Performance Measures of Magnetic Resonance Imaging Plus Mammography in the High Risk Ontario Breast Screening Program
Bibliographic record
Abstract
BACKGROUND: The Ontario Breast Screening Program expanded in July 2011 to screen high-risk women age 30-69 years with annual magnetic resonance imaging (MRI) and digital mammography. This study examined the benefits of screening with mammography and MRI by age and risk criteria. METHODS: This prospective cohort study included 8782 women age 30-69 years referred to the High Risk Ontario Breast Screening Program from July 2011 to June 2015, with final results to December 2016. Cancer detection rates, sensitivity, and specificity of MRI and mammography combined were compared with each modality individually within risk groups stratified by age using generalized estimating equation models. Prognostic features of screen-detected breast cancers were compared by modality using Fisher exact test. All P values are two-sided. RESULTS: Among 20 053 screening episodes, there were 280 screen-detected breast cancers (cancer detection rate = 14.0 per 1000, 95% confidence interval [CI] = 12.4 to 15.7). The sensitivity of mammography was statistically significantly lower than that of MRI plus mammography (40.8%, 95% CI = 29.3% to 53.5% vs 96.0%, 95% CI = 92.2% to 98.0%, P < .001). In mutation carriers age 30-39 years, sensitivity of the combination was comparable with MRI alone (100.0% vs 96.8%, 95% CI = 79.2% to 100.0%, P = .99) but with statistically significantly decreased specificity (78.0%, 95% CI = 74.7% to 80.9% vs 86.2%, 95% CI = 83.5% to 88.5%, P < .001). In women age 50-69 years, combining MRI and mammography statistically significantly increased sensitivity compared with MRI alone (96.3%, 95% CI = 90.6% to 98.6% vs 90.9%, 95% CI = 83.6% to 95.1%, P = .02), with a small but statistically significant decrease in specificity (84.2%, 95% CI = 83.1% to 85.2% vs 90.0%, 95% CI = 89.2% to 90.9%, P < .001). CONCLUSIONS: Screening high risk women age 30-39 years with annual MRI only may be sufficient for cancer detection and should be evaluated further, particularly for mutation carriers. Among women age 50-69 years, detection is most effective when mammography is included with annual MRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".