In-Patient Repeatability and Sensitivity Study of Multi-Plane Super-Resolution Ultrasound in Breast Cancer
Bibliographic record
Abstract
Abstract Purpose Super-resolution ultrasound (SRUS) is a promising imaging modality for detecting early microvascular changes after cancer treatment, offering advantages over tumour-size methods to evaluate response. For clinical application, it is crucial to assess repeatability of SRUS-derived biomarkers and their sensitivity to post-treatment changes. Experimental Design Clinical data were collected from breast cancer patients undergoing radiotherapy. 24 repeatability scans were conducted, and 11 participants underwent SRUS response assessment at 2-weeks and 6-months post-radiotherapy. Ultrafast CEUS acquisitions sampled four imaging planes of each tumour, generating 2D SRUS maps of microvascular structure and dynamics. SRUS-derived quantitative parameters were extracted, with repeatability assessed using the Repeatability Coefficient (RC). Changes in quantitative parameters were analysed post-radiotherapy, and the RC defined significant changes. SRUS-derived quantitative parameters were compared to histopathological CD31 staining of biopsy samples. Results The RCs of SRUS quantitative parameters improved when averaged over more imaging planes, indicating improved repeatability. Significant changes in SRUS quantitative parameters were observed at 2-weeks post-RT in 5/11 participants. In contrast, only 1/11 participants showed significant tumour size changes. By 2-weeks or 6-months post-RT, significant changes in SRUS quantitative parameter were detected in all participants, while significant changes in tumour size were observed in 6/11 participants. Among 10 participants with corresponding CD31 vessel counts, 7 showed a correlation between the direction of change in histopathological vessel count scores and SRUS vessel density. Conclusions This repeatability and response assessment study establishes multi-plane SRUS as a robust and sensitive tool for detecting early tumour microvascular changes in patients undergoing treatment. Funding CRUK Convergence Science Centre, Kortuc Inc., NHS, NIHR, ICiC, IAA.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".