A Single-Center Audit of BI-RADS 3 Assessment Category Utilization in Mammography and Breast Ultrasound
Bibliographic record
Abstract
Purpose: To evaluate outcomes of breast lesions assessed at our institution as probably benign (Breast Imaging Reporting and Data System [BI-RADS] category 3) with an expected malignancy rate of less than or equal to 2 %. Methods: Average-risk women with a BI-RADS 3 assessment following mammographic and/or ultrasound evaluation at our institution between January 1 and December 31, 2017 were included. Cancer yield was calculated within 90 days and at 6-month intervals up to 36 months. Results: Among 517 women (median age, 52 years; range, 13–89 years) with a BI-RADS 3 assessment, 349 (67.5 %) underwent biopsy or completed follow-up imaging up to 36 months. One hundred and 68 (32.5 %) were lost to follow-up. Thirty of 349 (8.6 %) had their imaging upgraded and underwent biopsy, yielding six cancers (cancer yield, 6 of 349 women [1.7 %]). Among 569 lesions assessed as BI-RADS 3, 92 (16.2 %) were characterized by morphologic features other than those validated as probably benign in prospective clinical studies. Fifty three of 517 women (10.3 %) had follow-up beyond 24 months, and 24 (4.6 %) had follow-up beyond 36 months. Conclusion: Overall utilization of the BI-RADS 3 assessment category at our institution is appropriate with a 1.7 % cancer yield. However, the rate of loss to follow-up, percentage of non-validated findings assessed as probably benign, and redundancy in follow-up protocols are too high, and warrant intervention. A patient handout explaining the BI-RADS 3 assessment category and automatic scheduling of follow-up studies have been implemented at our center to address loss to follow-up.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".