Quantitative ultrasound spectral parametric maps: Early surrogates of cancer treatment response
Bibliographic record
Abstract
Textural characteristics of quantitative ultrasound spectral parametric maps have been proposed for the first time to predict cancer therapy response, early following treatment initiation. Such an early prediction can facilitate personalized medicine in cancer treatment procedures. Patients (n=10) with locally advanced breast cancer received neo-adjuvant chemotherapy, as "up-front" treatment, followed by mastectomy with axillary nodal clearance. Data collection consisted of acquiring tumor ultrasound radio-frequency data prior to neo-adjuvant treatment onset and at 4 times during treatment, in addition to pathological examinations of resected specimens after mastectomy. Several textural features were extracted from parametric maps of mid-band fit and 0-MHz intercept. The relative changes of these features were calculated one week after the treatment commenced, compared to the pre-treatment scan. Statistical analysis performed suggested that five of the applied textural features exhibit statistically significant differences between clinically/pathologically responding and non-responding patients. The promising results obtained represent a substantial step forward towards customizing cancer therapies by using this quantitative imaging modality. This can facilitate the switch of an ineffective treatment for a specific patient to a salvage therapy within weeks, instead of having patient endures months of the ineffective 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".