Nanoparticle-based CT visualization of pulmonary vasculature for minimally-invasive thoracic surgery planning
Bibliographic record
Abstract
PURPOSE: To evaluate CF800, a novel lipid-based liposomal nanoparticle that co-encapsulates indocyanine green (ICG) and iohexol, for CT imaging of pulmonary vasculature in minimally-invasive thoracic surgery planning. METHODS: CF800 was intravenously administered to 7 healthy rabbits. In vivo CT imaging was performed 15 min post-injection, with a subset of animals imaged at 24h, 48h, and 72h post injection. Signal-to-background ratios (SBR) were calculated at the inferior vena cava and compared across time-points. A similar protocol was applied to 2 healthy pigs to evaluate the feasibility and efficacy in a large animal model. To evaluate the feasibility of clinical application, a survey was completed by 7 surgical trainees to assess pre- and post-injection CT images of rabbits and pigs. Responses on the discernibility of pulmonary vasculature sub-branches and comfort level to use the images for pre-operative planning were collected and analyzed. RESULTS: CF800 injection improved visualization of pulmonary vessels in both rabbit and pig models. The SBR of rabbit pulmonary vasculature was significantly higher after CF800 injection (range 3.7-4.4) compared to pre-injection (range 3.3-3.8, n = 7; p<0.05). SBR remained significantly different up to 24 hours after injection (range 3.7-4.3, n = 4; p<0.05). Trainees' evaluation found the post-injection CT images had significantly higher discernibility at the second vessel branch generation in both rabbit and pig models. Trainees identified smaller vasculature branch generations in the post-injection images compared to the pre-treatment images in both rabbit (mean 6.7±1.8 vs 5.4±2.1; p<0.05) and pig (mean 6.7±1.8 vs 5.4±2.1; p<0.05). Trainees were significantly more comfortable using post-injection images for surgical planning compared to the pre-injection images (rabbit: 8.1±1.1 vs. 4.7±2.1; pig: 7.6±2.1 vs. 4.9±2.2; p<0.05). CONCLUSION: CF800 provides SBR and contrast enhancement of pulmonary vasculature which may assist in pre-surgical CT planning of minimally invasive thoracic surgery.
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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 | 0.001 |
| 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".