Transesophageal endoscopic ultrasound in the diagnosis of the lung masses: a multicenter experience with fine-needle aspiration and fine-needle biopsy needles.
Bibliographic record
Abstract
BACKGROUND AND AIM: Intraparenchymal lung masses inaccessible through bronchoscopy or endobronchial ultrasound guidance pose a diagnostic challenge. Furthermore, some fragile or hypoxic patients may be poor candidates for transbronchial approaches. Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) offers a potential diagnostic approach to lung cancers adjacent to the esophagus. We aimed to evaluate the feasibility, accuracy, and safety of trans-esophageal EUS-FNA/FNB for tissue sampling of pulmonary nodules. METHODS: We retrospectively analyzed data from patients with pulmonary lesions who underwent EUS-FNA/FNB between March 2015 and August 2021 at eight Italian endoscopic referral centers. RESULTS: A total of 47 patients (36 male; mean age 64.47 ± 9.05 years) were included (22 EUS-FNAs and 25 EUS-FNBs). Overall diagnostic accuracy rate was 88.9% (76.3-96.2%). The sensitivity and diagnostic accuracy were superior for EUS FNB sampling versus EUS-FNA (100% vs. 78.73%); P = 0.05, and (100% vs. 78.57%); P = 0.05, respectively. Additionally, sample adequacy was superior for EUS-FNB sampling versus EUS-FNA (100% vs. 78.5%); P = 0.05. Multivariate logistic regression analysis for diagnostic accuracy showed nodule size at the cutoff of 15 mm (OR 2.29, 1.04-5.5, P = 0.05) and use of FNB needle (OR 4.33, 1.05-6.31, P = 0.05) as significant predictors of higher diagnostic accuracy. There were no procedure-related adverse events. CONCLUSION: This study highlights the efficacy and safety of EUS-FNA/FNB as a minimally invasive procedure for diagnosing and staging peri-esophageal parenchymal lung lesions. The diagnostic yield of EUS-FNB was superior to EUS-FNA.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".