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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".