[Research progress on the lung cancer risk prediction models]

Lung cancer is one of the malignancies with the highest incidence and mortality rates worldwide. Early detection and accurate diagnosis are critical for improving patient prognosis. In recent years, lung cancer risk prediction models have demonstrated increasing value in optimizing screening strategies for lung cancer. This review summarizes the current research progress in lung cancer risk prediction models, with a particular focus on recent advances in variable selection, model construction, and performance validation based on traditional statistical models and machine learning approaches. In addition, the key trends in model development are discussed, and the prospects and challenges of clinical application are analyzed, providing a reference for constructing more efficient and widely applicable lung cancer screening tools.