FAST <scp>MRI</scp> breast screening revisited
Bibliographic record
Abstract
INTRODUCTION: Screening for breast cancer in high-risk women takes about 40 minutes to acquire an MRI scan and is time-intensive to report. There is recent interest in the performance of an abbreviated MRI protocol (FAST) in the screening setting. FAST scans have a reported negative predictive value of 99.8%. This study evaluates the false positive rates (FPR) and recall rates for FAST scans as compared to full diagnostic studies (FD). METHODS: A database of all screening breast MRI scans performed at our institution between 30 June 2013 and 1 July 2014 (n = 591) was created by one of the researchers, who did not subsequently analyse the MRI scans. The T1W and first post-contrast and subtracted images from each of these scans (FAST protocol) were assessed by experienced breast MRI radiologists, blinded to the final diagnosis. The findings were then compared with the FD result. RESULTS: The recall rates were 6.6% for FAST scans and 5.8% for FD scans. FPR rates were 4.7% and 3.9% respectively. There is no statistically significant difference in the recall rates or FPR of FAST scans in comparison with full diagnostic studies. CONCLUSIONS: Given the absence of statistically significant difference in the FPR and recall rates in comparison with FD, FAST scans can replace FD for screening of breast cancer.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".