Effect of an Angiogenesis Inhibitor on Hepatic Tumor Perfusion and the Implications for Adjuvant Cytotoxic Therapy
Bibliographic record
Abstract
PURPOSE: To determine whether dynamic contrast material-enhanced (DCE) computed tomography (CT) can help identify hepatic tumor perfusion response to vascular remodeling induced by antiangiogenesis treatment in a rabbit model. MATERIALS AND METHODS: The study was approved by the Animal Use Subcommittee of the University Council on Animal Care. DCE CT hepatic perfusion measurements were performed in the livers of 20 rabbits implanted with VX2 carcinoma. Vascular remodeling was induced with thalidomide dissolved in dimethyl sulfoxide and sterile water, starting at a tumor diameter of 0.7 cm±0.1 and continuing until metastatic lung nodules were observed. The control group (n=8) was given an equivalent volume of the vehicle. The therapy group was subdivided into animals that survived for more than 24 days without lung metastasis (responder group, n=5) or those that survived for less than 24 days (nonresponder group, n=7). Data were analyzed with the Kruskal-Wallis or Friedman rank test and reported as medians and interquartile ranges. RESULTS: DCE CT depicted differential perfusion change within the therapy group after treatment. By day 4, hepatic blood volume (HBV) in the responder group decreased by 29.2% (-32.5% to -11.8%) relative to that before treatment and was significantly different from that in the nonresponder (P=.048) and control (P=.011) groups, where HBV remained stable. By day 8, hepatic artery blood flow decreased by 50.0% (-59.08% to -21.05%) relative to that before treatment in the responder group and was significantly different from that in the nonresponder and control groups (P=.030 for both), which remained stable at -3.5% (-8.5% to 28.7%, P=.50) and -10.0% (-33.8% to 10.4%, P=.48), respectively. CONCLUSION: DCE CT can help differentiate responders from nonresponders by their early differential perfusion response to antiangiogenesis therapy.
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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".