Abstract
Lung nodules are critical indicators for early lung cancer detection, yet accurately distinguishing between benign and malignant lesions remains a clinical challenge. This review summarizes advances in predictive models for lung nodule risk assessment, spanning classical clinical-imaging models, biomarker-based approaches, and artificial intelligence (AI)-driven tools. While classical models provide a foundational framework, their performance often varies across populations. Biomarkers and AI models significantly enhance diagnostic precision by capturing molecular and imaging features imperceptible to the human eye. However, issues such as generalizability, standardization, and data security persist. The most promising direction lies in multimodal integration, combining clinical, imaging, biomarker, and AI data to achieve superior accuracy with an area under the curve (AUC) >0.90. Future efforts should focus on multi-center validation, standardized biomarker assays, and data secure, scalable AI systems to translate these innovations into routine clinical practice, enabling personalized and early lung cancer diagnosis.