Lack of Standardization in the Processing and Reporting of Post-Neoadjuvant Breast Cancer Specimens
Bibliographic record
Abstract
CONTEXT.—: The use of neoadjuvant therapy in the management of early-stage invasive breast cancer is increasing. Residual Cancer Burden and other similar tools use pathologic characteristics of post-neoadjuvant therapy breast tumors to determine long-term outcome. However, there are no standardized guidelines for the pathologic evaluation of these specimens in the routine clinical setting. OBJECTIVE.—: To assess current practices among Canadian pathologists and pathology assistants with regard to the processing and reporting of post-neoadjuvant therapy breast specimens. DESIGN.—: An electronic survey was distributed to pathologists and pathology assistants across Canada. RESULTS.—: Sixty-three responses were obtained. A total of 48% (15 of 31) of surveyed pathologists reported familiarity with the Residual Cancer Burden tool. A total of 40% (25 of 63) of respondents reported a lack of routine use of specimen photography, and 35% (22 of 63) reported a lack of routine use of grossing diagrams. There was significant variation with respect to tumor bed sampling; the most common method was to submit 1 block per centimeter of tumor (20 of 63; 32%). There was also significant variation in the method of measuring residual tumor; the most common method was to measure the largest cross-section of residual tumor (16 of 32; 50%). CONCLUSIONS.—: There is a need for standardization of the evaluation of post-neoadjuvant therapy breast specimens in the routine clinical setting in Canada. We recommend the routine use of specimen mapping, submitting the largest cross section of tumor bed in toto, reporting tumor size as per American Joint Committee on Cancer and Residual Cancer Burden guidelines, and routinely including measurements of residual tumor cellularity and in situ disease in the final pathology report as per Residual Cancer Burden guidelines.
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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 | 0.065 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| 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".