Assessment of Surgical Margin of Tongue Squamous Cell Carcinoma via Raman Mapping.

OBJECTIVES

This study introduces a novel classification approach that combines convolutional neural network (CNN) and Raman mapping to differentiate between tongue squamous cell carcinoma (TSCC) and non-tumorous tissue, as well as to identify different histological grades of TSCC.

MATERIALS AND

METHODS

In this study, 240 Raman mappings data from 30 tissue samples were collected from 15 patients who had undergone surgical resection for TSCC. A total of 18,000 sub-mappings extracted from Raman mappings were then used to train and test a CNN model, which extracted feature representations that were subsequently processed through a fully connected network to perform classification tasks based on the Raman mapping data.

RESULTS

The experimental results indicated that the proposed method achieved competitive classification accuracy above 83%. To further validate the effectiveness of the Raman mapping, its performance was compared with Raman spectroscopy, demonstrating a competitive accuracy rate.

CONCLUSIONS

The promising outcomes from this application of CNN in Raman mapping suggest that this technique could be a reliable method for intraoperative assessment of surgical margins, potentially leading to shorter detection times.