Comparison of Breast Magnetic Resonance Imaging, Mammography, and Ultrasound for Surveillance of Women at High Risk for Hereditary Breast Cancer
Bibliographic record
Abstract
PURPOSE: Recommended surveillance for BRCA1 and BRCA2 mutation carriers includes regular mammography and clinical breast examination, although the effectiveness of these screening techniques in mutation carriers has not been established. The purpose of the present study was to compare breast magnetic resonance imaging (MRI) with ultrasound, mammography, and physical examination in women at high risk for hereditary breast cancer. PATIENTS AND METHODS: A total of 196 women, aged 26 to 59 years, with proven BRCA1 or BRCA2 mutations or strong family histories of breast or ovarian cancer underwent mammography, ultrasound, MRI, and clinical breast examination on a single day. A biopsy was performed when any of the four investigations was judged to be suspicious for malignancy. RESULTS: Six invasive breast cancers and one noninvasive breast cancer were detected among the 196 high-risk women. Five of the invasive cancers occurred in mutation carriers, and the sixth occurred in a woman with a previous history of breast cancer. The prevalence of invasive or noninvasive breast cancer in the 96 mutation carriers was 6.2%. All six invasive cancers were detected by MRI, all were 1.0 cm or less in diameter, and all were node-negative. In contrast, only three invasive cancers were detected by ultrasound, two by mammography, and two by physical examination. The addition of MRI to the more commonly available triad of mammography, ultrasound, and breast examination identified two additional invasive breast cancers that would otherwise have been missed. CONCLUSION: Breast MRI may be superior to mammography and ultrasound for the screening of women at high risk for hereditary breast cancer.
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 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".