Imaging-guided Percutaneous Biopsy of Nodules ≤1 cm
Bibliographic record
Abstract
OBJECTIVE: Percutaneous biopsy of lung nodules is established as a safe procedure with high diagnostic yield and accuracy. Its role in the diagnosis of subcentimeter nodules is, however, less clear. The goal of this study was to evaluate diagnostic yield, accuracy, and safety of computed tomography (CT)-guided needle biopsy in the diagnosis of subcentimeter lung nodules. MATERIAL AND METHODS: A retrospective review of a prospectively maintained database over a 12-year period identified 133 eligible CT-guided needle biopsies of lesions ≤1 cm. Diagnostic yield and accuracy for the diagnosis of malignancy were calculated. Lesion features and procedure characteristics were assessed using univariate and multivariate logistic regression analysis to identify risk factors associated with biopsy failure and complications. RESULTS: Biopsy specimens were adequate for diagnosis in 116/133(87%) cases; the diagnostic yield for malignant and benign lesions was 93% and 65%, respectively. Final benign diagnosis was the strongest independent risk factor for biopsy failure. In multivariate logistic regression, fine-needle aspiration was an independent risk factor for diagnostic failure. Core needle biopsy was an independent risk factor for pneumothorax, and core needle biopsy, number of passes, and age were independent risk factors for pneumothorax requiring tube drainage. CONCLUSIONS: CT-guided percutaneous needle biopsy had high diagnostic yield for the diagnosis of subcentimeter lung nodules with a similar complication rate to biopsy of larger lesions. Fine-needle aspiration may be an independent factor for diagnostic failure even for malignant lesions.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".