Bibliographic record
Abstract
Tumor angiogenesis has significant implications in the diagnosis and treatment of various solid tumors. With the advent of fast, multi-slice CT scanners, CT imaging techniques capable of qualitative and quantitative analysis of tumor angiogenesis have been the subject of extensive investigation in the past 2 decades. The fundamental bases for CT imaging of angiogenesis are both the transport by blood flow of intravenously administered iodinated contrast material to tissue and the exchange by diffusion of these contrast molecules between the intravascular space and the extravascular interstitial space. With current fast CT scanners both tissue and vascular enhancement can be measured and traced over time at small time intervals to allow detailed modeling of the distribution of contrast agent in tissue. Both compartmental and distributed parameter models for contrast transport and exchange have been developed to quantify from the CT data the following angiogenesis related parameters: tissue blood flow, blood volume, mean transit time, contrast arrival time, capillary permeability surface area product and hepatic arterial fraction in case of the liver. This review addresses the following aspects of CT imaging of angiogenesis: 1) basic concepts related to the understanding of both compartmental and distributed parameter models; 2) comparison between both types of models; 3) practical issues with respect to the measurement of the arterial input function, which is required for the solution of both types of models; and, 4) illustration of the application of a distributed parameter model, the Johnson and Wilson model, in a number of experimental studies.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".