Breast Magnetic Resonance Imaging for Preoperative Evaluation of Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Preoperative breast magnetic resonance imaging (MRI) is known to detect additional cancers that are occult on mammography and ultrasound. There is debate as to whether these additional lesions affect clinical outcomes. The objective of this systematic review was to summarize the evidence on whether additional information on disease extent obtained with preoperative breast MRI in patients with newly diagnosed breast cancer affects surgical management, rates of recurrence, survival, re-excision, and early detection of bilateral cancer. Methods: Embase, MEDLINE, and Cochrane Central Register of Controlled Trials were searched until January 2021 (partial update July 2022) for studies comparing outcomes with versus without pre-operative MRI. Included were both randomized controlled trials and other comparative studies provided MRI and control groups had equivalent disease and patient characteristics or methods such as multivariable analysis or propensity score matching were used to control potential confounders. Results: The search resulted in 26,399 citations, of which 8 randomized control trials, 1 prospective cohort study, and 42 retrospective studies met the inclusion criteria. Use of MRI resulted in decreased rates of reoperations (OR = 0.73, 95% CI = 0.63 to 0.85), re-excisions (OR = 0.63, 95% CI = 0.45 to 0.89), and recurrence (HR = 0.77, 95% CI = 0.65 to 0.90). Increased detection of synchronous contralateral breast cancers led to a reduction in metachronous contralateral breast cancer (HR = 0.71, 95% CI = 0.59 to 0.85). Hazard ratios for recurrence-free and overall survival were 0.77 (95% CI = 0.53 to 1.12) and 0.89 (95% CI = 0.74 to 1.07). Conclusion: This systematic review indicates substantial benefits of pre-operative breast MRI in decreasing reoperations and recurrence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".