Relocation of inadequate resection margins in the wound bed during oral cavity oncological surgery: A feasibility study

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

Investigation of the potential of Raman spectroscopy for oral cancer detection in surgical margins

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 pseudo-color 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

In vivo detection of dysplastic tissue by Raman spectroscopy

The detection of dysplasia and early cancer is important because of the improved survival rates associated with early treatment of cancer. Raman spectroscopy is sensitive to the changes in molecular composition and molecular conformation that occur in tissue during carcinogenesis, and recent developments in fiber-optic probe technology enable its application as an in vivo technique. In this study, the potential of Raman spectroscopy for in vivo classification of normal and dysplastic tissue was investigated. A rat model was used for this purpose, in which dysplasia in the epithelium of the palate was induced by topical application of the carcinogen 4-nitroquinoline 1-oxide. High quality in vivo spectra of normal and dysplastic rat palate tissue, obtained using signal integration times of 100 s were used to create tissue classification models based on multivariate statistical analysis methods. These were tested with an independent set of in vivo spectra, obtained using signal collection times of 10 s, The best performing model, in which signal variance due to signal contributions of the palatal bone was eliminated, was able to distinguish between normal tissue, low-grade dysplasia, and high-grade dysplasia/carcinoma in situ with a selectivity of 0.93 and a sensitivity of 0.78 for detecting low-grade dysplasia and a specificity of 1 and a sensitivity of 1 for detecting high-grade dysplasia/ carcinoma in situ

Autofluorescence and Raman microspectroscopy of tissue sections of oral lesions

Autofluorescence spectroscopy and Raman spectroscopy have been suggested for lesion diagnostics. We investigate the information contained in autofluorescence and Raman spectra recorded from oral tissue slices of various lesion types. Thirty-seven human oral mucosa lesions were biopsied and freeze-dried. Complete autofluorescence images and spectra were recorded from 20 mu m sections. Raman spectra were acquired from the same positions for 12 of the sections. Cluster analysis was applied to find any relationship between spectral shape and lesion type or cell layer. Autofluorescence images showed high intensities for keratin layers and connective tissue, but hardly any for the epithelium. Autofluorescence spectra were centered around 520 nm and did not show specific spectral features. No clustering with regard to lesion type or cell layer was observed. Raman spectra allowed for reliable classification into cell layers, but differences between lesion types were not significant in this study. Autofluorescence spectra of freeze-dried oral mucosa sections did not contain useful information. A more comprehensive study is required for Raman spectra