Diagnosing Breast Lesions With Contrast-Enhanced 3-Dimensional Power Doppler Imaging
Bibliographic record
Abstract
OBJECTIVE: To compare mammography with contrast-enhanced 2- and 3-dimensional power Doppler imaging for the diagnosis of breast cancer. METHODS: Fifty-five patients, who underwent breast biopsies with histopathologic assessment, participated in a study of mammography and contrast-enhanced sonography. Levovist (Berlex Laboratories, Montville, NJ) and Optison (Mallinckrodt, St Louis, MO) were administrated to 22 and 33 patients, respectively. Precontrast and postcontrast 2-dimensional power Doppler data of the lesion were obtained with an HDI 3000 system (Philips Medical Systems, Bothell, WA), and 3-dimensional data were acquired with an LIS 6000A system (Life Imaging Systems Inc, London, Ontario, Canada). Two independent and blinded readers assessed diagnosis. Receiver operating characteristic curves were computed individually and in combination for mammography and 2- and 3-dimensional sonography (before and after contrast). Histopathologic and imaging parameters were compared by Mann-Whitney statistics. RESULTS: Mammographic findings were available for 50 patients, biopsy for 54, and 2- and 3-dimensional sonographic images for 53 and 52, respectively. Of the 50 patients who had all 4 measures, 15 (30%) had malignancies. The areas under the receiver operating characteristic curve for the diagnosis of breast cancer were 0.51 for 2-dimensional contrast-enhanced imaging, 0.60 for 3-dimensional power Doppler imaging, and 0.76 for 3-dimensional contrast-enhanced imaging (P < .01). Mammography produced an area of 0.86, which increased when combined with 3-dimensional contrast-enhanced imaging to 0.90 and with all sonographic modalities to 0.96 (P < .001). The histopathologic diagnosis of benign or malignant correlated with the presence or absence of anastomoses and with the degree of vascularity assessed with contrast-enhanced 3-dimensional power Doppler imaging (P = .007 and .02). CONCLUSIONS: Contrast-enhanced 3-dimensional power Doppler imaging increases the ability to diagnose breast cancer relative to conventional 2- and 3-dimensional sonographic imaging.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".