Diagnostic Accuracy of Digital versus Film Mammography: Exploratory Analysis of Selected Population Subgroups in DMIST
Bibliographic record
Abstract
PURPOSE: To retrospectively compare the accuracy of digital versus film mammography in population subgroups of the Digital Mammographic Imaging Screening Trial (DMIST) defined by combinations of age, menopausal status, and breast density, by using either biopsy results or follow-up information as the reference standard. MATERIALS AND METHODS: DMIST included women who underwent both digital and film screening mammography. Institutional review board approval at all participating sites and informed consent from all participating women in compliance with HIPAA was obtained for DMIST and this retrospective analysis. Areas under the receiver operating characteristic curve (AUCs) for each modality were compared within each subgroup evaluated (age < 50 vs 50-64 vs >or= 65 years, dense vs nondense breasts at mammography, and pre- or perimenopausal vs postmenopausal status for the two younger age cohorts [10 new subgroups in toto]) while controlling for multiple comparisons (P < .002 indicated a significant difference). All DMIST cancers were evaluated with respect to mammographic detection method (digital vs film vs both vs neither), mammographic lesion type (mass, calcifications, or other), digital machine type, mammographic and pathologic size and diagnosis, existence of prior mammographic study at time of interpretation, months since prior mammographic study, and compressed breast thickness. RESULTS: Thirty-three centers enrolled 49 528 women. Breast cancer status was determined for 42,760 women, the group included in this study. Pre- or perimenopausal women younger than 50 years who had dense breasts at film mammography comprised the only subgroup for which digital mammography was significantly better than film (AUCs, 0.79 vs 0.54; P = .0015). Breast Imaging Reporting and Data System-based sensitivity in this subgroup was 0.59 for digital and 0.27 for film mammography. AUCs were not significantly different in any of the other subgroups. For women aged 65 years or older with fatty breasts, the AUC showed a nonsignificant tendency toward film being better than digital mammography (AUCs, 0.88 vs 0.70; P = .0025). CONCLUSION: Digital mammography performed significantly better than film for pre- and perimenopausal women younger than 50 years with dense breasts, but film tended nonsignificantly to perform better for women aged 65 years or older with fatty breasts.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".