Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture
Bibliographic record
Abstract
Early alterations in textural characteristics of quantitative ultrasound spectral parametric maps, in conjunction with changes in their mean values, are demonstrated here, for the first time, to be capable of predicting ultimate clinical/pathologic responses of breast cancer patients to chemotherapy. Mechanisms of cell death, induced by chemotherapy within tumor, introduce morphological alterations in cancerous cells, resulting in measurable changes in tissue echogenicity. We have demonstrated that the development of such changes is reflected in early alterations in textural characteristics of quantitative ultrasound spectral parametric maps, followed by consequent changes in their mean values. The spectral/textural biomarkers derived on this basis have been demonstrated as non-invasive surrogates of breast cancer chemotherapy response. Particularly, spectral biomarkers sensitive to the size and concentration of acoustic scatterers could predict treatment response of patients with up to 80% of sensitivity and specificity (p=0.050), after one week within 3-4 months of chemotherapy. However, textural biomarkers characterizing heterogeneities in distribution of acoustic scatterers, could differentiate between treatment responding and non-responding patients with up to 100% sensitivity and 93% specificity (p=0.002). Such early prediction permits offering effective alternatives to standard treatment, or switching to a salvage therapy, for refractory patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".