Functional Flow Patterns and Static Blood Pooling in Tumors Revealed by Combined Contrast-Enhanced Ultrasound and Photoacoustic Imaging
Bibliographic record
Abstract
Alterations in tumor perfusion and microenvironment have been shown to be associated with aggressive cancer phenotypes, raising the need for noninvasive methods of tracking these changes. Dynamic contrast-enhanced ultrasound (DCEUS) and photoacoustic (PA) imaging serve as promising candidates-one has the ability to measure tissue perfusion, whereas the other can be used to monitor tissue oxygenation and hemoglobin concentration. In this study, we investigated the relationship between the different functional parameters measured with DCEUS and PA imaging, using two morphologically different hind-limb tumor models and drug-induced alterations in an orthotopic breast tumor model. Imaging results showed some correlation between perfusion and oxygen saturation maps and the ability to sensitively monitor antivascular treatment. In addition, DCEUS measurements revealed different vascular densities in the core of specific tumors compared with their rims. Noncorrelated perfusion and hemoglobin concentration measurements facilitated discrimination between blood lakes and necrotic areas. Taken together, our results illustrate the utility of a combined contrast-enhanced ultrasound method with photoacoustic imaging to visualize blood flow patterns in tumors. Cancer Res; 76(15); 4320-31. ©2016 AACR.
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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 | 0.001 | 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) | 0.001 | 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".