Development of contrast digital mammography
Bibliographic record
Abstract
Development of breast tumors is often accompanied by angiogenesis--the formation of new blood vessels. It is possible to image the effects of this process by tracking the uptake and washout of contrast agents in the vicinity of a lesion. In this article, a method for carrying out contrast subtraction mammography on a full-field digital mammography unit is described. Spectral measurements and modeling were performed to optimize the choice of x-ray target, kilovoltage and x-ray beam filtration for contrast digital mammography (CDM) on an available digital mammography system. Phantom studies were carried out to determine the sensitivity of CDM to iodine. Detection of iodine area densities of 0.3 mg/cm2 is possible for a circular object with a radius of 1.3 mm, which allows detection of uptake levels in the breast typically seen with cancer and some benign breast conditions. It was found that with a molybdenum anode x-ray tube, copper filtration could be used to effectively shape the x-ray spectrum to maximize the proportion of x rays with energies above the k edge of iodine. Simple logarithmic subtraction was found to be adequate in suppressing background signals dependent on the x-ray beam intensity and background thickness of the breast. The total x-ray dose from the procedure ranges between 1 and 3 mGy, similar to that from a conventional single view film mammogram. A clinical pilot study is currently being carried out to evaluate this technique.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".