From high-frequency to low-frequency cell death detection: quantitative ultrasound evaluation of tumor response in breast cancer
Bibliographic record
Abstract
Pre-clinical and clinical studies were undertaken investigating the efficacy of ultrasound to quantify cell death in tumor responses with cancer treatment. Animals bearing tumours (n=48), and patients (n=24) with locally advanced breast cancer received various therapies including for patients anthracyline and taxane-based chemotherapy treatments over four to six months. Tumour cell-death was assessed in specimens after treatment using histopathology. Pre-clinical ultrasound data collection was carried out at low-frequency and high-frequency. For human imaging, low-frequency ultrasound data were collected 5 times during neoadjuvant chemotherapy. Data indicated considerable increases in ultrasound backscatter in animal tumours after treatment. Similar findings were observed in patients who clinically responded to treatment. Patients assessed as responding poorly demonstrated significantly lower increases. Increases in 0-MHz intercept followed similar trends while increases in spectral slope were observed locally from tumor regions demonstrating increases in tissue echogenicity. This study demonstrates the potential of ultrasound to quantify changes in tumours in response to cancer treatment administration in a pre-clinical and clinical setting. The results indicate that such responses can be detected early during a course of chemotherapy in patients and should permit ineffective treatments to be changed to more efficacious ones potentially leading to improved treatment outcomes.
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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 | 0.001 |
| 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".