Research on Early Screening and Diagnosis of Pulmonary Nodules Based on Novel Non-invasive Technologies.

Lung cancer is one of the malignant tumours with a poor prognosis. Data from 61 countries globally show that the 5-year survival rate for lung cancer patients is only 10.0% to 20.0%. Since 2005, China has launched several major national public health service projects, including lung cancer screening initiatives such as the Rural Cancer Early Diagnosis and Treatment Program and the Urban Cancer Early Diagnosis and Treatment Program, gradually establishing a national network for lung cancer screening and early diagnosis/treatment. Although China has made significant progress in the diagnosis and treatment of lung cancer, the 5-year survival rate for the Chinese population was 19.7% between 2012 and 2015. The prognosis for lung cancer remains poor, making early diagnosis and treatment an essential guide for our research to improve patient survival. CT scanning has become the recognised standard tool for lung cancer screening and early diagnosis. However, the false-positive rate for highly suspected nodules remains high, at 30% to 40%, and the accuracy of preoperative lesion assessment needs improvement. Therefore, establishing a highly sensitive, highly specific, minimally invasive, or even non-invasive, precise diagnostic method for early lung cancer, thereby avoiding unnecessary surgery, is a critical clinical problem urgently needing resolution, and is of great significance for advancing China's level of early lung cancer diagnosis. This study will first systematically evaluate the efficacy and accessibility of \*\*exhaled breath metabolomics, plasma metabolomics, and radiomics in early lung cancer screening and diagnosis using a prospective database. Based on large-sample detection data, we will construct a novel multi-dimensional stereo lung cancer early screening system. This system uses exhaled breath metabolomics and plasma metabolomics for initial screening. Then it integrates multiple omics technologies, including radiomics, cfDNA methylation and fragmentomics, and the original metabolomics data, for differentiation and definitive diagnosis. Modelling algorithms will be optimised using methods such as cross-validation, internal validation, and stratified validation to form a stable lung cancer screening system.