Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma Raman spectroscopy and a deep learning algorithm.

BACKGROUND

Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer. Many molecular genetic changes are associated with its occurrence. Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.

AIM

To detect alterations in Raman spectral information across different stages of esophageal neoplasia.

METHODS

Different grades of esophageal lesions were collected, and a total of 360 groups of Raman spectrum data were collected. A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma. In addition, a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.

RESULTS

A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm (DNA, symmetric PO, and stretching vibration), 1132 cm (cytochrome c), 1171 cm (acetoacetate), 1216 cm (amide III), and 1315 cm (glycerol). A comparison among the training results of different models revealed that the 1D-transformer network performed best. A 93.30% accuracy value, a 96.65% specificity value, a 93.30% sensitivity value, and a 93.17% F1 score were achieved.

CONCLUSION

Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia. The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.