Assessment of vessel size by MRI in an orthotopic model of human pancreatic cancer
Bibliographic record
Abstract
Pancreatic cancer is a devastating disease with no cure. Therapies that target the tumor vasculature are promising new treatment strategies. Magnetic resonance imaging (MRI) can non-invasively determine a vessel size index and a blood volume fraction to characterize the vascular compartment in a tumor. The changes in the T2 and T2* relaxation rate constants after the administration of superparamagnetic iron oxide (SPIO) particles are dependent on the size and morphology of tissue blood vessels. In this study, MRI was used to investigate changes in the tumor vasculature in an orthotopic primary human pancreatic cancer xenograft model during tumor progression. The SPIO contrast agent Feridex I.V. was first validated as an intravascular contrast agent over the course of the imaging session, and shown to remain in the blood for at least 1.5 h. The average vessel size index was not correlated to the tumor area within an image slice, but the average blood volume fraction was significantly and negatively correlated to the tumor area (p<0.05). Blood volume fraction may serve as a non-invasive biomarker for changes in the tumor vasculature due to tumor growth Further investigation is needed to evaluate this promising technique as a tool to monitor tumor vascular changes in response to antiangiogenic therapies in pancreatic cancer.
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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 | 0.001 |
| 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".