Robotic-assisted bronchoscopy combined with digital tomosynthesis for pulmonary nodules characterization: a single center experience.

INTRODUCTION

Diagnostic work-up of peripheral pulmonary lesions (PPLs) remains a challenge in interventional pulmonology. Conventional bronchoscopy and trans-thoracic needle aspiration (TTNA) often entail limitations in accuracy or safety. Robotic-assisted bronchoscopy with shape-sensing technology (ssRAB) combined with artificial intelligence (AI)-aided augmented fluoroscopy (LungVision system) represents an innovative approach to enhance lesion localization and diagnostic yield for small or otherwise hard-to-reach lesions while maintaining the safety profile of the procedure.

MATERIALS AND

METHODS

This retrospective observational study included all procedures performed under general anesthesia using the ssRAB (Ion™ robotic platform) in combination with C-arm based computed tomography (LungVision System) at the Interventional Pulmonology Unit of IRCCS Azienda Ospedaliero-Universitaria, Policlinico Sant'Orsola, Bologna (Italy), between December 2024 and September 2025. Overall, 82 patients with 94 sampled pulmonary lesions were included; in 12 patients, two distinct nodules were sampled during the same bronchoscopic procedure. Lesions features, procedural characteristics, diagnostic yield and complications were collected and statistically analyzed. Diagnostic yield was defined according to the 2024 Delphi consensus and STARD 2015 guidelines.

RESULTS

This study included 82 patients and 94 sampled pulmonary nodules. The median size of the lesion was 14 mm (IQR 11-18 mm). Target lesions were identified by radial endobronchial ultrasound (r-EBUS) in 80% of cases. A definitive cyto-histologic diagnosis was achieved in 74.5% of cases. Lesions ≥10 mm yielded a 79.2% diagnostic rate, versus 59.1% for smaller nodules. Diagnostic success was independently predicted by upper lobe location (OR 3.28; p = 0.031) and lesion visibility on CABT (OR 3.22; p = 0.043). The overall complication rate was 3.3% (three pneumothorax cases; no major bleeding). The median procedure time was 34.5 min, and most procedures were performed in a day-hospital setting.

DISCUSSION AND

CONCLUSIONS

The integration of robotic-assisted bronchoscopy with the LungVision system exhibited high diagnostic efficacy and a strong safety profile when sampling peripheral lung lesions. The synergy of real-time AI aided augmented fluoroscopy and robotic technologies facilitates precise lesion targeting, even within anatomically complex areas. However, follow-up data were not available for non-diagnostic cases; therefore, false-negative rates, sensitivity, and diagnostic accuracy could not be assessed. Given the significant cost associated with this procedure, future prospective studies are warranted to further validate these results and refine patient selection criteria to optimize its clinical application.