Evaluation of the Utility of Screening Mammography for High-Risk Women Undergoing Screening Breast MR Imaging
Bibliographic record
Abstract
Purpose To evaluate the value of mammography in detecting breast cancer in high-risk women undergoing screening breast magnetic resonance (MR) imaging. Materials and Methods An ethics-approved, retrospective review of prospective databases was performed to identify outcomes of 3934 screening studies (1977 screening MR imaging examinations and 1957 screening mammograms) performed between January 2012 and July 2014 in 1249 high-risk women. Performance measures including recall and cancer detection rates, sensitivity, specificity, and positive predictive values were calculated for both mammography and MR imaging. Results A total of 45 cancers (33 invasive and 12 ductal carcinomas in situ) were diagnosed, 43 were seen with MR imaging and 14 with both mammography and MR imaging. Additional tests (further imaging and/or biopsy) were recommended in 461 screening MR imaging studies (recall rate, 23.3%; 95% confidence interval [CI]: 21.5%, 25.2%), and mammography recalled 217 (recall rate, 11.1%; 95% CI: 9.7%, 12.6%). The cancer detection rate for MR imaging was 21.8 cancers per 1000 examinations (95% CI: 15.78, 29.19) and that for mammography was 7.2 cancers per 1000 examinations (95% CI: 3.92, 11.97; P < .001). Sensitivity and specificity of MR imaging were 96% and 78% respectively, and those of mammography were 31% and 89%, respectively (P < .001). Positive predictive value for MR imaging recalls was 9.3% (95% CI: 6.83%, 12.36%) and that for mammography recalls was 6.5% (95% CI: 3.57%, 10.59%). Conclusion Contemporaneous screening mammography did not have added value in detection of breast cancer for women who undergo screening MR imaging. Routine use of screening mammography in women undergoing screening breast MR imaging warrants reconsideration. © RSNA, 2017 Online supplemental material is available for this article.
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".