Macroscopic handling and reporting of breast cancer specimens pre‐ and post‐neoadjuvant chemotherapy treatment: review of pathological issues and suggested approaches
Bibliographic record
Abstract
Neoadjuvant chemotherapy (NACT) is used increasingly in the treatment of invasive breast cancer and presents challenges for the pathologist in the handling and interpretation of tissues. Potential issues include pathological identification and localization of the residual tumour site; how best to assess pathological response (given the diversity of scoring systems described); the timing and assessment of axillary node biopsy; and the value of retesting any residual tumour for dissonance between core biopsy and post-treatment residual cancer cells for biomarker expression such as oestrogen and progesterone receptors and human epidermal growth factor receptor 2 (HER2). The role of the pathologist is critical in modern NACT approaches to breast cancer and is likely to remain challenging as novel agents and newer biomarkers become available. In this manuscript we review these issues and describe some practical approaches to handling and reporting these samples in the routine histopathology laboratory.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".