Magnetic Resonance Imaging of the Breast Improves Detection of Invasive Cancer, Preinvasive Cancer, and Premalignant Lesions during Surveillance of Women at High Risk for Breast Cancer
Bibliographic record
Abstract
PURPOSE: To assess the diagnostic accuracy of mammography, ultrasound, and magnetic resonance imaging (MRI) of the breast in the surveillance of women at high risk for breast cancer. EXPERIMENTAL DESIGN: In this prospective comparison study, women at high risk for breast cancer were offered annual surveillance examinations, consisting of mammography, ultrasound, and MRI, at a single tertiary care breast center. The sensitivity and specificity of each modality was based on the histopathologic evaluation of suspicious findings from all modalities plus the detected interval cancers. RESULTS: Three hundred and twenty-seven women underwent 672 complete imaging rounds. Of a total of 28 detected cancers, 14 were detected by mammography, 12 by ultrasound, and 24 by MRI, which resulted in sensitivities of 50%, 42.9%, and 85.7%, respectively (P < 0.01). MRI detected not only significantly more invasive but also significantly more preinvasive cancers (ductal carcinoma in situ). Mammography, ultrasound, and MRI led to 25, 26, and 101 false-positive findings, which resulted in specificities of 98%, 98%, and 92%, respectively (P < 0.05). Thirty-five (35%) of these false-positive findings were atypical ductal hyperplasias, lesions considered to be of premalignant character. Nine (26%) of those were detected by mammography, 2 (6%) with ultrasound, and 32 (91%) with MRI (P < 0.01). CONCLUSION: Our results show that MRI of the breast improves the detection of invasive cancers, preinvasive cancers, and premalignant lesions in a high-risk population and should therefore become an integral part of breast cancer surveillance in these patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".