Contrast-enhanced Digital Mammography: Initial Clinical Experience
Bibliographic record
Abstract
PURPOSE: To investigate the potential of using intravenous contrast material with full-field digital mammography to facilitate the detection and characterization of lesions in the breast. MATERIALS AND METHODS: Twenty-two women scheduled for biopsy because they were suspected of having abnormalities at breast imaging underwent imaging with contrast material-enhanced digital mammography. Six sequential images of the affected breast were obtained, with a contrast agent injected intravenously between the time the first and second images were obtained. Image processing included registration and logarithmic subtraction. Lesions were evaluated for the presence, morphology, and kinetics of enhancement. Lesion type, size, and pathologic findings were correlated with the findings at contrast-enhanced digital mammography. RESULTS: At contrast-enhanced digital mammography, enhancement was observed in eight of 10 patients with biopsy-proved cancers. In one case of ductal carcinoma in situ and one case of invasive ductal carcinoma, enhancement was not observed. No enhancement was seen in seven of 12 cases in which lesions were suspected of being malignant at initial imaging but were benign. Morphology generally correlated with the pathologic diagnosis. The kinetics of lesion enhancement showed similarity to that seen with gadolinium-enhanced magnetic resonance imaging but was not consistent. CONCLUSION: The results of this preliminary study suggest that contrast-enhanced digital mammography potentially may be useful in identification of lesions in the mammographically dense breast. Further investigation of contrast-enhanced digital mammography as a diagnostic tool for breast cancer 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".