The effect of acute aromatase inhibition on breast parenchymal enhancement in magnetic resonance imaging
Bibliographic record
Abstract
OBJECTIVE: The breast is highly hormonally sensitive especially to the sex steroid hormone estrogen. Both physiological and iatrogenic steroid hormone modifications could affect how the breast tissue may appear in breast imaging techniques. We hypothesized that estrogen deprivation therapy could reduce breast nonspecific enhancement on magnetic resonance imaging (MRI). METHODS: This study was a prospective pilot phase II clinical trial. The study was approved by Health Canada and the institutional research ethics board, and participants signed informed consent forms. Sixteen healthy postmenopausal women were enrolled, and 14 completed the study. Baseline breast MRI was done followed 1 month later by administration of a high-dose aromatase inhibitor (letrozole 12.5 mg/day) for 3 successive days before a second breast MRI. Background breast parenchymal enhancement was compared between the pretreatment and posttreatment studies. RESULTS: There was a statistically significant reduction of the average background breast enhancement after treatment with aromatase inhibitors compared with baseline MRI. Of particular interest, specific areas of benign breast enhancement were reduced after aromatase inhibitor treatment. No significant adverse effects were recorded using this relatively high dose of the aromatase inhibitors. CONCLUSIONS: This preliminary study provided evidence that aromatase inhibitors could reduce the parenchymal background enhancement of benign breast tissue during MRI and may improve the specificity of the technique.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".