The value of breast <scp>MRI</scp> in high‐risk patients with newly diagnosed breast cancer to exclude invasive disease in the contralateral prophylactic mastectomy: Is there a role to choose wisely patients for sentinel node biopsy?
Bibliographic record
Abstract
The aim of this study was to evaluate the presence of clinically and mammographically occult disease using breast MRI in a cohort of cancer patients undergoing contralateral prophylactic mastectomy (CPM) and the utmost indication of axillary assessment (sentinel node biopsy (SLNB)) for this side. A retrospective review of patients with unilateral invasive breast cancer or ductal carcinoma in situ (DCIS) from institutional MRI registry data (2004-2010) was conducted. Characteristics of patients undergoing CPM with breast MRI obtained less than 6 month before surgery were evaluated. A total of 2322 consecutive patients diagnosed with DCIS or stage I to III infiltrating breast cancer underwent preoperative breast MRI. Of these, 1376 patients (59.2%) had contralateral clinical breast exam and mammography without abnormalities; and 116 patients (4.9%) underwent CPM (28 excluded patients had breast MRI more than 6 months before CPM). The mean age of the 88 patients was 49 years (range 28-76 years). Two (2.3%) DCIS identified on surgical pathology specimen were not depicted by MRI and the 5 mm T1N0 invasive cancer (1.1%) was identified on MRI. Preoperative MRI showed 95% accuracy to demonstrate absence of occult disease with negative predicted value (NPV) of 98% (95% CI: 91.64-99.64%). Occult disease was present in 3.4% of CPM. MRI accurately identified the case of invasive cancer in this cohort. The high negative predictive value suggests that MRI can be used to select patients without consideration of SLNB for the contralateral side.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".