Research Progress on AI-Driven Biomarker Prediction in Non-Small Cell Lung Cancer

This study overviews the application of artificial intelligence (AI) in the prediction of biomarkers for non-small cell lung cancer (NSCLC). Different from recent reviews on AI-driven radiogenomics that adopt clinical or imaging-based classification, this work innovatively takes AI model architecture as the core classification framework. By analyzing multimodal data (mainly CT) and modeling paradigms (radiomics, deep learning and fusion models), it reveals the current research status centered on EGFR mutation prediction. In terms of technological evolution, prediction models are evolving from traditional radiomics to end-to-end learning, Transformer architecture and multimodal perception-driven deep learning paradigms. This study deeply analyzes the performance differences among different models, fills the application gaps in existing reviews, and provides an important benchmark reference for the clinical optimization of AI-assisted tools and the promotion of standardized precise diagnosis and treatment of NSCLC.