This is a prospective observational study designed to address the clinical challenge posed by the high false-positive rate associated with CT imaging in early lung cancer screening.

The primary objective is to develop a multi-omics technology for early lung cancer screening, leveraging **exhaled breath metabolomics, plasma metabolomics, radiomics, and liquid biopsy. Based on large-sample detection data, the study aims to construct a **multi-dimensional, sequential decision-making system**. This system utilises the high accessibility of metabolomics for primary screening, combined with radiomics and ctDNA technologies for subsequent **differentiation and definitive diagnosis.

The research plans to prospectively enrol 300 patients with non-small cell lung cancer, along with corresponding subjects with benign nodules and healthy controls. By optimising the model using machine learning and deep learning algorithms (such as SVM, HRNet, and PAResNet), the ultimate goal is to establish a novel lung cancer early screening system characterised by **high sensitivity, high accuracy, and high accessibility**, enabling the precise differentiation and screening of healthy individuals, benign pulmonary nodules, and early-stage lung cancer.

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.

Inclusion Criteria

1. Age >18 years old.
2. Availability of both exhaled breath and peripheral blood samples, and raw CT image data; the collection time is within one month before biopsy or surgical resection, and the subject has not received any treatment in between.
3. Pulmonary nodular lesions identified by chest CT with a diameter < 3 cm.
4. Pulmonary nodular lesions must be surgically resected and have complete, definitive pathological information regarding their benign or malignant nature.
5. No prior history of malignant tumors.
6. Has not received anti-tumor treatments such as radiotherapy, chemotherapy, or targeted therapy.
7. Signed informed consent.

Exclusion Criteria

1. Missing clinical data or incomplete sample collection.
2. Presence or suspicion of active infection or other severe co-morbidities.
3. Abnormal liver or kidney function.
4. Indefinite or inconclusive postoperative pathological results.

This study proposes to prospectively recruit participants with early-stage non-small cell lung cancer, benign pulmonary lesions (including tuberculosis, hamartomas, inflammatory conditions, etc.), and healthy individuals undergoing medical examinations at Peking University People's Hospital, Rongcheng County People's Hospital in Xiong'an, Tangshan Workers' Hospital, Union Hospital affiliated with Tongji Medical College of Huazhong University of Science and Technology, and the 731 Hospital of China Aerospace Science and Industry Corporation.