The present application relates to the technical field of squamous
cell carcinoma early diagnosis, and provides a method for assisting early diagnosis of
head and neck squamous
cell carcinoma, comprising: S1: collecting existing video data obtained by using MET targeted near-
infrared fluorescence imaging technology, and training a
supervised learning model by using the video
data set; S2: building a classifier
system, inputting video data to be classified into the classifier
system, and applying the trained
supervised learning model to segment and determine the
pathological properties of the
lesion; S3: comparing the efficiency of the built classifier
system and human doctors in diagnosing early
head and neck squamous
cell carcinoma; S4: screening key features related to the occurrence and development of
head and neck squamous cell carcinoma through the classifier system, forming a comprehensive
data set by connecting the key features with multi-
omics data, building an intermediate model based on the comprehensive
data set, and performing
bioinformatics analysis by using the intermediate model. The present application can improve the
early detection rate of HNSCC, improve the accuracy of
biopsy, and realize early and accurate tumor diagnosis.