Screening with magnetic resonance imaging, mammography and ultrasound in women at average and intermediate risk of breast cancer
Bibliographic record
Abstract
The addition of MRI to mammography and ultrasound for breast cancer screening has been shown to improve screening sensitivity for high risk women, but there is little data to date for women at average or intermediate risk. Two thousand nine hundred and ninety-five women, aged 40 to 65 years with no previous history of breast cancer were enrolled in a screening program, which consisted of two rounds of MRI, ultrasound and mammography, one year apart. Three hundred and fifty-six women had a CHEK2 mutation, 370 women had a first-degree relative with breast cancer (and no CHEK2 mutation) and 2269 women had neither risk factor. Subjects were followed for breast cancer for three years from the second screening examination. Twenty-seven invasive epithelial cancers, one angiosarcoma and six cases of DCIS were identified over the four-year period. Of the 27 invasive cancers, 20 were screen-detected, 2 were interval cancers, and five cancers were identified in the second or third follow-up year (i.e., after the end of the screening period). For invasive cancer, the sensitivity of MRI was 86%, the sensitivity of ultrasound was 59% and the sensitivity of mammography was 50%. The number of biopsies incurred by MRI (n = 156) was greater than the number incurred by mammography (n = 35) or ultrasound (n = 57). Of the 19 invasive cancers detected by MRI, 17 (89%) were also detected by ultrasound or mammography. In terms of sensitivity, MRI is slightly better than the combination of mammography and ultrasound for screening of women at average or intermediate risk of breast cancer. However, because of additional costs incurred by MRI screening, and the small gain in sensitivity, MRI screening is probably not warranted outside of high-risk populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".