Upgrade rate of percutaneously diagnosed pure flat epithelial atypia: systematic review and meta-analysis of 1,924 lesions
Bibliographic record
Abstract
CONTEXT: Management remains controversial due to the risk of upgrade for malignancy from flat epithelial atypia (FEA). Data about the frequency and malignancy upgrade rates are scant. Namely, observational follow-up is advised by many studies in cases of pure FEA on core biopsy and in the absence of an additional surgical excision. For cases of pure FEA, the American College of Surgeons no longer recommends surgical excision but rather recommends observation with clinical and imaging follow-up. OBJECTIVES: The aim of this study is to perform a systematic review and meta-analysis to calculate the pooled upgrade of pure FEA following core needle biopsies. METHODS: A search of MEDLINE and Embase databases were conducted in December 2020. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A fixed- or random-effects model was utilized. Heterogeneity among studies was estimated by utilizing the I2 statistic and considered high if the I2 was greater than 50%. The random-effects model with the DerSimonian and Laird method was utilized to calculate the pooled upgrade rate and its 95% confidence interval. RESULTS: A total of 1924 pure FEA were analyzed among 59 included studies. The overall pooled upgrade rate to malignancy was 8.8%. The pooled upgrade rate for mammography only was 8.9%. The pooled upgrade rate for ultrasound was 14%. The pooled upgrade rate for mammography and ultrasound combined was 8.8%. The pooled upgrade rate for MRI-only cases was 27.3%. CONCLUSIONS: Although the guidelines for the management of pure FEA are variable, our data support that pure FEA diagnosed at core needle biopsy should undergo surgical excision since the upgrade rate >2%.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".