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.

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue.

BACKGROUND: Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins. OBJECTIVE: To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue. MATERIALS AND METHODS: Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients. RESULTS: A PCA-LDA tissue classification model 'tumor' versus 'non-tumorous tissue' (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model 'tumor' versus 'non-tumorous tissue' showed an accuracy of 91% (sensitivity: 100%, specificity: 78%). CONCLUSION: 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.

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.