Differentiating focal interstitial fibrosis from adenocarcinoma in persistent pulmonary subsolid nodules (> 5 mm and < 20 mm): the role of coronal thin-section CT images

• Published in: European radiology 2021-11, Vol.31 (11), p.8326-8334
• Main Authors: Ko, Kai-Hsiung, Huang, Tsai-Wang, Chang, Wei-Chou, Huang, Hsu-Kai, Tsai, Wen-Chiuan, Hsu, Hsian-He
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Cancer; Computed Tomography; Diagnostic Radiology; Fibrosis; Imaging; Internal Medicine; Interventional Radiology; Localization; Lung nodules; Malignancy; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Nodules; Prediction models; Radiology; Thickness; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-07940-8

Objectives To 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. Methods From 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. Results Among 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%). Conclusions For 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 .