The diagnosis of lung cancer in the era of interventional pulmonology
Bibliographic record
Abstract
Advances in bronchoscopic and other interventional pulmonology technologies have expanded the sampling procedures pulmonologist can use to diagnose lung cancer and accurately stage the mediastinum. Among the modalities available to the interventional pulmonologist are endobronchial ultrasound-guided transbronchial needles aspiration (EBUS-TBNA) and transoesophageal bronchoscopic ultrasound-guided fine-needle aspiration (EUS-B-FNA) for sampling peribronchial/perioesophageal central lesions and for mediastinal lymph node staging, as well as navigational bronchoscopy and radial probe endobronchial ultrasound (RP-EBUS) for the diagnosis of peripheral lung cancer. The role of the interventional pulmonologist in this setting is to apply these procedures based on the correct interpretation of clinical and radiological findings in order to maximise the chances of achieving the diagnosis and obtaining sufficient tissue for molecular biomarker testing to guide targeted therapies for advanced non-small cell lung cancer. The safest and the highest diagnosis-yielding modality should be chosen to avoid a repeat sampling procedure if the first one is non-diagnostic. The choice of site and biopsy modality are influenced by tumour location, patient comorbidities, availability of equipment and local expertise. This review provides a concise state-of-the art account of the interventional pulmonology procedures in the diagnosis and staging of lung cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".