Quantitative Ex-Vivo Micro-Computed Tomographic Imaging of Blood Vessels and Necrotic Regions within Tumors
Bibliographic record
Abstract
Techniques for visualizing and quantifying the microvasculature of tumors are essential not only for studying angiogenic processes but also for monitoring the effects of anti-angiogenic treatments. Given the relatively limited information that can be gleaned from conventional 2-D histological analyses, there has been considerable interest in methods that enable the 3-D assessment of the vasculature. To this end, we employed a polymerizing intravascular contrast medium (Microfil) and micro-computed tomography (micro-CT) in combination with a maximal spheres direct 3-D analysis method to visualize and quantify ex-vivo vessel structural features, and to define regions of hypoperfusion within tumors that would be indicative of necrosis. Employing these techniques we quantified the effects of a vascular disrupting agent on the tumor vasculature. The methods described herein for quantifying whole tumor vascularity represent a significant advance in the 3-D study of tumor angiogenesis and evaluation of novel therapeutics, and will also find potential application in other fields where quantification of blood vessel structure and necrosis are important outcome parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".