Low-frequency ultrasound radiosensitization and therapy response monitoring of tumors: An in vivo study
Bibliographic record
Abstract
A new framework has been introduced in this paper for tumor radiosensitization and therapy response monitoring using low-frequency ultrasound. Human fibrosarcoma xenografts grown in severe combined immunodeficiency (SCID) mice (n = 108) were treated using ultrasound-stimulated microbubbles at various concentration and exposed to different doses of radiation. Low-frequency ultrasound radiofrequency (RF) data were acquired from tumors prior to and at different times after treatment. Quantitative ultrasound (QUS) techniques were applied to generate spectral parametric maps of tumors. Textural analysis were performed to quantify spatial heterogeneities within QUS parametric maps. A hybrid model was developed using multiple regression analysis to predict extent of histological tumor cell death non-invasively based on QUS spectral and textural biomarkers. Results of immunohistochemistry on excised tumor sections demonstrated increases in cell death with higher concentration of microbubbles and radiation dose. Quantitative ultrasound results indicated changes that paralleled increases in histological cell death. Specifically, the hybrid QUS biomarker demonstrated a good correlation with extent of tumor cell death observed from immunohistochemistry. A linear discriminant analysis applied in conjunction with the receiver operating characteristic (ROC) curve analysis indicated that the hybrid QUS biomarker can classify tumor cell death fractions with an area under the curve of 91.2. The results obtained in this research suggest that low-frequency ultrasound can concurrently be used to enhance radiation therapy and evaluate tumor response to treatment.
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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 | 0.000 |
| 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) | 0.000 | 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".