# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 1 Introduction  
As one of the most serious diseases, lung disease threatens human health. According to the GLOBOCAN2021 database [1], lung cancer remains the leading cause of human death, with approximately 17.96 million deaths globally each year, accounting for 18% of total cancer mortality. Previously, the best way to deal with lung cancer was to perform early cancer screening, which may improve the 5-year survival rate of patients by 16-49% with early detection and treatment. Accompanied by improving computed tomography (CT) imaging technology development, CT images have become the most effective and feasible technique to detect lung cancer. Results from the National Cancer Institute's Lung Cancer Screening Trial indicated that CT detected 4-10 times more cases than chest X-rays [2], reducing the mortality rate of lung cancer by 20.3%. Therefore, it is vitally crucial to use CT for early screening and diagnosis of lung disease.  
A \"sign\" in CT lung scans refers to a radiologic feature that implies a specific disease condition. Radiologists diagnose and detect lesions by analyzing their imaging characteristics in CT scans. In clinical practice, these characteristics are called CT imaging signs (CISs), often known as CT features. CISs are particularly significant for the judgment of benign and malignant pulmonary diseases and the kinds of lesions [3]. Some common CISs can be used as the direct foundation for doctors to diagnose illnesses, while others can aid in restricting the diagnosis. For example, ground-glass opacity (GGO), spiculation, pleural indentation, lobulation, and cavity signs that appear in CT scans are strongly related with malignant lung cancer. However, certain other CISs, such as tree in bud and crescent sign, are connected with benign lung illnesses. Therefore, it provides an essential reference value for the qualitative identification of lung lesions [4; 5]. CT imaging signs detection ismore crucial and acts as a basis for the physician to make a credible diagnosis, which can effectively improve the speed and accuracy of clinical diagnosis, and it has great clinical application value.  
Due to the diversity of CISs in lung diseases, the current CT image diagnosis process is more dependent on the experience of clinicians, which has that difficulties of strong subjectivity and high misdiagnosis rate. Therefore, adopting computer-aided diagnostic approaches to quantify and reliably diagnose CISs has become an urgent requirement for clinical therapy, which aims to assist clinicians in enhancing the accuracy of diagnosis. However, it is still a challenging task to accurately quantify and detect complex and changeable CISs. To solve these issues, a significant variety of solutions have been presented by local and foreign academics based on distinct processing steps of standard machine learning methods and deep learning methods. In the processing framework based on classic machine learning approaches, researchers have proposed optimization algorithms for lesion segmentation, feature extraction, and false positive reduction. In the study of deep learning approaches, researchers typically create breakthroughs in the design, selection, and matching of network models.  
Some progress has been achieved in the investigation of lung CT signs; however, comprehensive assessments of studies from the standpoint of signs are relatively rare. Therefore, this paper first systematically reviews the current research status of lung CT signs, summarizes a general framework to express and describe the existing methods, and makes a systematic analysis and comparison of traditional machine learning methods and deep learning methods in the detection of CISs. Finally, the present obstacles and difficulties to overcome in this field are discussed, and the prospective improvement direction of future study efforts is pointed out.