Ultrasound-Based Microvascular Parameters for Classification of Anti-Angiogenic Tumor Treatment Response: A Scalable Preclinical Approach
Bibliographic record
Abstract
The utility of ultrasound imaging for evaluating anti-angiogenic treatment responses in cancer models is widely recognized. However, the best approach to combine ultrasound-based microvascular features remains unclear. This study presents initial steps towards a scalable preclinical platform to compare methods for classifying anti-angiogenic treatment response using ultrasound-based multiparametric microvascular imaging. Seven perfusion parameters from two- and three-dimentional power Doppler (PD) and two-dimensional contrast-enhanced ultrasound (CEUS) as well as tumor volume are fed into a multivariable logistic regression learning model to classify control (modeling resistant) and treated (modeling senstive) tumors engrafted on a chicken embryo assay. Classification results indicate that, in the cell-line model used here, PD imaging using singular value decomposition-based clutter filters are valuable for detecting anti-angiogenic treatment responses, whereas CEUS perfusion parameters are less informative for this purpose.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".