Contrast Enhanced Mammography in Breast Cancer Surveillance
Bibliographic record
Abstract
Abstract Purpose: Women with a personal history of breast cancer or DCIS (PHBC) are at increased risk of either a local recurrence or a new primary breast cancer. Adjunctive screening ultrasound or MRI is often used to supplement mammography. Contrast enhanced mammography (CEM) is reported to have higher sensitivity than MG and ultrasound, and similar performance with better accessibility than MRI. Methods: We introduced CEM as a routine single imaging modality for surveillance of those with PHBC. This report is of the first surveillance round outcomes comparing CEM with digital mammography. Results: 73/1191 (6.1%) patients were recalled for further assessment. 35 (48%) were true positives (TP), with 26 invasive cancers and 9 cases of DCIS, while 38 (52%) were false positive (FP) with a positive predictive value (PPV) 47.9%. 32/73 were recalled due to findings on MG, while 41/73 were only recalled due to Contrast. 14/73 had ‘minimal signs’ with a lesion identifiable with knowledge of the Contrast finding while 27/73 were ‘contrast only’. 41% (17/41) of those recalled due to contrast were TP. Contrast-only TPs were found in those with low and high mammographic density (MD). Bilateral screening breast US reduced by 55% in the year after routine surveillance CEM was implemented. Conclusion: Compared to MG, CEM as a single surveillance modality for those with PHBC has higher sensitivity and comparable specificity, identifying additional malignant lesions that appear to be clinically significant. Further investigation of interval cancer and subsequent round cancer detection rates is warranted.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".