Evaluation of benign and malignant pulmonary nodules based on clinical and contrast-enhanced computed tomography...

Lung cancer is a leading cause of cancer-related death worldwide, and accurate differentiation of pulmonary nodules is crucial for clinical management. Low-dose and multiphasic computed tomography (CT) provides improved nodule detection, but the incremental diagnostic value of contrast-enhanced phases remains unclear. The objective of this study was to establish a prediction model based on clinical and CT data from three phases (noncontrast phase, arterial phase, and venous phase) for differentiating benign and malignant pulmonary nodules.

A total of 863 patients from multiple centers with benign or malignant pulmonary nodules pathologically confirmed between 2015 and 2024 were retrospectively analyzed. Data from The First Affiliated Hospital of Bengbu Medical University and Anqing Municipal Hospital (Anhui, China), constituted the training cohort (n=470), data from Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University and Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University (Zhejiang, China), were used as the test cohort (n=205), and data from an additional center, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University (Hangzhou, Zhejiang, China), served as the validation cohort (n=188). According to different CT scanning phases, the data were divided into the following four groups, from which models were constructed: noncontrast phase, arterial phase, venous phase, and noncontrast phase each combined with the enhanced dual phase. Candidate variables were screened via univariate analysis and subsequently entered into multivariable logistic regression to identify independent predictors for model construction. Model performance was evaluated via receiver operating characteristic analysis and the area under the curve (AUC).

A total of 863 patients with pulmonary nodules were included, comprising 518 malignant nodules and 345 benign nodules. The final models incorporated clinical and imaging predictors, including age, surgical history, hepatitis or liver cirrhosis, interstitial lung disease, bronchiectasis, obstructive pneumonia, mixed ground-glass opacity, lobulation, pleural retraction, minimum CT attenuation value, and mean CT attenuation value. Model 1 retained 11 predictors, whereas models 2-4 retained 10 predictors with identical predictor sets. In the validation cohort, the AUC, sensitivity, and specificity values for model 1 were 0.909, 0.852, and 0.740, respectively; those for models 2 and 4 were 0.904, 0.755 and 0.849, respectively; and those for model 3 were 0.904, 0.870, and 0.712, respectively. There was no significant difference in the diagnostic performance of the four models (P>0.05).

The four prediction models established based on clinical and three-phase CT data all demonstrated high diagnostic efficiency in differentiating benign and malignant pulmonary nodules.