Assessment of Acute Antivascular Effects of Vandetanib with High-Resolution Dynamic Contrast-Enhanced Computed Tomographic Imaging in a Human Colon Tumor Xenograft Model in the Nude Rat
Bibliographic record
Abstract
Tumor size is not a reliable marker for the assessment of early antivascular effects of antiangiogenics. In the present study, we used 200-microm in-plane high-resolution dynamic contrast-enhanced computed tomography (DCE-CT) to noninvasively assess the immediate antivascular effects of vandetanib in a subcutaneous human colon cancer (LoVo) xenograft model in nude rats and to investigate correlation between changes in CT perfusion parameters and tumor volume or immunohistochemical end points. At 3 to 4 weeks after LoVo cell implantation, the animal was gavaged with either vandetanib (50 mg/kg) or vehicle twice (22 hours apart) and scanned with a preclinical DCE-CT scanner before (0 hour) and after treatment (24 hours). Quantitative maps of blood flow (BF) and volume (BV) of the tumor were calculated from the acquired DCE-CT images. The rats were divided into nonhypovascular, hypovascular, and combined (regardless of vascularity) groups. In the nonhypovascular group, significant decreases in both tumor BF and BV were observed in the vandetanib-treated rats compared with increases in the vehicle-treated rats. A significant decrease in BV was detected in the vandetanib-treated rats in the combined group as well. No differences in tumor growth, vascular endothelial growth factor expression, microvessel density, or apoptosis were observed between vandetanib- and vehicle-treated rats in all three groups. These results demonstrate that BF and BV imaging biomarkers from DCE-CT imaging can be used for rapid monitoring of immediate (24 hours after) antimicrovascular effects of vandetanib on tumors, even in the absence of significant changes of tumor volume or clinically relevant immunohistochemical end points.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".