Conventional frequency evaluation of tumor cell death response in locally advanced breast cancer patients to chemotherapy treatment administration.
Bibliographic record
Abstract
The aim of many cancer therapies is to induce cell death within a target tumor. A substantial body of research using in vitro and in vivo models has demonstrated that cell death can be detected via quantitative ultrasound techniques. This study investigates for the first time the potential to quantify tumor responses to therapy in patients, using spectral and signal envelope statistics analysis of ultrasound data. A clinical study was undertaken investigating the efficacy of ultrasound to quantify cell death in tumor responses with cancer treatment. Patients (n=20) with locally advanced breast cancer received anthracyline and taxane-based chemotherapy treatments. Data collection consisted of acquiring tumor images and radiofrequency data prior to treatment onset and at 4 times during treatment (weeks 1, 4, and 8, and preoperatively). Data indicate increases of approximately 9 dBr (±1.67) in ultrasound backscatter in patients who responded to treatment. Patients assessed as responding poorly demonstrated significantly lower increases (2.3 ± 1.7 dBr). Increases in 0-MHz intercept followed similar trends, while increases in spectral slope were observed from tumor regions demonstrating increases in tissue echogenicity. This study demonstrates the potential of ultrasound to quantify changes in tumors in response to cancer treatment administration in a clinical setting.
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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".