Added cancer yield of breast magnetic resonance imaging screening in women with a prior history of chest radiation therapy
Bibliographic record
Abstract
BACKGROUND: Recommendation for breast magnetic resonance imaging (MRI) screening for women with a prior history of chest radiation is currently based on expert opinion, because existing data are very scant. The objective of this study was to evaluate added cancer yield of screening breast MRI in this population. METHODS: A retrospective review identified 98 women with a prior history of chest radiation therapy who had screening mammography and screening MRI performed at the authors' institution between January 2004 and July 2010. Medical records of these patients and results of 558 screening studies (296 mammograms and 262 MRI) were reviewed. Sensitivity, specificity, positive predictive value, negative predictive value, and added cancer yield were calculated. RESULTS: Malignancy was diagnosed in 13 patients, invasive cancer was diagnosed in 10 patients, and ductal carcinomas in situ was diagnosed in 3 patients. The median latency from completion of radiation to detection of the breast cancer was 18 years (range, 8-37 years). Of the 13 cancers, 12 (92%) were detected by MRI, and 9 (69%) by mammography. For mammography, the sensitivity, specificity, positive predictive value, and negative predictive value were 69%, 98%, 82%, and 95%, respectively; and, for MRI, these values were 92%, 94%, 71%, and 99%, respectively. In 4 of 98 patients, cancer was diagnosed on MRI only, resulting in an incremental cancer detection rate of 4.1% (95% confidence interval, 1.6%-10%). CONCLUSIONS: The current results indicated that MRI is a useful adjunct modality for screening high-risk women who have a prior history of chest radiation therapy, resulting in a 4.1% (4 of 98 women) added cancer detection rate. The authors concluded that both MRI and mammography should be used to screen women in this high-risk group.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".