Dual-energy contrast-enhanced digital mammography: initial clinical results of a multireader, multicase study
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to compare the diagnostic accuracy of dual-energy contrast-enhanced digital mammography (CEDM) as an adjunct to mammography (MX) ± ultrasonography (US) with the diagnostic accuracy of MX ± US alone. METHODS: One hundred ten consenting women with 148 breast lesions (84 malignant, 64 benign) underwent two-view dual-energy CEDM in addition to MX and US using a specially modified digital mammography system (Senographe DS, GE Healthcare). Reference standard was histology for 138 lesions and follow-up for 12 lesions. Six radiologists from 4 institutions interpreted the images using high-resolution softcopy workstations. Confidence of presence (5-point scale), probability of cancer (7-point scale), and BI-RADS scores were evaluated for each finding. Sensitivity, specificity and ROC curve areas were estimated for each reader and overall. Visibility of findings on MX ± CEDM and MX ± US was evaluated with a Likert scale. RESULTS: The average per-lesion sensitivity across all readers was significantly higher for MX ± US ± CEDM than for MX ± US (0.78 vs. 0.71 using BIRADS, p = 0.006). All readers improved their clinical performance and the average area under the ROC curve was significantly superior for MX ± US ± CEDM than for MX ± US ((0.87 vs 0.83, p = 0.045). Finding visibility was similar or better on MX ± CEDM than MX ± US in 80% of cases. CONCLUSIONS: Dual-energy contrast-enhanced digital mammography as an adjunct to MX ± US improves diagnostic accuracy compared to MX ± US alone. Addition of iodinated contrast agent to MX facilitates the visualization of breast lesions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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