Lung Cancer Associated With Cystic Airspaces

The goal of this observational study is to develop and validate an artificial intelligence (AI)-based multimodal radiomics model that integrates preoperative CT imaging features and clinical data to predict pathological high-risk features in patients with lung cancer associated with cystic airspaces (LCCA). The main questions it aims to answer are: Can an AI-based multimodal radiomics model accurately predict pathological high-risk features in LCCA before surgery? Does the integration of CT imaging features and clinical variables improve preoperative risk stratification compared with imaging or clinical information alone?

The goal of this observational study is to determine the most accurate tumor size measurement method for T-staging and prognostic assessment in lung cancer with cystic airspaces (LCCA). The main questions it aims to answer are: * What is the optimal T-staging approach for accurately classifying lung cancer with cystic airspaces （LCCA) and predicting patient outcomes? * How do imaging features of cystic lesions correlate with their pathological characteristics? * What is the relationship between imaging features of cystic airspace-associated lesions and patient prognosis? * Can optimizing the T-staging method improve clinical decision-making in patients with LCCA?

The goal of this observational study is to investigate the multi-omics characterization early-stage lung cancer exhibiting distinct phenotypes including lung cancer associated with cystic airspaces, multiple primary lung cancers, and so on. The main questions it aims to answer are: * What are the differences in pathogenesis of non-small cell lung cancer with different phenotypes explored by multi-omics? * Whether differential genes lead to potential prognostic models and therapeutic targets? Participants will be followed up after surgery to answer prognosis: whether they have recurred, and the time to recurrence.