Assessment of tumor angiogenesis: dynamic contrast‐enhanced MRI with paramagnetic nanoparticles compared with Gd‐DTPA in a rabbit Vx‐2 tumor model
Bibliographic record
Abstract
The purpose of this study was to evaluate the suitability of a macromolecular MRI contrast agent (paramagnetic nanoparticles, PNs) for the characterization of tumor angiogenesis. Our aim was to estimate the permeability of PNs in developing tumor vasculature and compare it with that of a low molecular weight contrast agent (Gd-DTPA) using dynamic contrast-enhanced MRI (DCE). Male New Zealand white rabbits (n = 5) underwent DCE MRI 12-14 days after Vx-2 tumor fragments were implanted into the left hind limb. Each contrast agent (PNs followed by Gd-DTPA) was evaluated using a DCE protocol and transendothelial transfer coefficient (K(i)) maps were calculated using a two-compartment model. Two regions of interest (ROIs) were located within the tumor core and hindlimb muscle and five ROIs were placed within the tumor rim. Comparisons were performed using repeated measures analysis of variance (ANOVA). The K(i) values estimated using PNs were significantly lower than those obtained for Gd-DTPA (p = 0.018). When PNs and Gd-DTPA data were analyzed separately, significant differences were identified among tumor rim ROIs for PNs (p < 0.0001), but not for Gd-DTPA data (p = 0.34). The mean K(i) for the tumor rim was significantly greater than that of either the core or the hindlimb muscle for both contrast agents (p < 0.05 for each comparison). In summary, the extravasation of Gd-DTPA was far greater than that of PNs, suggesting that PNs can reveal regional differences in tumor vascular permeability that are not otherwise apparent with clinical contrast agents such as Gd-DTPA. These results suggest that PNs show potential for the noninvasive delineation of tumor angiogenesis.
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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) | 0.000 | 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 | 0.001 |
| 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 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".