Objective To explore a novel method for early lung cancer screening based on exhaled breath analysis. MethodsThis study enrolled patients with suspected pulmonary malignancies and healthy individuals undergoing physical examinations at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Qingchun and Qiantang campuses) from September 2023 to June 2024. Enrolled subjects were categorized into a lung cancer group, a benign nodule/tumor group, and a healthy control group. Exhaled breath samples were collected using a sensor array constructed from multiple graphene composite materials to capture breath fingerprints. Based on the collected data, screening and diagnostic models for lung cancer were developed and their performance was evaluated. ResultsA total of 4 580 subjects were included. Among them, 3 195 were pathologically diagnosed with pulmonary malignancies, including 1 394 males and 1 801 females with a mean age of (58.93±12.37) years, 599 were diagnosed with benign nodules/tumors (339 males, 260 females; mean age: 57.10±11.06 years), and 786 were healthy controls with no pulmonary nodules detected on chest CT (420 males, 366 females; mean age: 29.75±9.32 years). The screening model for high-risk populations (distinguishing patients with lung cancer/high-risk pulmonary nodules from healthy individuals) demonstrated excellent performance, with an area under the receiver operating characteristic curve (AUC) of 0.926. At the optimal Youden’s index (cutoff threshold of 63.5%), the external test set achieved a specificity of 85.2%, a sensitivity of 88.4%, and an accuracy of 86.8%. The diagnostic model (distinguishing patients with lung cancer/premalignant lesions from those with benign pulmonary nodules/healthy individuals) achieved an AUC of 0.818. At its optimal Youden’s index (cutoff threshold of 47.0%), the external test set showed a specificity of 71.7%, a sensitivity of 77.3%, and an accuracy of 74.5%. ConclusionThe non-invasive breath analysis platform based on a sensor array, developed in this study, can achieve rapid and relatively accurate lung cancer screening by analyzing breath fingerprints. This confirms the feasibility of this technology for early lung cancer screening and holds promise for facilitating the early detection and intervention of lung cancer.