Percutaneous transthoracic localization of pulmonary nodules under C-arm cone-beam CT virtual navigation guidance
Bibliographic record
Abstract
PURPOSE: We aimed to describe our initial experience with percutaneous transthoracic localization (PTL) of pulmonary nodules using a C-arm cone-beam CT (CBCT) virtual navigation guidance system. METHODS: From February 2013 to March 2014, 79 consecutive patients (mean age, 61±10 years) with 81 solid or ground-glass nodules (mean size, 12.36±7.21 mm; range, 4.8-25 mm) underwent PTLs prior to video-assisted thoracoscopic surgery (VATS) excision under CBCT virtual navigation guidance using lipiodol (mean volume, 0.18±0.04 mL). Their procedural details, radiation dose, and complication rates were described. RESULTS: All 81 target nodules were successfully localized within 10 mm (mean distance, 2.54±3.24 mm) from the lipiodol markings. Mean number of CT acquisitions was 3.2±0.7, total procedure time was 14.6±5.14 min, and estimated radiation exposure during the localization was 5.21±2.51 mSv. Postprocedural complications occurred in 14 cases (17.3%); complications were minimal pneumothorax (n=10, 12.3%), parenchymal hemorrhage (n=3, 3.7%), and a small amount of hemoptysis (n=1, 1.2%). All target nodules were completely resected; pathologic diagnosis included invasive adenocarcinoma (n=53), adenocarcinoma-in-situ (n=10), atypical adenomatous hyperplasia (n=4), metastasis (n=7), and benign lesions (n=7). CONCLUSION: PTL procedures can be performed safely and accurately under the guidance of a CBCT virtual navigation system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".