MRI surveillance for women with dense breasts and a previous breast cancer and/or high risk lesion
Bibliographic record
Abstract
BACKGROUND: The role of surveillance breast MRI for women with mammographically dense breasts, a personal history of breast cancer (BC), atypical hyperplasia (AH), or lobular carcinoma in situ (LCIS) is unclear. We estimated the performance of annual surveillance MRI in women with a combination of these risk factors. METHODS: We performed a retrospective review of the clinical, radiological, and pathological parameters of women who received annual concurrent surveillance breast MRI and mammography between 04/2013 and 12/2015 and fulfilled all of the following criteria: 1) age <70; 2) prior diagnosis of AH, LCIS or BC; 3) heterogeneously or extremely dense breast(s); and 4) did not qualify for our provincial breast MRI high risk screening program. RESULTS: This study included 198 patients (266 MRI exams). MRI detected 15 cancers: 11 invasive stage I and 4 in-situ. All but 1 were mammographically occult and there were no interval cancers. The cancer detection rate (CDR) and false positive (FP) rate were 6.1% and 21% for round one and 4.7% and 12.5% for round two, respectively. Not being on anti-estrogen therapy and having a 1st degree relative with BC significantly increased the likelihood of tumor detection. CONCLUSIONS: The CDR and FP rate of surveillance MRI in this study were comparable to those reported for women with BRCA mutations. The addition of annual MRI to mammography should be considered for surveillance of women with a combination of these risk factors, particularly if they have a family history of BC and are not on anti-estrogen therapy.
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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 | 0.001 |
| 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) | 0.001 | 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".