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Shape sensing robotic assisted bronchoscopy versus virtual bronchoscopic navigation in the diagnosis of peripheral pulmonary nodules. | LitMetric

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Article Abstract

Bronchoscopy is a commonly used method for pulmonary nodule biopsy. Shape-sensing robotic-assisted bronchoscopy (ssRAB), which features enhanced catheter articulation and stability, has recently become available. Although the diagnostic performance of ssRAB appears to surpass that of conventional guided bronchoscopy, the existing data are limited. The aim of this study was to evaluate the diagnostic efficacy and safety of ssRAB compared with those of virtual bronchoscopic navigation (VBN) for diagnosing peripheral pulmonary nodules (PPNs). This retrospective comparative study involved consecutive patients who underwent ssRAB or VBN for diagnosing PPNs at the Henan Provincial People's Hospital between October 2021 and June 2022. Clinical data, nodule and procedure characteristics, outcomes, and complications were compared. Final diagnoses were established on the basis of pathological interpretations, which were complemented by clinical and radiographic follow-up data and/or additional sampling, as necessary. The enrolled patients underwent follow-up visits for a duration of 2 years after the procedure. The study included 59 patients, of whom 30 received ssRAB and 29 received VBN. There were no significant differences between the two groups in terms of patient demographics or nodule or procedure characteristics. Compared with the VBN group, the ssRAB group presented a significantly greater overall diagnostic yield (69.0% vs. 90.0%, p = 0.045). No procedure-related complications occurred in the ssRAB group, whereas two cases of pneumothorax occurred in the VBN group, with symptom improvement after the placement of closed thoracic drainage. Compared with VBN, ssRAB offers a more promising diagnostic approach with a greater diagnostic efficacy for sampling PPNs.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12227645PMC
http://dx.doi.org/10.1038/s41598-025-08798-2DOI Listing

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