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
Why this work is in the frame
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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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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it