Assessment of intratumor hypoxia by integrated 18F-FDG PET / perfusion CT in a liver tumor model
Bibliographic record
Abstract
OBJECTIVES: Hypoxia in solid tumors occurs when metabolic demands in tumor cells surpass the delivery of oxygenated blood. We hypothesize that the 18F-fluorodeoxyglucose (18F-FDG) metabolism and tumor blood flow mismatch would correlate with tumor hypoxia. METHODS: Liver perfusion computed tomography (CT) and 18F-FDG positron emission tomography (PET) imaging were performed in twelve rabbit livers implanted with VX2 carcinoma. Under CT guidance, a fiber optic probe was inserted into the tumor to measure the partial pressure of oxygen (pO2). Tumor blood flow (BF) and standardized uptake value (SUV) were measured to calculate flow-metabolism ratio (FMR). Tumor hypoxia was further identified using pimonidazole immunohistochemical staining. Pearson correlation analysis was performed to determine the correlation between the imaging parameters and pO2 and pimonidazole staining. RESULTS: Weak correlations were found between blood volume (BV) and pO2 level (r = 0.425, P = 0.004), SUV and pO2 (r = -0.394, P = 0.007), FMR and pimonidazole staining score (r = -0.388, P = 0.031). However, there was stronger correlation between tumor FMR and pO2 level (r = 0.557, P < 0.001). CONCLUSIONS: FMR correlated with tumor oxygenation and pimonidazole staining suggesting it may be a potential hypoxic imaging marker in 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 | 0.000 | 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 | 0.000 | 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".