Useful computed tomography features for differentiating between focal atelectasis and pleural dissemination on preoperative evaluations of thymic epithelial tumors

Shingo Omata, Yoshiyuki Ozawa, Motoo Nakagawa, Masaki Hara, Yuta Shibamoto

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Purpose: Distinguishing between focal atelectasis (FA) and pleural dissemination (PD) is important for determining the optimal therapeutic strategy for thymic epithelial tumors (TET). This study aimed to identify useful computed tomography (CT) features for distinguishing between these two conditions. Materials and methods: We retrospectively analyzed preoperative CT images of 27 TET, which included 40 PD and 40 FA lesions. Two radiologists independently interpreted the pleural lesions without knowing the final diagnosis. The CT images were evaluated to assess the lesion location, size, and shape, presence of a spinous shadow and ground glass opacities (GGO) near to the lesion, and the shortest distance from the lesion to the nearest peripheral pulmonary artery (PA). Results: FA lesions tended to occur on the dorsal side (90%, P = 0.024); have shorter major and minor axes (P < 0.001), a triangular shape (43%, P = 0.002), a spinous shadow (45%, P = 0.001) and GGO (28%, P = 0.006); and be close to a peripheral PA (P = 0.007). Ninety percent of PD lesions were located in the left thorax, and all of them were ipsilateral to the tumor (both P < 0.001). The 9 examined factors exhibited sensitivity, specificity, positive predictive, and negative predictive values of 85%, 95%, 94%, and 86%, respectively, for diagnosing FA (when ≥3 factors were present), and 90%, 48%, 63%, and 83%, respectively, for diagnosing PD (when ≥4 factors were present). Conclusion: The site, size, and shape of a lesion; the presence of a spinous shadow/GGO; and the distance to the nearest PA are useful for distinguishing between PD and FA.

Original languageEnglish
Pages (from-to)38-43
Number of pages6
JournalEuropean journal of radiology
Volume103
DOIs
Publication statusPublished - 06-2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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