Influence of post-treatment delay on the evaluation of the response to focused ultrasound surgery of breast cancer by dynamic contrast enhanced MRI
Bibliographic record
Abstract
The assessment of the effectiveness of MRI-guided focused ultrasound surgery (MRIgFUS) of breast carcinomas can be performed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters which monitor the presence of residual tumour. The aim of this study was to evaluate the effect of the post-treatment delay on this assessment. DCE-MRI data were acquired immediately and 3-14 days after MRIgFUS treatment of 26 tumours (<7 days, n = 6; = or > ge;7 days, n = 20). The percentage of residual tumour was determined histologically on the resected mass and correlated with two DCE-MRI parameters: increase in signal intensity (ISI) and positive enhancement integral (PEI). No correlation could be found between DCE-MRI data acquired immediately after treatment and the percentage of residual tumour. Good correlation coefficients were found for data acquired several days after treatment (ISI, r = 0.749; PEI, r = 0.778). However, they were higher when the post-treatment time interval was 7 days or more (ISI, r = 0.962; PEI, r = 0.934). These results suggest that a post-treatment delay of 7 days is necessary for the accurate assessment of the presence of residual tumour by DCE-MRI parameters.
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 | 0.006 |
| 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".