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Updated: Jun 12, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Clinical and Computed Tomography Characteristics of Inflammatory Solid Pulmonary Nodules with Morphology Suggesting
Wei-Hua Zhao1, Li-Juan Zhang1, Xian Li2
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Rationale And Objectives:
To investigate the clinical and computed tomography characteristics of inflammatory solid pulmonary nodules (SPNs) with morphology suggesting malignancy, hereinafter referred to as atypical inflammatory SPNs (AI-SPNs).
Materials And Methods:
The CT data of 515 patients with SPNs who underwent surgical resection were retrospectively analyzed. These patients were divided into inflammatory and malignant groups and their clinical and imaging features were compared. Binary logistic regression analysis was performed to identify the independent factors for diagnosing AI-SPNs. An external validation cohort included 133 consecutive patients to test the model's predictive efficiency.
Results:
Univariate analysis showed that age < 62 years, male sex, maximum spiculation length > 9 mm, polygonal shapes, three-planar ratio > 1.48, Lung window/mediastinal window (L/M) ratio > 1.13, pleural tag type I, satellite lesions, and halo sign were more frequent in AI-SPNs, whereas pleural tag type III, bronchial truncation, and perifocal fibrosis were more common in malignant SPNs (M-SPNs) (all P < 0.05). Binary logistic regression showed age < 62 years, male sex, polygonal shape, three-planar ratio > 1.48, L/M ratio > 1.13, pleural tag type I, satellite lesions, halo sign, and absence of bronchial truncation were independent factors for diagnosing AI-SPNs (AUC, sensitivity, specificity, and accuracy of 0.951, 83.30%, 92.30%, and 87.20%, respectively). In the external validation cohort, the AUC, sensitivity, specificity, and accuracy were 0.969, 90.47%, 90.00%, and 90.23%, respectively.
Conclusion:
AI-SPNs and M-SPNs exhibited different clinical and imaging characteristics. A good understanding of these differences may help reduce diagnostic errors in AI-SPNs and enable to choose an optimal treatment strategy.
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