ArticleEuropean radiology2021
Differentiating focal interstitial fibrosis from adenocarcinoma in persistent pulmonary subsolid nodules (> 5 mm and < 20 mm): the role of coronal thin-section CT images.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Combined Model Integrating Clinical-Imaging and Deep Learning to Discriminate Between Persistent Inflammatory and Malignant Subsolid Pulmonary Nodules.Journal of imaging informatics in medicine · 2026Article
- Correlation between CT growth patterns and invasiveness progression in neoplastic subcentimeter sub-solid nodules.Annals of medicine · 2025Article
- Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm.Insights into imaging · 2024Article
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6 authors.
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Abstract
objectivesTo investigate thin-section computed tomography (CT) features of pulmonary subsolid nodules (SSNs) with sizes between 5 and 20 mm to determine predictive factors for differentiating focal interstitial fibrosis (FIF) from adenocarcinoma.
methodsFrom January 2017 to December 2018, 169 patients who had persistent SSNs 5-20 mm in size and underwent preoperative nodule localization were enrolled. Patient characteristics and thin-section CT features of the SSNs were reviewed and compared between the FIF and adenocarcinoma groups. Univariable and multivariable analyses were used to identify predictive factors of malignancy. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of these factors.
resultsAmong the 169 enrolled SSNs, 103 nodules (60.9%) presented as pure ground-glass opacities (GGOs), and 40 (23.7%) were FIFs. Between the FIF and adenocarcinoma groups, there were significant differences (p< 0.05) in nodule border, shape, thickness, and coronal/axial (C/A) ratio. Multivariable analysis demonstrated that a well-defined border, a nodule thickness >4.2, and a C/A ratio >0.62 were significant independent predictors of malignancy. The performance of a model that incorporated these three predictors in discriminating FIF from adenocarcinoma achieved a high area under the ROC curve (AUC, 0.979) and specificity (97.5%).
conclusionsFor evaluating persistent SSNs 5-20 mm in size, the combination of a well-defined border, a nodule thickness > 4.2, and a C/A ratio > 0.62 is strongly correlated with malignancy. High accuracy and specificity can be achieved by using this predictive model. KEY POINTS: • Thin-section coronal images play an important role in differentiating FIF from adenocarcinoma. • The combination of a well-defined border, nodule thickness>4.2 mm, and C/A ratio >0.62 is associated with malignancy. • This predictive model may be helpful for managing persistent SSNs between 5 and 20 mm in size.
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