Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields.