Predicting Tumor Mutation Burden of Lung Cancer Based on Residual Network

Medical studies have found that Tumor Mutation Burden (TMB) is positively correlated with the efficacy of immunotherapy for Non-Small Cell Lung Cancer (NSCLC), and TMB value can predict the efficacy of targeted therapy and chemotherapy. However, the calculation of TMB value mainly depends on the Whole Exon Sequencing (WES) technology, which is usually expensive and takes too much time. To deal with this problem, this paper explores the connection between TMB and digital pathology images, predicting TMB using common clinical digital slices. We proposes RCA-MSAG, a deep learning model based on the Residual Coordinate Attention (RCA) structure and Multi-Scale Attention Guidance (MSAG) module, enhancing TMB prediction accuracy. The model takes ResNet-50, integrating Coordinate Attention (CA) and MSAG to extract critical information from lung cancer pathology. Using The Cancer Genome Atlas (TCGA) dataset, the model achieves 96.2% accuracy, 96.4% precision, 96.2% recall, and a 96.3% F1 score, outperforming mainstream models. This model shows promise in aiding clinical diagnoses and guiding TMB predictions.