Anna L McGuire1,2, Renelle Myers3, Kyle Grant1,2, Stephen Lam3, John Yee1,2. 1. Department of Surgery, Division of Thoracic Surgery. 2. Vancouver Coastal Health Research Institute, Vancouver General Hospital, Vancouver, BC, Canada. 3. Department of Medicine, Division of Respiratory Medicine, Faculty of Medicine, University of British Columbia.
Abstract
BACKGROUND: Lung cancer screening with computed tomography chest is identifying peripheral pulmonary lesions (PPLs) suspicious for early-stage lung cancer at increasing rates. Radial-endobronchial ultrasound (R-EBUS) and electromagnetic navigation bronchoscopy (ENB) are 2 methods to sample PPLs to diagnose and treat early lung cancer. ENB has a higher operating financial cost, however, the rationale for its use is possible higher diagnostic accuracy versus R-EBUS. OBJECTIVE: The objective of this study was to determine the comparative diagnostic accuracy, sensitivity, and negative predictive value for R-EBUS and ENB in sampling PPLs. METHODS: A systematic review and meta-analysis were conducted. The Ovid Medline database was queried for original research reporting a diagnostic yield of R-EBUS or ENB for PPLs identified on computed tomography chest suspicious for malignancy. The I statistic assessed study heterogeneity. Random effects models produced pooled estimates of diagnostic accuracy and sensitivity for malignancy. Reasons for heterogeneity were explored with meta-regression. Publication bias and small study effects were assessed. RESULTS: A total of 41 studies involved 2988 lung nodules (R-EBUS 2102, ENB 886) in 3204 patients (R-EBUS 2097, ENB 1107). Overall sensitivity to detect cancer was 70.7% [95% confidence interval (CI): 67.2-74.0]; R-EBUS 70.5% (95% CI: 66.1-74.8), ENB 70.7% (95% CI: 64.7-76.8). Pooled overall diagnostic accuracy was 74.2% (95% CI: 71.0-77.3); R-EBUS 72.4% (95% CI: 68.7-76.1), ENB 76.4% (95% CI: 70.8-82.0). The localization modalities had comparative safety profiles of <2% complications. CONCLUSION: Both technologies have a high proportion of successful PPL localization with similar sensitivity for malignancy and accuracy. As such, both reasonable options for health care authorities to employ diagnostic algorithms.
BACKGROUND:Lung cancer screening with computed tomography chest is identifying peripheral pulmonary lesions (PPLs) suspicious for early-stage lung cancer at increasing rates. Radial-endobronchial ultrasound (R-EBUS) and electromagnetic navigation bronchoscopy (ENB) are 2 methods to sample PPLs to diagnose and treat early lung cancer. ENB has a higher operating financial cost, however, the rationale for its use is possible higher diagnostic accuracy versus R-EBUS. OBJECTIVE: The objective of this study was to determine the comparative diagnostic accuracy, sensitivity, and negative predictive value for R-EBUS and ENB in sampling PPLs. METHODS: A systematic review and meta-analysis were conducted. The Ovid Medline database was queried for original research reporting a diagnostic yield of R-EBUS or ENB for PPLs identified on computed tomography chest suspicious for malignancy. The I statistic assessed study heterogeneity. Random effects models produced pooled estimates of diagnostic accuracy and sensitivity for malignancy. Reasons for heterogeneity were explored with meta-regression. Publication bias and small study effects were assessed. RESULTS: A total of 41 studies involved 2988 lung nodules (R-EBUS 2102, ENB 886) in 3204 patients (R-EBUS 2097, ENB 1107). Overall sensitivity to detect cancer was 70.7% [95% confidence interval (CI): 67.2-74.0]; R-EBUS 70.5% (95% CI: 66.1-74.8), ENB 70.7% (95% CI: 64.7-76.8). Pooled overall diagnostic accuracy was 74.2% (95% CI: 71.0-77.3); R-EBUS 72.4% (95% CI: 68.7-76.1), ENB 76.4% (95% CI: 70.8-82.0). The localization modalities had comparative safety profiles of <2% complications. CONCLUSION: Both technologies have a high proportion of successful PPL localization with similar sensitivity for malignancy and accuracy. As such, both reasonable options for health care authorities to employ diagnostic algorithms.
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