Comparative performance of modern digital mammography systems in a large breast screening program
Bibliographic record
Abstract
PURPOSE: To compare physical measures pertaining to image quality among digital mammography systems utilized in a large breast screening program. To examine qualitatively differences in these measures and differences in clinical cancer detection rates between CR and DR among sites within that program. METHODS: As part of the routine quality assurance program for screening, field measurements are made of several variables considered to correlate with the diagnostic quality of medical images including: modulation transfer function, noise equivalent quanta, d' (an index of lesion detectability) and air kerma to allow estimation of mean glandular dose. In addition, images of the mammography accreditation phantom are evaluated. RESULTS: It was found that overall there were marked differences between the performance measures of DR and CR mammography systems. In particular, the modulation transfer functions obtained with the DR systems were found to be higher, even for larger detector element sizes. Similarly, the noise equivalent quanta, d', and the phantom scores were higher, while the failure rates associated with low signal-to-noise ratio and high dose were lower with DR. These results were consistent with previous findings in the authors' program that the breast cancer detection rates at sites employing CR technology were, on average, 30.6% lower than those that used DR mammography. CONCLUSIONS: While the clinical study was not large enough to allow a statistically powered system-by-system assessment of cancer detection accuracy, the physical measures expressing spatial resolution, and signal-to-noise ratio are consistent with the published finding that sites employing CR systems had lower cancer detection rates than those using DR systems for screening mammography.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".