Preoperative Breast Magnetic Resonance Imaging: Controversies Arising in the Quest to Evaluate Clinical Benefit
Bibliographic record
Abstract
Breast magnetic resonance imaging (MRI) is indisputably the highest sensitivity test available to detect breast cancer, revealing more extensive cancer in the ipsilateral and otherwise occult cancer in the contralateral breasts when used before surgery. The use of preoperative breast MRI has become somewhat controversial, because the clinical benefit of the heightened detection provided by MRI has been questioned in the context of multidisciplinary breast cancer treatment, relatively low local recurrence, and metachronous contralateral cancer rates. Also, MRI detection rates have been compared with the high rates reported in the pathology literature. The emerging clinical outcome literature is showing conflicting results to demonstrating actual overall benefit. Critical review of this literature reveals several misconceptions about MRI detection rates and limitations of many of the published outcome studies to date, which render the results not necessarily generalizable to contemporary optimized breast MRI practices. This article addresses some of the misconceptions raised by critics, provides a critical review of the clinical outcome literature, reviews patient subgroups anticipated to have the highest yield when using preoperative MRI, makes recommendations for optimizing breast MRI practice, and suggests areas for potential future research.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".