BACKGROUND: This study proposed a precise diagnostic model for malignant solitary pulmonary nodules (SPNs). This model can be used to identify objective and quantifiable image features and guide the clinical treatment strategy adopted for SPNs. This model will help clinicians optimize management strategies for SPN. METHODS: In this retrospective study, the clinical data of 455 patients of SPN with defined pathological diagnosis between September 2016 and August 2019 were collected and analyzed. The data included pathological diagnosis, preoperative computed tomography (CT) diagnosis, gender, age, smoking history, family history of tumor, previous history, and contact history data. The quantitative image features and radiomic information of the SPNs were provided using computer-aided detection (CAD) “digital lung” software. The Chi-squared test was used to assess the accuracy between CAD and conventional CT in the diagnosis of SPNs. The diagnostic model for benign or malignant SPNs was developed using a multivariate logistic regression analysis that comprises 6 radiomic factors (irregularity, average diameter, COPD910, proportion of emphysema, proportion of fat, and average density of related blood vessels). The area under the receiver operating characteristic curve was used to evaluate the performance of the model in determining SPN risk of malignancy. RESULTS: There was a statistical difference in the accuracy of CAD and conventional CT in diagnosing SPNs. According to the golden standard pathological diagnosis, the diagnostic accuracy of CAD (81%) was higher than that of conventional CT (63.7%) (P