Discordance in Hormone Receptor Status Among Primary, Metastatic, and Second Primary Breast Cancers: Biological Difference or Misclassification?
Bibliographic record
Abstract
INTRODUCTION: Discordance in hormone receptor status has been observed between two breast tumors of the same patients; however, the degree of heterogeneity is debatable with regard to whether it reflects true biological difference or the limited accuracy of receptor assays. METHODS: A Bayesian misclassification correction method was applied to data on hormone receptor status of two primary breast cancers from the Surveillance, Epidemiology, and End Results database between 1990 and 2010 and to data on primary breast cancer and paired recurrent/metastatic disease assembled from a meta-analysis of the literature published between 1979 and 2014. RESULTS: The sensitivity and specificity of the estrogen receptor (ER) assay were estimated to be 0.971 and 0.920, respectively. After correcting for misclassification, the discordance in ER between two primary breast cancers was estimated to be 1.2% for synchronous ipsilateral pairs, 5.0% for synchronous contralateral pairs, 14.6% for metachronous ipsilateral pairs, and 25.0% for metachronous contralateral pairs. Technical misclassification accounted for 53%-83% of the ER discordance between synchronous primary cancers and 11%-25% of the ER discordance between metachronous cancers. The corrected discordance in ER between primary tumors and recurrent or metastatic lesions was 12.4%, and there were more positive-to-negative changes (10.1%) than negative-to-positive changes (2.3%). Similar patterns were observed for progesterone receptor (PR), although the overall discordance in PR was higher. CONCLUSION: A considerable proportion of discordance in hormone receptor status can be attributed to misclassification in receptor assessment, although the accuracy of receptor assays was excellent. Biopsy of recurrent tumors for receptor retesting should be conducted after considering feasibility, cost, and previous ER/PR status.
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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 | 0.136 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".