Comparing outcomes of neoadjuvant endocrine therapy versus chemotherapy in ER-positive breast cancer: Results from a prospective institutional database.
Bibliographic record
Abstract
581 Background: While neoadjuvant chemotherapy (NACT) has been established as the standard of care for medically fit patients, there has been renewed interest in utilizing neoadjuvant endocrine therapy (NET) for the treatment of women with estrogen-receptor (ER) positive, HER-2 negative breast cancer. Rates of pCR are known to be low in this population, but there is inconsistent data regarding downstaging and long-term outcomes in a non-trial setting with NET vs NACT. Methods: A prospective institutional databaseof breast cancer patients treated with neoadjuvant therapy at the British Columbia Cancer Agency from 2012-2016 was analyzed to identify all medically fit patients with ER positive, HER2 negative breast cancer. Patients were then divided into two groups: those who received NET or NACT. Baseline characteristics were compared between groups. A matched analysis (age, stage and grade) was then performed to compare rates of downstaging, pCR and scores from a validated neoadjuvant therapy outcomes calculator (CPS+EG). Results: A total of 154 patients met eligibility criteria for this study. One hundred and six patients (69%) received NACT and 48 (31%) received NET. Women offered NACT were significantly younger (51 vs 64y, p < 0.001) than those offered endocrine therapy and presented with a higher clinical stage (LR 27.93, p = 0.002). According to multiple linear regression for downstaging, clinical stage followed by NACT were the most important predictors of downstaging. When matched for age, stage and grade, downstaging was significantly higher with NACT (31/48, 65%) as compared to NET (12/48, 25%), p < 0.001. Of these, 12.5% achieved pCR with NACT as compared to 2.1% with NET, LR 4.243, p = 0.039. No significant differences in CPS+EG scores were identified when comparing NACT to NET. Conclusions: Significantly higher rates of downstaging were achieved with NACT as compared to NET when patients were matched for age, stage and grade. Rates of pCR remain low for ER-positive breast cancer patients. Although not validated with the use of NET, CPS+EG scores predicting long-term outcomes were not significantly different with NET compared to NACT.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".