Bibliographic record
Abstract
Angiogenesis, long recognized as a key factor in tumor growth and metastasis, has been the target of new anticancer treatment paradigms. Development of antiangiogenesis drugs is challenging, mainly due to the difficulty of determining the correct dosage and the time required to observe a clinical effect. In the past decade, imaging has shown potential to answer these questions and accelerate the drug development process by providing functional, morphological, and even molecular characterization. In this review, we describe existing challenges to modern drug development and the potential of imaging biomarkers to monitor drug bioactivity and establish early response of drug efficacy. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), particularly attractive for its non-invasiveness and high spatial resolution, has been useful for measuring properties of tumor microvasculature. The general methodology of DCE-MRI is described, in addition to measurable hemodynamic parameters compared to other imaging modalities. Experience with DCE-MRI in antiangiogenesis cancer therapy and results from correlative studies are examined. Current challenges for DCE-MRI, especially in relation to the required sensitivity and reproducibility, are highlighted. We conclude with an outlook on the future of DCE-MRI, including its role in the emerging field of imaging molecular markers of angiogenesis for target-specific therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".