Impact of Digital Mammography on Cancer Detection and Recall Rates: 11.3 Million Screening Episodes in the English National Health Service Breast Cancer Screening Program
Bibliographic record
Abstract
Purpose To report the impact of changing from screen-film mammography to digital mammography (DM) in a large organized national screening program. Materials and Methods A retrospective analysis of prospectively collected annual screening data from 2009-2010 to 2015-2016 for the 80 facilities of the English National Health Service Breast Cancer Screening Program, together with estimates of DM usage for three time periods, enabled the effect of DM to be measured in a study of 11.3 million screening episodes in women aged 45-70 years (mean age, 59 years). Regression models were used to estimate percentage and absolute change in detection rates due to DM. Results The overall cancer detection rate was 14% greater with DM (P < .001). There were higher rates of detection of grade 1 and 2 invasive cancers (both ductal and lobular), but no change in the detection of grade 3 invasive cancers. The recall rate was almost unchanged by the introduction of DM. At prevalent (first) screening episodes for women aged 45-52 years, DM increased the overall detection rate by 19% (P < .001) and for incident screening episodes in women aged 53-70 years by 13% (P < .001). Conclusion The overall cancer detection rate was 14% greater with digital mammography with no change in recall rates and without confounding by changes in other factors. There was a substantially higher detection of grade 1 and grade 2 invasive cancers, including both ductal and lobular cancers, but no change in the detection of grade 3 invasive cancers. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by C.I. Lee and J.M. Lee in this issue.
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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 | 0.001 | 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 | 0.000 |
| 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".