Concordance of immunohistochemistry for predictive and prognostic factors in breast cancer between biopsy and surgical excision: a single-centre experience and review of the literature
Bibliographic record
Abstract
PURPOSE: Accurate evaluation of breast cancer on bioptic samples is of fundamental importance to guide therapeutic decisions, especially in the neoadjuvant or metastatic setting. We aimed to assess concordance for oestrogen receptor (ER), progesterone receptor (PR), c-erbB2/HER2 and Ki-67. We also reviewed the current literature to evaluate our results in the context of the data available at present. METHODS: We included patients who underwent both biopsy and surgical resection for breast cancer at San Matteo Hospital, Pavia, Italy, between January 2014 and December 2020. ER, PR, c-erbB2, and Ki-67 immunohistochemistry concordance between biopsy and surgical specimen was evaluated. ER was further analysed to include the recently defined ER-low-positive in our analysis. RESULTS: We evaluated 923 patients. Concordance between biopsy and surgical specimen for ER, ER-low-positive, PR, c-erbB2 and Ki-67 was, respectively, 97.83, 47.8, 94.26, 68 and 86.13%. Cohen's κ for interobserver agreement was very good for ER and good for PR, c-erbB2 and Ki-67. Concordance was especially low (37%) in the c-erbB2 1 + category. CONCLUSION: Oestrogen and progesterone receptor status can be safely assessed on preoperative samples. The results of this study advise caution in interpreting biopsy results regarding ER-low-positive, c-erbB2/HER and Ki-67 results due to a still suboptimal concordance. The low concordance for c-erbB2 1 + cases underlines the importance of further training in this area, in the light of the future therapeutic perspectives.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".