Percentage of Hormone Receptor Positivity in Breast Cancer Provides Prognostic Value: A Single-Institute Study
Bibliographic record
Abstract
BACKGROUND: A majority of breast cancer tumors express estrogen receptor (ER) and/or progesterone receptor (PR); however, the percentage of cancer cells expressing these receptors can range from 0-100%. The prognostic and therapeutic impact of the percentage of cells expressing hormone receptors in breast cancer is not fully understood. METHODS: A retrospective analysis of 411 breast cancer patients who were treated at the University of Nebraska Medical Center between 2010 and 2017 was performed. Patient tumors were evaluated for percentage of cells expressing ER and PR in conjunction with clinical outcomes. RESULTS: Patient tumors demonstrated a highly bimodal pattern of ER and PR staining with a majority of tumors demonstrating either a high percentage (> 80% of cells) or lack of cells (0%) staining for ER or PR. An increase in the percentage of ER positivity correlated with decreased local recurrence and improved overall survival. An increase in the percentage of PR positivity demonstrated a trend towards decreased local recurrence and improved overall survival, but was not statistically significant. CONCLUSIONS: Results based on both continuous and categorical evaluation of ER expression revealed that increasing expression correlated with improved patient outcomes. Similar evaluation of PR expression demonstrated a trend towards improved patient outcomes though not statistically significant. These findings suggest that the degree of hormone receptor positivity and not a Boolean representation of positivity could provide additional prognostic value in the treatment and management of breast cancer.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".