Multi-Modal Imaging in a Mouse Model of Orthotopic Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Investigation of CF800, a novel PEGylated nano-liposomal imaging agent containing indocyanine green (ICG) and iohexol, for real-time near infrared (NIR) fluorescence and computed tomography (CT) image-guided surgery in an orthotopic lung cancer model in nude mice. METHODS: CF800 was intravenously administered into 13 mice bearing the H460 orthotopic human lung cancer. At 48 h post-injection (peak imaging agent accumulation time point), ex vivo NIR and CT imaging was performed. A clinical NIR imaging system (SPY®, Novadaq) was used to measure fluorescence intensity of tumor and lung. Tumor-to-background-ratios (TBR) were calculated in inflated and deflated states. The mean Hounsfield unit (HU) of lung tumor was quantified using the CT data set and a semi-automated threshold-based method. Histological evaluation using H&E, the macrophage marker F4/80 and the endothelial cell marker CD31, was performed, and compared to the liposomal fluorescence signal obtained from adjacent tissue sections. RESULTS: The fluorescence TBR measured when the lung is in the inflated state (2.0 ± 0.58) was significantly greater than in the deflated state (1.42 ± 0.380 (n = 7, p<0.003). Mean fluorescent signal in tumor was highly variable across samples, (49.0 ± 18.8 AU). CT image analysis revealed greater contrast enhancement in lung tumors (a mean increase of 110 ± 57 HU) when CF800 is administered compared to the no contrast enhanced tumors (p = 0.0002). CONCLUSION: Preliminary data suggests that the high fluorescence TBR and CT tumor contrast enhancement provided by CF800 may have clinical utility in localization of lung cancer during CT and NIR image-guided 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".