FDG PET/CT Performed Prior to CT-Guided Percutaneous Biopsy of Lung Masses is Associated With an Increased Diagnostic Rate and Often Identifies Alternate Safer Sites to Biopsy
Bibliographic record
Abstract
Purpose: To determine the benefit of a FDG PET/CT scan prior to CT-guided lung biopsy on the rate of diagnosis, rate of complication, and the identification of potentially safer biopsy sites. Methods: This retrospective observational cross-sectional study evaluated consecutive adult patients who underwent CT-guided lung biopsy in 2020 or 2021 at 2 Canadian tertiary care hospitals. These patients were grouped into those that had PET/CT performed within 8 weeks prior to biopsy, within 8 weeks after biopsy, or no PET/CT scan within this time frame. Biopsy complication rates and pathology diagnostic rates were compared. The PET/CT images of those performed after biopsy were reviewed to determine if alternate safer biopsy sites could be identified. Categorical variables were compared using Pearson chi square test ( P < .05 significant). Results: 547 patients who had CT-guided lung biopsy were included. Patients with lung masses (≥3 cm) who had a PET/CT scan prior to biopsy had a higher diagnostic rate (90.8%) compared to those that did not (80.2%). The overall post-biopsy pneumothorax rate was 43.3% with 11.3% overall requiring chest tube insertion and 13.9% requiring hospitalization. There was no difference in complication rate for those who had PET/CT prior to biopsy and those that did not. 28.9% to 42.1% of patients who had PET/CT after biopsy had safer sites amenable to biopsy identified retrospectively outside of the lungs. Conclusion: PET/CT prior to CT-guided lung biopsy improves the diagnostic rate in 10.6% of patients with lung masses (≥3 cm) and identifies alternate safer sites to biopsy in 28.9% to 42.1% of patients (any size lesion).
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".