Abstract PO1-18-02: Histologic grade is a better predictor of specific survival than Ki67 in localized ER+/HER2- breast cancer: a real-world study
Bibliographic record
Abstract
Abstract Introduction: Breast cancer (BC) is the most common cancer in women. The determination of prognostic factors is relevant for the decision of systemic therapy. Objective: To determine, in the real world, the prognostic role of Ki67 and histologic grade (HG) in patients with non-metastatic BC in two cancer centers; an academic and a community hospital. Methodology: Retrospective analysis of a longitudinal BC patients registry. Clinicopathological characteristics and disease specific survival (DSS) of women diagnosed in stages I/II/III between the years 2012-2021 were analyzed. Results: We evaluated 3,969 cases that met the inclusion criteria. In the univariate analysis, prognostic factors significantly associated with DSS were: reason for consultation (screening vs symptoms), stage, hormone receptor status, HG and Ki67. On multivariate analysis, stage III, Ki67 ≥20%, and GH3 were significantly associated with a risk of death of 4.41, 2.52, and 1.92; respectively, regardless of the treatment center and BC subtype. However, in the hormone receptor positive (HR+)/HER2 + group the HG presented greater discriminatory power than Ki67. Hazard ratio 2.0 for both, but not statistically significant for Ki67. The ROC-AUC curves for Ki67 indicated that the best cut-off point for DSS was 20%, for the entire cohort and also for the HR+/HER2- group. Conclusions: The behavior of the prognostic variables was expected and coincided with the literature. HG seems to be a better predictor of specific mortality in HR+ BC. Ki67 showed a cut-off value consistent with that suggested by expert consensus. Citation Format: Cesar SÁNCHEZ, Cataldo Alejandro, Benjamin Walbaum, FRANCISCO ACEVEDO, Christine Constabel, Antoine Saure, Pablo Rey. Histologic grade is a better predictor of specific survival than Ki67 in localized ER+/HER2- breast cancer: a real-world study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-18-02.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".