Abstract: Tumor mutational burden(TMB) is positively correlated with the immunotherapy efficacy of non-small cell lung cancer(NSCLC).In clinical practice,tumor mutational burden is generally measured through whole exome sequencing(WES).How-ever,whole exome sequencing is complex,time-consuming,and expensive,making it inaccessible for most hospitals.In light of this,a low-cost,short-cycle,and high-accuracy deep learning model called DBFormer has been proposed to predict the tumor muta-tional burden of lung adenocarcinoma based on pathological tissue slices.Firstly,the color deconvolution structure combines the RGB and HED image information of the digital pathological images input into the model,enriching the information in the pathological images and making the model more suitable for medical task classification.Secondly,the images are processed through a four-layer pyramid structure,each layer consisting of a max-pooling la-yer and a DBFormer block.The max-pooling layer reduces the image size and increases the feature matrix dimensions,while the DBFormer block includes normalization layers and dual-route attention mechanisms for feature extraction and processing.Finally,337 and 200 lung cancer tissue pathological images are randomly selected from the TCGA-LUAD public dataset to construct binary and ternary classification datasets for experimentation.On the binary classification dataset,the DBFormer model achieves AUC,F1-Score,Precision,and Recall of 99.7%,97.3%,97.6%,and 97.2%,respectively.On the ternary classification dataset,DBFormer achieves an Accuracy,Precision,Recall,and F1-Score of 97.3%,97.0%,97.0%,and 97.1%,respectively.Experimental results demonstrate that the DBFormer model outperforms classical deep learning models in predicting the tumor mutational burden of lung adenocarcinoma based on di-gital pathological images.

Key words: Tumor mutation burden prediction, Lung adenocarcinoma, Histopathological image, Deep learning model, Self-attention mechanism