Conventional Frequency Ultrasonic Biomarkers of Cancer Treatment Response In Vivo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Conventional frequency quantitative ultrasound in conjunction with textural analysis techniques was investigated to monitor noninvasively the effects of cancer therapies in an in vivo preclinical model. METHODS: Conventional low-frequency (∼7 MHz) and high-frequency (∼20 MHz) ultrasound was used with spectral analysis, coupled with textural analysis on spectral parametric maps, obtained from xenograft tumor-bearing animals (n = 20) treated with chemotherapy to extract noninvasive biomarkers of treatment response. RESULTS: Results indicated statistically significant differences in quantitative ultrasound-based biomarkers in both low- and high-frequency ranges between untreated and treated tumors 12 to 24 hours after treatment. Results of regression analysis indicated a high level of correlation between quantitative ultrasound-based biomarkers and tumor cell death estimates from histologic analysis. Applying textural characterization to the spectral parametric maps resulted in an even stronger correlation (r (2) = 0.97). CONCLUSION: The results obtained in this research demonstrate that quantitative ultrasound at a clinically relevant frequency can monitor tissue changes in vivo in response to cancer treatment administration. Using higher order textural information extracted from quantitative ultrasound spectral parametric maps provides more information at a high sensitivity related to tumor cell death.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.004 | 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 it