Correlation between Hepatic Tumor Blood Flow and Glucose Utilization in a Rabbit Liver Tumor Model
Bibliographic record
Abstract
PURPOSE: To prospectively determine the relationship between hepatic tumor blood flow and glucose utilization in vivo by using a combined positron emission tomographic (PET)/computed tomographic (CT) scanner. MATERIALS AND METHODS: The animal care and use subcommittee at the University of Western Ontario approved this study. VX2 carcinoma cells were implanted in the livers of eight male New Zealand white rabbits. Functional CT was performed before tumor implantation and every 4 days thereafter. Each examination consisted of two phases: In the first phase, 30-second cine breath-hold scanning was performed with simultaneous injection of 5 mL of contrast material. In the second phase, 4-second cine scanning was performed without breath holding every 10 seconds for 2 minutes. Second-phase CT images were coregistered with first-phase images to eliminate breathing artifacts. The weighted summation of the aortic and portal venous time-attenuation curves was deconvolved against curves from the liver to derive hepatic blood flow (HBF). Five animals underwent fluorine 18 fluorodeoxyglucose (FDG) scanning before and every 8 days after implantation. FDG uptake was measured as standardized uptake value (SUV). Data were analyzed with repeated-measures analysis of variance and the Tukey-Kramer multiple comparison test. Linear regression was used to compare SUV and HBF in tumors and normal tissue. RESULTS: In the hypovascular tumor core, (a) mean HBF decreased from 262 mL.min-1.100 g-1+/-22 (standard deviation) at baseline to 101 mL.min-1.100 g-1+/-62 at the end of the study (P<.05) and (b) mean SUV increased from 2.12 g/mL+/-0.06 to 4.56 g/mL+/-0.73 (P<.05) during the same period. CONCLUSION: Functional CT in combination with FDG PET can be used to observe changes in HBF and glucose utilization in a growing liver tumor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".