The Impact of Preoperative Breast MRI on Timing of Surgical Management in Newly Diagnosed Breast Cancer
Bibliographic record
Abstract
Purpose: Preoperative breast MRI has been recommended at our center since 2016 for invasive lobular carcinoma and cancers in dense breasts. This study examined how preoperative breast MRI impacted surgical timing and outcomes for patients with newly diagnosed breast cancer. Methods: Retrospective single-center study of consecutive women diagnosed with new breast cancer between June 1, 2019, and March 1, 2021, in whom preoperative breast MRI was recommended. MRI, tumour histology, breast density, post-MRI biopsy, positive predictive value of biopsy (PPV3), surgery, and margin status were recorded. Time from diagnosis to surgery was compared using t-tests. Results: There were 1054 patients reviewed, and 356 were included (mean age 60.9). Of these, 44.4% (158/356) underwent preoperative breast MRI, and 55.6% (198/356) did not. MRI referral was more likely for invasive lobular carcinoma, multifocal disease, and younger patients. Following preoperative MRI, 29.1% (46/158) patients required additional breast biopsies before surgery, for a PPV3 of 37% (17/46). The time between biopsy and surgery was 55.8 ± 21.4 days for patients with the MRI, compared to 42.8 ± 20.3 days for those without ( P < .00001). MRI was not associated with the type of surgery (mastectomy vs breastconserving surgery) ( P = .44) or rate of positive surgical margins ( P = .52). Conclusion: Among patients who underwent preoperative breast MRI, we observed significant delays to surgery by almost 2 weeks. When preoperative MRI is requested, efforts should be made to mitigate associated delays.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".