Abstract

Background

Preoperative computed tomography (CT)-guided localization has been used to guide the video-assisted thoracoscopic surgery (VATS) sublobar (wedge or segmental) resection for pulmonary nodules (PNs). We aimed to assess the relative efficacy and safety of CT-guided methylene blue (MB)- and coil-based approaches to the preoperative localization of multiple PNs (MPNs).

Methods

Between January 2015 and December 2020, 31 total cases suffering from MPNs at our hospital underwent CT-guided localization and subsequent VATS resection in our hospital, of whom 15 and 16 respectively underwent MB localization (MBL) and coil localization (CL). The clinical effectiveness and complication rates were compared between 2 groups.

Results

The PN- and patient-based technical success rates in the MBL group were both 100%, whereas in the CL group they were 97.2% (35/36) and 93.8% (15/16), respectively, with no substantial discrepancies between groups. Patients in the MBL group illustrated a substantially shorter CT-guided localization duration compared with the CL group (18 min vs. 29.5 min, P < 0.001). Pneumothorax rates (P = 1.000) and lung hemorrhage (P = 1.000) were comparable in both groups. In the MBL and CL groups, the median interval between localization and VATS was 1 h and 15.5 h, respectively (P < 0.001). One-stage VATS sublobar resection of the target nodules was successfully performed in all patients from both groups.

Conclusion

Both CT-guided MBL and CL can be readily and safely utilized for preoperative localization in individuals who had MPNs, with MBL being correlated with a shorter localization duration compared with CL.

Details

Title
Preoperative computed tomography-guided localization for multiple pulmonary nodules: comparison of methylene blue and coil
Author
Sheng-Feng, Zhang; Liu, Hai-Ri; Ai-Li, Ma; Er-Liang, Li
Pages
1-7
Section
Research
Publication year
2022
Publication date
2022
Publisher
Springer Nature B.V.
e-ISSN
1749-8090
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2703801855
Copyright
© 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.