Quality control for digital mammography in the ACRIN DMIST trial: Part I
Bibliographic record
Abstract
The Digital Mammography Imaging Screening Trial, conducted by the American College of Radiology Imaging Network, is a clinical trial designed to compare the accuracy of full-field digital mammography (FFDM) versus screen-film mammography in a screening population. Five FFDM systems from four manufacturers (Fischer, Fuji, General Electric, and Lorad) were employed in the study at 35 clinical sites. A core physics team devised and implemented tests to evaluate these systems. A detailed description of physics and quality control tests is presented, including estimates of: mean glandular dose, modulation transfer function (MTF), 2D noise power spectra, and signal-to-noise ratio (SNR). The mean glandular doses for the standard breast ranged from 0.79 to 2.98 mGy, with 1.62 mGy being the average across all units and machine types. For the five systems evaluated, the MTF dropped to 50% at markedly different percentages (22% to 87%) of the Nyquist limit, indicating that factors other than detector element (del) size have an important effect on spatial resolution. Noise power spectra and SNR were measured; however, we found that it was difficult to standardize and compare these between units. For each machine type, the performance as measured by the tests was very consistent, and no predictive benefit was seen for many of the tests during the 2-year period of the trial. It was found that, after verification of proper operation during acceptance testing, if systems failed they generally did so suddenly rather than through gradual deterioration of performance. Because of the relatively short duration of this study further, investigation of the long-term failure characteristics of these systems is advisable.
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.
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 | 0.001 | 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 | 0.000 |
| 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 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".