Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy
Bibliographic record
Abstract
PURPOSE: Quantitative ultrasound techniques have been recently shown to be capable of detecting cell death through studies conducted on in vitro and in vivo models. This study investigates for the first time the potential of early detection of tumor cell death in response to clinical cancer therapy administration in patients using quantitative ultrasound spectroscopic methods. EXPERIMENTAL DESIGN: Patients (n = 24) with locally advanced breast cancer received neoadjuvant chemotherapy treatments. Ultrasound data were collected before treatment onset and at 4 times during treatment (weeks 1, 4, and 8, and preoperatively). Quantitative ultrasound parameters were evaluated for clinically responsive and nonresponding patients. RESULTS: Results indicated that quantitative ultrasound parameters showed significant changes for patients who responded to treatment, and no similar alteration was observed in treatment-refractory patients. Such differences between clinically and pathologically determined responding and nonresponding patients were statistically significant (P < 0.05) after 4 weeks of chemotherapy. Responding patients showed changes in parameters related to cell death with, on average, an increase in mid-band fit and 0-MHz intercept of 9.1 ± 1.2 dBr and 8.9 ± 1.9 dBr, respectively, whereas spectral slope was invariant. Linear discriminant analysis revealed a sensitivity of 100% and a specificity of 83.3% for distinguishing nonresponding patients by the fourth week into a course of chemotherapy lasting several months. CONCLUSION: This study reports for the first time that quantitative ultrasound spectroscopic methods can be applied clinically to evaluate cancer treatment responses noninvasively. The results form a basis for monitoring chemotherapy effects and facilitating the personalization of cancer 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 | 0.000 | 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) | 0.001 | 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".