Transthoracic Computed Tomography–Guided Lung Nodule Biopsy: Comparison of Core Needle and Fine Needle Aspiration Techniques
Bibliographic record
Abstract
PURPOSE: To determine if there is a statistically significant difference in the computed tomography (CT)-guided trans-thoracic needle biopsy diagnostic rate, complication rate, and degree of pathologist confidence in diagnosis between core needle biopsy (CNB) and fine needle aspiration biopsy (FNAB). METHODS: A retrospective cohort design was used to compare the diagnostic biopsy rate, diagnostic confidence, and biopsy-related complications of pneumothorax, chest tube placement, pulmonary hemorrhage, hemoptysis, admission to hospital, and length of stay between 251 transthoracic needle biopsies obtained via CNB (126) or FNAB (125). Complication rates were assessed using imaging and clinical follow-up. Final diagnosis was confirmed via surgical pathology or clinical follow-up over a period of up to 10 years. RESULTS: CNB provided diagnostic samples in 91% and FNA in 80% of biopsies, which was statistically significant (P < .05). The sensitivities for CNB and FNAB were 89% (85 of 95) and 95% (84 of 88), respectively. The specificity of CNB was 100% (21 of 21) and for FNAB was 81% (2 of 11) with 2 false positives in the FNAB group. The differences in complication rate was not statistically significant for pneumothorax (50% vs 46%; determined by routine postbiopsy CT), chest tube (2% vs 4%), hemoptysis (4% vs 6%), and pulmonary hemorrhage (38% vs 47%) between FNAB and CNB, respectively. Seven patients requiring chest tube were admitted to hospital, 2 in the FNAB cohort for an average of 2.5 days and 5 in the CNB cohort for an average of 4.6 days. CONCLUSIONS: CNB provided more diagnostic samples with no statistical difference in complication rate.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".