Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules

Owing to the extensive use of computed tomography (CT), the detection rate of solitary pulmonary nodules (SPNs) has shown a significantly increasing trend in recent years (1-3). Lung cancer screening trials with large sample sizes indicate that the detection rate of SPN ranges from 8% to 51%, with the vast majority at approximately 20% (4). In SPN cases, malignant nodules account for 5-69%, with an average rate of 40% (5,6). Early diagnosis and treatment of such malignant nodules greatly improves the overall survival rate and prognosis of patients with lung cancer (7,8). Therefore, correctly identifying malignancy in the detected SPN becomes a key point. The ideal goal is to diagnose and treat of malignant nodules early while avoiding unnecessary invasive examinations and surgery for benign nodules. The ultimate goal is to avoid unnecessary cost while allowing SPN patients to obtain the maximum cost benefit