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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".