Tumor microvascular changes in antiangiogenic treatment: Assessment by magnetic resonance contrast media of different molecular weights
Bibliographic record
Abstract
PURPOSE: To test magnetic resonance (MR) contrast media of different molecular weights (MWs) for their potential to characterize noninvasively microvascular changes in an experimental tumor treatment model. MATERIALS AND METHODS: MD-MBA-435, a poorly differentiated human breast cancer cell line, was implanted into 31 female homozygous athymic rats. Animals were assigned randomly to a control (saline) or drug treatment (monoclonal antibody vascular endothelial growth factor (Mab-VEGF) antibody) group. In both groups, dynamic MR imaging (MRI) was performed in each animal using up to three different contrast media on sequential days at baseline and follow-up examination. The MWs of the contrast media used ranged from 557 Da to 92 kDa. Using a bidirectional kinetic model, tumor microvessel characteristics, including the fractional plasma volume (fPV) and transendothelial permeability (K(PS)), were estimated for each contrast medium. These microvascular characteristics were compared between drug and control groups and between contrast media of different MWs. RESULTS: Tumors grew significantly slower (P < 0.0005) in the drug treatment group than in the control group. Mean K(PS) and fPV values decreased significantly (P < 0.05) in the Mab-VEGF antibody-treated group compared to baseline values using intermediate or macromolecular contrast media (MMCM), but did not change significantly using small molecular contrast media (SMCM). In the control groups, mean K(PS) and mean fPV values did not reach statistical significance for any of the contrast media used. CONCLUSION: Therapeutic effects of a Mab-VEGF antibody on tumor microvessel characteristics can be monitored by dynamic MRI. Intermediate-size agents, such as Gadomer-17, offer a substantial dynamic range and are less limited by imaging precision and therefore should be considered a practical alternative to monitor antiangiogenesis treatment effects in a clinical setting.
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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".