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 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 | 0.001 | 0.000 |
| 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".