Does Double Reading of Screening Breast MRI Scans Impact Recall Rates and Cancer Detection?
Bibliographic record
Abstract
Objective: To investigate the effect of double reads by a second radiologist on cancer detection rate (CDR), positive predictive value of recommendation for tissue diagnosis (PPV2), and the positive predictive value of biopsy performed (PPV3) for biopsy recommendations in high-risk screening breast MRIs. Methods: The policy of second reads on biopsies recommended for MRIs was prospectively implemented in October 2019. This IRB approved retrospective analysis compared consecutive high-risk screening breast MRI scans performed in a single academic institution between 06/01/2018 to 06/01/2019 (pre-intervention) with screening breast MRI scans performed between 10/31/2019 to 10/31/2020 (post-intervention). Pathology results after biopsy were recorded. Testing of association was performed using the Chi-square test. Results/Discussion: A total of 1124 screening breast MRIs in the pre-intervention and 1672 screening breast MRIs were performed in the post-intervention periods. Biopsies were recommended in 8.6% (97/1124) of pre-intervention and 5.5% (92/1672) of post-intervention MRIs ( P = .0012). There was a non-significant increase in PPV2 from pre-intervention 10.3% (10/97) to post-intervention 18.4% (17/92) ( P = .109) and in PPV3 from 14% (10/71) to 22.9% (17/74), respectively ( P = .17). Similar cancer detection rates, 8.9/1000 (10/1124) and 10.2/1000 (17/1672) ( P = .736) were diagnosed in pre-intervention and post-intervention periods, respectively. Conclusion: Double reading of screening breast MRI scans significantly reduced the number of unnecessary biopsies without significant impact in the PPVs or cancer detection rate.
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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 | 0.032 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".