Effectiveness of Screening With Annual Magnetic Resonance Imaging and Mammography: Results of the Initial Screen From the Ontario High Risk Breast Screening Program
Bibliographic record
Abstract
PURPOSE: The Ontario Breast Screening Program expanded in July 2011 to screen women age 30 to 69 years at high risk for breast cancer with annual magnetic resonance imaging (MRI) and digital mammography. To the best of our knowledge, this is the first organized screening program for women at high risk for breast cancer. PATIENTS AND METHODS: Performance measures after assessment were compared with screening results for 2,207 women with initial screening examinations. The following criteria were used to determine eligibility: known mutation in BRCA1, BRCA2, or other gene predisposing to a markedly increased risk of breast cancer, untested first-degree relative of a gene mutation carrier, family history consistent with hereditary breast cancer syndrome and estimated personal lifetime breast cancer risk ≥ 25%, or radiation therapy to the chest (before age 30 years and at least 8 years previously). RESULTS: The recall rate was significantly higher among women who had abnormal MRI alone (15.1%; 95% CI, 13.8% to 16.4%) compared with mammogram alone (6.4%; 95% CI, 5.5% to 7.3%). Of the 35 breast cancers detected (16.3 per 1,000; 95% CI, 11.2 to 22.2), none were detected by mammogram alone, 23 (65.7%) were detected by MRI alone (10.7 per 1,000; 95% CI, 6.7 to 15.8), and 25 (71%) were detected among women who were known gene mutation carriers (30.8 per 1,000, 95% CI, 19.4 to 43.7). The positive predictive value was highest for detection based on mammogram and MRI (12.4%; 95% CI, 7.3% to 19.3%). CONCLUSION: Screening with annual MRI combined with mammography has the potential to be effectively implemented into an organized breast screening program for women at high risk for breast cancer. This could be considered an important management option for known BRCA gene mutation carriers.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".