed well. After addition of β-glycerophosphate during the last 2 weeks of culture, the expression of hypertrophy markers was analysed and mineralization was demonstrated by alkaline phosphatase activity and alizarin red staining. No significant TGFβ subtype-dependent differences were observed in expression collagen type X or VEGF secretion. Nevertheless, pellets cultured with TGFβ1 had significantly less mineralization than pellets cultured with TGFβ3. In conclusion, this study suggests that TGFβ subtypes do affect terminal differentiation of in vitro cultured hBMSCs differently.
A Raman tissue spectrum is a quantitative representation of the overall molecular composition of that tissue. Raman spectra are often used as tissue fingerprints without further interpretation of the specific information that they contain about the tissue's molecular composition. In this study, we analyzed the differences in molecular composition between oral cavity squamous cell carcinoma (OCSCC) and healthy tissue structures in tongue, based on their Raman spectra. A total of 1087 histopathologically annotated spectra (142 OCSCC, 202 surface squamous epithelium, 61 muscle, 65 adipose tissue, 581 connective tissue, 26 gland, and 10 nerve) were obtained from Raman maps of 44 tongue samples from 21 patients. A characteristic, average spectrum of each tissue structure was fitted with a set of 55 pure-compound reference spectra, to define the best library of fit-spectra. Reference spectra represented proteins, lipids, nucleic acids, carbohydrates, amino acids and other miscellaneous molecules. A non-negative least-squares algorithm was used for fitting. Individual spectra per histopathological annotation were then fitted with this selected library in order to determine the molecular composition per tissue structure. The spectral contribution per chemical class was calculated. The results show that all characteristic tissue-type spectra could be fitted with a low residual of <4.82%. The content of carbohydrates, proteins and amino acids was the strongest discriminator between OCSCC and healthy tissue. The combination of carbohydrates, proteins and amino acids was used for a classification model of 'tumor' versus 'healthy tissue'. Validation of this model on an independent dataset showed a specificity of 93% at a sensitivity of 100%.
An accurate PCA-hLDA Raman spectroscopy-based tissue classification model for discrimination between OCSCC and (especially the subepithelial) non-tumorous tongue tissue was developed and validated. This model with high sensitivity and specificity may prove to be very helpful to detect tumor in the resection margins.
Background Specimen‐driven intraoperative assessment of the resection margins provides immediate feedback if an additional excision is needed. However, relocation of an inadequate margin in the wound bed has shown to be difficult. The objective of this study is to assess a reliable method for accurate relocation of inadequate tumor resection margins in the wound bed after intraoperative assessment of the specimen. Methods During oral cavity cancer surgery, the surgeon placed numbered tags on both sides of the resection line in a pair‐wise manner. After resection, one tag of each pair remained on the specimen and the other tag in the wound bed. Upon detection of an inadequate margin in the specimen, the tags were used to relocate this margin in the wound bed. Results The method was applied during 80 resections for oral cavity cancer. In 31 resections an inadequate margin was detected, and based on the paired tagging an accurate additional resection was achieved. Conclusion Paired tagging facilitates a reliable relocation of inadequate margins, enabling an accurate additional resection during the initial surgery.
An earlier and more accurate detection of (small) cancerous and precancerous lesions in the oral cavity is essential to improve the prognosis of oral squamous cell carcinomas. Raman spectroscopy is being pursued as a potential method to realize this improvement, since the technique provides objective information on a biochemical level and can be used for real‐time guidance of the diagnostic procedure. Since oral mucosal tissue is inhomogeneous and comprises different layers and histological structures, a good understanding of the signal contributions of the individual layers and structures is required for an accurate interpretation of in vivo tissue spectra measurement volumes. The aim of this study was to create a standardized method to collect and analyse the spectral contributions of individual histopathological structures in oral mucosa. The method is based on Raman microspectroscopic mapping of unstained frozen tissue sections and subsequent histopathological annotation of the features in the resulting Raman images. The obtained annotated reference spectra were used as input in an unsupervised hierarchical cluster analysis in order to determine the spectral characteristics and variance within one histo(patho)logical structure. The described method resulted in an annotated database of Raman spectral characteristics of individual histopathological structures encountered in oral tissue. This database can be used as input for the development of classification and quantification algorithms, in order to achieve a high specificity and sensitivity for clinical diagnostic instruments. Additionally, this database can be used to optimize the exact location and measurement volume of in vivo measurements. Copyright © 2013 John Wiley & Sons, Ltd.
Introduction This study aims to describe the occurrence of postoperative complications related to cholesteatoma surgery and to determine factors influencing the most common complication, i.e. postoperative surgical site infection (SSI) in cases with and without mastoid obliteration. Materials and methods Retrospective analyses were performed on surgically treated cholesteatomas in our hospital between 2013 and 2019. Patient characteristics, peri- and postoperative management and complications were reviewed. The cases were divided into two groups based on whether mastoid obliteration was performed or not. Results A total of 336 cholesteatoma operations were performed, of which 248 cases received mastoid obliteration. In total 21 complications were observed, of which SSI was the most common (15/21). No difference in occurrence of any postoperative complication was seen between the obliteration and no-obliteration group ( p = 0.798), especially not in the number of SSI ( p = 0.520). Perioperative and/or postoperative prophylactic antibiotics were not associated to the development of an SSI in both groups. In the no-obliteration group a younger age (p = 0.015), as well as primary surgery ( p = 0.022) increased the risk for SSI. In the obliteration group the use of bioactive glass (BAG) S53P4 was identified as independent predictor of SSI ( p = 0.008, OR 5.940). Discussion SSI is the most common postoperative complication in cholesteatoma surgery. The causes of SSI are multifactorial, therefore further prospective research is needed to answer which factors can prevent the development of an SSI in cholesteatoma surgery.
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