A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization

Lung cancer surgery presents significant challenges due to its complexity and the need for precise risk stratification to improve patient outcomes. This study presents a Transformer-based multi-scale deep learning framework that integrates imaging, clinical, and genomic data to optimize decision-making surrounding surgery. By using the self-attention mechanism in Transformers and multi-scale feature extraction, the model expertly explores different data modalities. Therefore, it enables a precise prediction of surgical risks, such as delayed extubation and mortality; besides, it further performs risk stratification, having the model improve resource utilization by identifying high- and low-risk patients, ensuring that intervention is matched accordingly and resources are not wasted on unnecessary measures. Thorough evaluations, including ablation experiments, case analyses, and error analyses, prove the model's robustness and practical applicability in a clinical setting. This study demonstrates the game-changing potential of advanced deep learning techniques in the field of precision medicine and provides a concrete framework for personalized treatment in lung cancer surgery, laying the foundation for broader healthcare applications.