Can We Predict Atypical Ductal Hyperplasia Upstaging to Breast Cancer? External Validation of a Predictive Risk Calculator Using a Provincial Database
Bibliographic record
Abstract
INTRODUCTION: Guidelines recommend surgical excision of atypical ductal hyperplasia (ADH) owing to the 20% risk of upstaging. Strategies to identify patients with a low oncological risk of upstaging are highly sought after, because it would potentially allow for women to be offered short-term follow-up as an alternative to operation. Recently, a predictive ADH risk calculator was developed, capable of stratifying women into a low-risk cohort who had a 2% or lower of risk of upstaging to breast cancer. The purpose of this study is to externally validate the predictive upstaging risk calculator before its widespread clinical application. METHODS: A prospectively collected provincial database was queried for patients diagnosed with ADH on core needle biopsy (CNB) between January 2020 and December 2021. The risk calculator includes 5 variables which were collected: (1) lesion larger than 5 mm on ultrasound; (2) lesion larger than 5 mm on mammogram; (3) at least 1 “high-risk” lesion(s) on CNB; (4) pathological suspicion for cancer and; (5) incomplete removal of calcification on CNB with 1 point assigned for each metric present where a score of 0 was considered low-risk of upstaging. RESULTS: A total of 69 women were diagnosed with ADH on CNB and subsequently underwent surgical excision. The upstage rate was 30.1% (n = 23). Using the risk calculator, 10 women (14.5%) were deemed “low-risk” who had a score of 0. Among this cohort 0% (n = 0 of 10) upstaged to cancer. Patients with a score between 1 and 5 had an upstage rate between 17.6% and 76.9%. CONCLUSION: Using the ADH risk calculator, women can be successfully stratified into low and high upstaging risk groups.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".