Are Columnar Cell Lesions the Earliest Non-Obligate Precursor in the Low-Grade Breast Neoplasia Pathway?
Bibliographic record
Abstract
Columnar cell lesions (CCLs) of the breast comprise a spectrum of morphologic alterations of the terminal duct lobular unit involving variably dilated and enlarged acini lined by columnar epithelial cells. The World Health Organization currently classifies CCLs without atypia as columnar cell change (CCC) and columnar cell hyperplasia (CCH), whereas flat epithelial atypia (FEA) is a unifying term encompassing both CCC and CCH with cytologic atypia. CCLs have been increasingly recognized in stereotactic core needle biopsies (CNBs) performed for the assessment of calcifications. CCLs are believed to represent the earliest non-obligate precursor of low-grade invasive breast carcinomas as they share molecular alterations and often coexist with entities in the low-grade breast neoplasia pathway. Despite this association, however, the risk of progression of CCLs to invasive breast carcinoma appears low and may not exceed that of concurrent proliferative lesions. As the reported upgrade rates of pure CCL/FEA when identified as the most advanced high-risk lesion on CNB vary widely, the management of FEA diagnosed on CNB remains controversial. This review will include a historical overview of CCLs and will examine histologic diagnostic criteria, molecular alterations, prognosis and issues related to upgrade rates and clinical management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".