4.7 Article

Correlation between pleural tags on CT and visceral pleural invasion of peripheral lung cancer that does not appear touching the pleural surface

Journal

EUROPEAN RADIOLOGY
Volume 31, Issue 12, Pages 9022-9029

Publisher

SPRINGER
DOI: 10.1007/s00330-021-07869-y

Keywords

Non-small-cell lung cancer; Helical computed tomography; pleura

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The study evaluated the association between peripheral non-small-cell lung cancer and visceral pleural invasion, finding that a bridge tag sign on CT may improve the accuracy of predicting VPI.
Objectives To evaluate the association between a sign and visceral pleural invasion (VPI) of peripheral non-small-cell lung cancer (NSCLC) that does not appear touching the pleural surface. Methods A total of 221 consecutive patients with NSCLC that did not appear touching the pleural surface, <= 3 cm in solid tumor diameter, and was surgically resected between January 2009 and December 2015 were included. We focused on the flat distortion of the tumor caused by an arch-shaped linear tag between the tumor and the pleura on CT and named it a bridge tag sign. We evaluated the associations between the clinicopathological features of the tumor, including the bridge tag sign, and VPI. We also evaluated the associations between histopathological findings and the bridge tag sign. The utility of the bridge tag sign in the diagnosis of VPI was statistically assessed. Results The bridge tag sign was observed in 48 (20.8%) patients. VPI was positive in 9 (4.1%) patients; among these, the bridge tag sign was positive in 8 patients. In multivariate analysis, a bridge tag sign was significantly associated with VPI. The bridge tag sign was associated with longer contact length of the pleura with the tumor and trapezoid type pleural retraction. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the bridge tag sign in the diagnosis of VPI were 88.9%, 83.5%, 83.7%, 18.6%, and 99.4%, respectively. Conclusions A bridge tag sign on CT might improve the accuracy of the prediction of VPI.

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