Variability in the diagnosis and reporting of metaplastic breast carcinoma: results of an international survey
Bibliographic record
Abstract
AIMS: Metaplastic breast carcinoma (MBC) is a heterogeneous group of invasive breast carcinoma with squamous, spindle cell, or mesenchymal elements. It may be monophasic or biphasic, and often coexists with invasive breast carcinoma of no special type (IBC-NST). Currently, there are no standardized guidelines for reporting MBC, and a diagnostic threshold for the metaplastic component is not established by the WHO classification. METHODS AND RESULTS: A survey conducted from April-July 2023 gathered responses from 44 pathologists worldwide. Most respondents were academic breast pathologists, and attended weekly breast tumour boards. The criteria for diagnosing MBC were highly varied, with cutoffs ranging from any/<10% to >90% metaplastic component. Although 90% was the most common threshold used, only 25% of respondents applied it. Pathologists generally preferred diagnosing invasive carcinoma with mixed features when the metaplastic component was ≤50% and MBC when the metaplastic component >50%. Most pathologists reported both the type and percentage of metaplastic component. In all, 43% reported core biopsy and resection specimen with different approaches. Diagnostic guidelines reportedly used (if any) were highly varied. CONCLUSION: The study underscores the need for standardized guidelines for MBC. Without clear and established diagnostic criteria, current data on MBC remains inconsistent and hinders further research into the clinical significance and prognostic implications of the metaplastic component.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".