Conventional frequency ultrasound detection of tumor response in vivo to cancer treatment administration
Bibliographic record
Abstract
Current methods employed to evaluate patient response to cancer therapy are typically invasive requiring examination of excised tissue. The development of a non-invasive method of monitoring patient response to cancer therapy administration would potentiate clinical decisions permitting clinicians to adjust therapy regimens early in a treatment course based upon individual patient responses. It has been previously demonstrated that high frequency ultrasound is capable of reliably quantifying structural changes in tumor morphology in response to cancer therapies. Preliminary work has also indicated that ultrasound employed at clinically relevant frequencies (1-15 MHz) can detect apoptotic cell death using in vitro models. This thesis examines changes in tumor morphology in response to cancer therapy administration employing ultrasound at a clinically applicable frequency in a preclinical in vivo mouse model. The power spectrum of the radiofrequency data obtained from tumors was analyzed via linear regression spectroscopic analysis, as well as evaluating a statistical analysis of the amplitude distribution of the signal envelope. It is demonstrated here for the first time that 7 MHz ultrasound can detect apoptotic and other forms of cell death in vivo. A potential for a parametric imaging technique to visually represent analysis results is also demonstrated.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".