Contrast-enhanced Digital Mammography: Initial Clinical Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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