Patterns, Predictors, and Prognostic Implication of Treatment-Related Amenorrhea in Patients With Breast Cancer
Bibliographic record
Abstract
Background: Treatment-related amenorrhea (TRA) is a common side effect of treatment in premenopausal patients with breast cancer, with important consequences for patient counseling and management. Its occurrence and potential influence on survival outcomes remain active areas of investigation. This study aimed to evaluate the incidence, risk factors, and prognostic significance of TRA in patients with breast cancer. Methods: This is a retrospective cohort study. Patients were interviewed during and after chemotherapy to assess their menstrual status. Sociodemographic, clinical, and treatment data of patients were also collected. TRA was classified into early amenorrhea (EA) and late amenorrhea (LA) based on the duration of amenorrhea. Univariable and multivariable logistic regression were used to identify risk factors of EA and LA. Kaplan-Meier curves and Cox proportional hazards analyses were used to investigate the impact of EA and LA on 3-year overall survival (OS). Results: There were 81 patients who were eligible for the final analysis. Of these subjects, 14 (17.3%) developed no amenorrhea, 67 (82.7%) developed EA, and 45 (55.6%) developed LA. We did not find any significant independent risk factor for EA. Age > 45 years (odds ratio (OR): 4.00; confidence interval (CI): 1.23 - 13.01; P = 0.021) and the usage of hormonal therapy (OR: 4.96; CI: 1.58 - 15.53; P = 0.006) independently significantly increase the risk of LA, whereas a metastatic disease status decreased the risk (OR: 0.20; CI: 0.04 - 0.90; P = 0.036). Both EA (hazard ratio (HR) = 0.262, CI: 0.105 - 0.653; P = 0.002) and LA (HR = 0.234, CI: 0.091 - 0.604; P = 0.001) were associated with an improved 3-year OS rate. Conclusions: Age > 45 years and the usage of hormonal therapy are risk factors for LA, while metastatic disease was associated with a decreased risk. Both EA and LA had a significant association with favorable 3-year OS. These findings enable clinicians to provide personalized guidance, tailor treatment strategies, and improve the outcomes of premenopausal patients with breast cancer. Standardization of how TRA is defined and assessed in future studies is essential to improve comparability and enhance the understanding of its clinical implications.
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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 | 0.000 | 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".