Imaging Tumor Angiogenesis With Contrast Ultrasound and Microbubbles Targeted to α <sub>v</sub> β <sub>3</sub>
Bibliographic record
Abstract
BACKGROUND: Angiogenesis is a critical determinant of tumor growth and metastasis. We hypothesized that contrast-enhanced ultrasound (CEU) with microbubbles targeted to alpha(v)-integrins expressed on the neovascular endothelium could be used to image angiogenesis. METHODS AND RESULTS: Malignant gliomas were produced in 14 athymic rats by intracerebral implantation of U87MG human glioma cells. On day 14 or day 28 after implantation, CEU was performed with microbubbles targeted to alpha(v)beta3 by surface conjugation of echistatin. CEU perfusion imaging with nontargeted microbubbles was used to derive tumor microvascular blood volume and blood velocity. Vascular alpha(v)-integrin expression was assessed by immunohistochemistry, and microbubble adhesion was characterized by confocal microscopy. Mean tumor size increased markedly from 14 to 28 days (2+/-1 versus 35+/-14 mm2, P<0.001). Tumor blood volume increased by approximately 35% from day 14 to day 28, whereas microvascular blood velocity decreased, especially at the central portions of the tumors. On confocal microscopy, alpha(v)beta3-targeted but not control microbubbles were retained preferentially within the tumor microcirculation. CEU signal from alpha(v)beta3-targeted microbubbles in tumors increased significantly from 14 to 28 days (1.7+/-0.4 versus 3.3+/-1.0 relative units, P<0.05). CEU signal from alpha(v)beta3-targeted microbubbles was greatest at the periphery of tumors, where alpha(v)-integrin expression was most prominent, and correlated well with tumor microvascular blood volume (r=0.86). CONCLUSIONS: CEU with microbubbles targeted to alpha(v)beta3 can noninvasively detect early tumor angiogenesis. This technique, when coupled with changes in blood volume and velocity, may provide insights into the biology of tumor angiogenesis and be used for diagnostic applications.
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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) | 0.000 | 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".