DIAGNOSTIC YIELD OF SHAPE-SENSING ROBOTIC-ASSISTED BRONCHOSCOPY: AN UPDATED META-ANALYSIS
Author(s)
Matthew Lien, MS, PharmD1, Neera Patel, MS2, Ana Yankovsky, MSc3, Usha Kreaden, MSc1;
1Intuitive Surgical, Sunnyvale, CA, USA, 2Intuitive Surgical, Yorba Linda, CA, USA, 3Intuitive Surgical, Palo Alto, CA, USA
1Intuitive Surgical, Sunnyvale, CA, USA, 2Intuitive Surgical, Yorba Linda, CA, USA, 3Intuitive Surgical, Palo Alto, CA, USA
OBJECTIVES: Shape-sensing robotic-assisted bronchoscopy (ssRAB) is an emerging technology designed to improve diagnostic accuracy in lung nodule biopsy. This meta-analysis aimed to quantify the overall diagnostic yield of ssRAB and investigate the impact of various factors on diagnostic yield through subgroup analyses.
METHODS: We reviewed literature published from January 2019 to July 2025 for studies reporting diagnostic outcomes in adults undergoing lung biopsy with ssRAB. Studies were excluded if they used other navigation bronchoscopy platforms, lacked relevant outcomes, enrolled <10 patients, were preclinical studies, had non-primary diagnostic objectives, were conference abstracts or secondary sources (reviews, letters, and editorials). Meta-analysis was performed in R, pooling diagnostic yield and subgroup estimates using the Freeman-Tukey transformation; random-effects modeling was applied for significant heterogeneity (I² > 50%). Subgroup analyses were performed for strict definition of diagnostic yield, use of cone-beam computer tomography (CBCT), nodule size, and combinations of these factors.
RESULTS: Of 263 screened studies, 30 studies were included in the analysis, representing 5,539 nodules. Overall diagnostic yield across all definitions was 86%, and 82% when a strict definition was applied. For only studies reporting a strict definition of diagnostic yield, yields were similar with and without CBCT use (82% vs. 80%, p=0.68 ). For nodules ≤20 mm based on individual nodule data, diagnostic yield was 86% across all definitions. Among the studies with a median nodule size ≤20 mm, strict diagnostic yield was 81% and were comparable with and without CBCT use (82% vs. 79%, p=0.37).
CONCLUSIONS: Shape-sensing robotic-assisted bronchoscopy demonstrates a high diagnostic yield for lung nodule biopsy, with consistent performance across definitions and subgroups. These findings support ssRAB as a reliable diagnostic approach across a range of clinical scenarios.
METHODS: We reviewed literature published from January 2019 to July 2025 for studies reporting diagnostic outcomes in adults undergoing lung biopsy with ssRAB. Studies were excluded if they used other navigation bronchoscopy platforms, lacked relevant outcomes, enrolled <10 patients, were preclinical studies, had non-primary diagnostic objectives, were conference abstracts or secondary sources (reviews, letters, and editorials). Meta-analysis was performed in R, pooling diagnostic yield and subgroup estimates using the Freeman-Tukey transformation; random-effects modeling was applied for significant heterogeneity (I² > 50%). Subgroup analyses were performed for strict definition of diagnostic yield, use of cone-beam computer tomography (CBCT), nodule size, and combinations of these factors.
RESULTS: Of 263 screened studies, 30 studies were included in the analysis, representing 5,539 nodules. Overall diagnostic yield across all definitions was 86%, and 82% when a strict definition was applied. For only studies reporting a strict definition of diagnostic yield, yields were similar with and without CBCT use (82% vs. 80%, p=0.68 ). For nodules ≤20 mm based on individual nodule data, diagnostic yield was 86% across all definitions. Among the studies with a median nodule size ≤20 mm, strict diagnostic yield was 81% and were comparable with and without CBCT use (82% vs. 79%, p=0.37).
CONCLUSIONS: Shape-sensing robotic-assisted bronchoscopy demonstrates a high diagnostic yield for lung nodule biopsy, with consistent performance across definitions and subgroups. These findings support ssRAB as a reliable diagnostic approach across a range of clinical scenarios.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
MT34
Topic
Medical Technologies
Disease
SDC: Oncology, SDC: Respiratory-Related Disorders (Allergy, Asthma, Smoking, Other Respiratory)