The role of radiological imaging in differentiating malignant and benign pulmonary nodules: a retrospective study.

backgroundAccurate differentiation between malignant and benign pulmonary nodules remains a critical challenge in clinical practice. This study aimed to evaluate the diagnostic performance of computed tomography (CT) imaging features in distinguishing malignant from benign pulmonary nodules.

methodsA retrospective analysis was conducted on 200 patients with pulmonary nodules who underwent chest CT scanning and subsequent histopathological confirmation between January 2020 and December 2024. CT imaging features including nodule size, density, margin characteristics, internal characteristics, and relationship to adjacent structures were analyzed. To systematically evaluate the independent contribution of CT imaging features, multivariate logistic regression analysis was performed using a progressive modeling approach. Model stability was assessed using bootstrap internal validation (1,000 resamples) and verified by AIC-based backward stepwise selection. Receiver operating characteristic curve analysis was employed to assess the diagnostic performance of CT imaging features.

resultsAmong 200 cases, 88 (44.00%) were malignant and 112 (56.00%) were benign. Malignant nodules demonstrated significantly larger mean diameter (18.76 ± 8.52 mm vs. 12.85 ± 5.94 mm, P < 0.001), higher prevalence of spiculated margins (70.45% vs. 28.57%, P < 0.001), lobulation (67.05% vs. 33.04%, P < 0.001), and pleural indentation (54.55% vs. 22.32%, P < 0.001). Calcification was more common in benign nodules (43.75% vs. 12.50%, P < 0.001). After adjustment for clinical confounders, nodule size (OR = 1.095, P < 0.001), spiculated margin (OR = 4.523, P < 0.001), lobulation (OR = 2.845, P < 0.001), and calcification (OR = 0.185, P < 0.001) remained independent predictors of malignancy. AIC-based backward stepwise selection independently confirmed the same four CT features, supporting the robustness of variable selection. The integrated model incorporating four CT imaging features achieved sensitivity of 71.59%, specificity of 88.39%, and area under the curve of 0.870(optimism-corrected AUC = 0.845).

conclusionChest CT imaging features, particularly nodule size, spiculated margin, lobulation, and calcification patterns, are independent predictors of malignancy and demonstrate good diagnostic performance in differentiating malignant from benign pulmonary nodules. These findings provide preliminary evidence supporting the integration of structured CT feature analysis into clinical decision-making; however, external validation in independent cohorts is needed before clinical implementation.