Abstract
The increased use of computed tomography (CT) for lung cancer screening has led to an increase in the incidence of lung nodules. However, accurately localizing small or deep-seated nodules remains challenging and often requires image-guided techniques. CT-guided coil localization has emerged as a popular method for precise localization. A retrospective analysis was conducted on a cohort of patients who underwent CT-guided coil localization followed by surgical resection of lung nodules at the National University Hospital, Singapore, between 2015 and 2021. The study examined the rates of successful localization, procedural complications, and postoperative length of stay. Descriptive statistics were employed for data analysis. This retrospective study included 55 patients with pulmonary nodules, of whom 76.4% had a history of previous malignancy. A total of 58 nodules were successfully localized using coil placement and distributed across various lung lobes. The average dimensions of the nodules were 8.4 ± 3.2 mm × 10.1 ± 4.0 mm, with an average distance of 10.9 ± 9.0 mm from the pleura. Surgical resection of the coiled nodules resulted in an average resected volume of 91.1 ± 76.0 cm3 and a resection margin of 9.5 ± 7.3 mm. Failed localization occurred in only 3.6% of cases. Procedural complications were observed in 21.8% of patients, including hemothorax, persistent air leaks, pneumothoraces, pleural effusion, pneumonia, and atelectasis, all of which were appropriately managed. The average length of postoperative hospital stay was 6.7 ± 15.9 days. Preoperative CT-guided coil localization is a reliable and safe technique for accurately localizing lung nodules before surgical resection. The high success rate of this approach enables precise intraoperative planning. Despite procedural complications, their manageable nature and the absence of major adverse events highlight the safety of this technique. Incorporating CT-guided coil localization into routine clinical practice can significantly enhance surgical planning, improve patient outcomes, and optimize resource utilization.
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