The invention relates to the technical field of
gene and immune molecule detection, in particular to a method for predicting
lung cancer EGFR
genotype and immune molecule expression level, and the method specifically comprises the following steps: S1, obtaining a CT image: obtaining the CT image of a non-
small cell lung cancer patient, the CT image comprising a tumor area and a peritumor area; s2, image
omics characteristics are extracted, wherein the image
omics characteristics are extracted from the tumor area and the peritumor area respectively; s3, extracting deep network features: extracting the deep network features from the CT image by using a deep neural network based on an attention mechanism; and S4, constructing a prediction model: inputting the
radiomics characteristics and the deep network characteristics into the prediction model, and outputting prediction results of EGFR genotypes and immune molecule expression levels. The method for predicting the
lung cancer EGFR
genotype and immune molecule expression level has the advantages of non-invasiveness, high efficiency, accurate prediction, clinical practicability, model
interpretability and technical compatibility and flexibility.