Clinical and Pathological Features and Survival Outcomes of Breast Cancers with Intermediate ER Expression
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most prevalent women's cancer, representing about a third of all cancers diagnosed in women. Estrogen receptor (ER) is an important transcription factor expressed in 70% to 75% of all breast cancers. Of these breast cancers, the majority express ER robustly in 91-100% of tumor cells. However, a minority of breast cancers express ER in intermediate levels between 11% and 90% of tumor cells. The characteristics and outcomes of this intermediate subset of ER-positive breast cancers are not well studied. METHODS: We retrospectively reviewed the medical records of all breast cancer patients treated in our Cancer Center over a period of 8 years. Patients were grouped according to the level of ER expression in tumors with negative/low ER expression, intermediate ER expression, and high ER expression, and the groups were compared for tumor characteristics and outcomes. RESULTS: Patients with high ER levels (91% to 100%) represented 75.6% (600 of 794 patients), patients with intermediate ER expression represented 11.5% of the entire group (91 of 794 patients), and patients with negative/low ER expression (ER expression 0-10%) represented 12.9% of the entire cohort (103 of 794 patients). Patients with intermediate ER expression presented at a younger age than patients with high-ER cancers, as well as a more advanced stage and higher grade. These characteristics were more akin to patients with negative/low ER levels. Mastectomy was the surgical method of resection more commonly in ER-intermediate tumors than in ER-high tumors. The relapse-free survival (RFS) of patients with ER-intermediate cancers was worse than the RFS of patients that expressed ER robustly. CONCLUSION: The level of expression of ER defines groups of patients with varying characteristics and prognoses. Patients in the intermediate ER expression group had notable differences from patients in the high ER expression group including younger age, (more often) a higher grade, and low PR positivity. Differences observed between the group of patients with intermediate ER expression and that with high ER expression may help to prioritize therapeutic algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".