Advances in quantitative ultrasound and applications to breast cancer treatment
Bibliographic record
Abstract
Quantitative ultrasound (QUS) has demonstrated the ability to characterize tissues for the purposes of diagnosis, monitoring disease progression, and identifying therapeutic response. In recent work, we have focused on implementing novel QUS technologies to improve upon traditional methods for estimating scattering properties from the backscatter coefficient (BSC), i.e., spectral-based QUS. The goal of this research was to demonstrate that QUS techniques could provide biomarkers of early response of breast cancer to neoadjuvant systemic chemotherapy (NST). Specifically, we have developed an in situ reference method for estimating the BSC whereby a small metallic bead is inserted into patients with locally advanced breast cancer prior to the onset of NST. The bead provides a calibration target within the tissue that accounts for overlying attenuation and transmission losses resulting in more consistent estimates of BSC-based estimates at different time points during therapy. Similarly, we also integrated QUS techniques onto a breast tomography scanner, the QT Breast Acoustic CT platform. The QT tomography scanner provides estimates of attenuation and sound speed and provides compounding of QUS estimates from a full 360°. These technologies were used to identify the response or lack of response of breast cancer to NST early during the course of 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".