Bronchoscopically delivered microwave ablation in an<i>in vivo</i>porcine lung model
Bibliographic record
Abstract
Background Percutaneous microwave ablation is clinically used for inoperable lung tumour treatment. Delivery of microwave ablation applicators to tumour sites within lung parenchyma under virtual bronchoscopy guidance may enable ablation with reduced risk of pneumothorax, providing a minimally invasive treatment of early-stage tumours, which are increasingly detected with computed tomography (CT) screening. The objective of this study was to integrate a custom microwave ablation platform, incorporating a flexible applicator, with a clinically established virtual bronchoscopy guidance system, and to assess technical feasibility for safely creating localised thermal ablations in porcine lungsin vivo. Methods Pre-ablation CTs of normal pigs were acquired to create a virtual model of the lungs, including airways and significant blood vessels. Virtual bronchoscopy-guided microwave ablation procedures were performed with 24–32 W power (at the applicator distal tip) delivered for 5–10 mins. A total of eight ablations were performed in three pigs. Post-treatment CT images were acquired to assess the extent of damage and ablation zones were further evaluated with viability stains and histopathologic analysis. Results The flexible microwave applicators were delivered to ablation sites within lung parenchyma 5–24 mm from the airway wallviaa tunnel created under virtual bronchoscopy guidance. No pneumothorax or significant airway bleeding was observed. The ablation short axis observed on gross pathology ranged 16.5–23.5 mm and 14–26 mm on CT imaging. Conclusion We have demonstrated the technical feasibility for safely delivering microwave ablation in the lung parenchyma under virtual bronchoscopic guidance in anin vivoporcine lung model.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".