A Novel Minimally Invasive Technique to Create a Rabbit VX2 Lung Tumor Model for Nano-Sized Image Contrast and Interventional Studies
Bibliographic record
Abstract
BACKGROUND: The rabbit VX2 lung cancer model is a large animal model useful for preclinical lung cancer imaging and interventional studies. However, previously reported models had issues in terms of invasiveness of tumor inoculation, control of tumor aggressiveness and incidence of complications. PURPOSE: We aimed to develop a minimally invasive rabbit VX2 lung cancer model suitable for imaging and transbronchial interventional studies. METHODS: New Zealand white rabbits and VX2 tumors were used in the study. An ultra-thin bronchoscope was inserted through a miniature laryngeal mask airway into the bronchus. Different numbers of VX2 tumor cells were selectively inoculated into the lung parenchyma or subcarinal mediastinum to create a uniform tumor with low incidence of complications. The model was characterized by CT, FDG-PET, and endobronchial ultrasound (EBUS). Liposomal dual-modality contrast agent was used to evaluate liposome drug delivery system in this model. RESULTS: Both peripheral and mediastinal lung tumor models were created. The tumor making success rate was 75.8% (25/33) in the peripheral lung tumor model and 60% (3/5) in the mediastinal tumor model. The group of 1.0×10(6) of VX2 tumor cells inoculation showed a linear growth curve with less incidence of complications. Radial probe EBUS visualized the internal structure of the tumor and the size measurement correlated well with CT measurements (r(2) = 0.98). Over 7 days of continuous enhancement of the lung tumor by liposomal contrast in the lung tumor was confirmed both CT and fluorescence imaging. CONCLUSION: Our minimally invasive bronchoscopic rabbit VX2 lung cancer model is an ideal platform for lung cancer imaging and preclinical bronchoscopic interventional studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".