The Diagnostic Accuracy and Sensitivity for Malignancy of Radial-Endobronchial Ultrasound and Electromagnetic Navigation Bronchoscopy for Sampling of Peripheral Pulmonary Lesions
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.035 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".