Is there an association between antipsychotic use and breast cancer risk? A retrospective observational case series
Bibliographic record
Abstract
Abstract Background Breast cancer remains a leading cause of cancer-related morbidity and mortality among women globally. Antipsychotic medications, particularly those that elevate serum prolactin, have been implicated as potential contributors to breast carcinogenesis. However, clinical evidence from the Gulf region remains limited. Methods A retrospective observational case series was conducted at Al-Masarra Hospital, Oman’s largest governmental psychiatric referral center. A total of 1,200 electronic medical records (2013–2025) of female patients receiving antipsychotic therapy were screened. Records containing breast-related keywords and laboratory-confirmed hyperprolactinemia were further reviewed. Only cases with histologically confirmed breast cancer following antipsychotic use were included. Results Four cases met the inclusion criteria. All patients were female, married, illiterate, and from Muscat. They were treated with typical antipsychotics and atypical antipsychotics. Duration of antipsychotic use ranged from 8 to 27 years. All cases had documented hyperprolactinemia prior to their breast cancer diagnosis. One patient had a first-degree relative with breast cancer, and three had comorbid metabolic conditions. Invasive breast cancer was diagnosed in all four cases, and no record of prior routine breast cancer screening was found. Conclusion This case series suggests a potential association between prolonged use of prolactin-elevating antipsychotics and the development of invasive breast cancer in a small subset of women. These findings highlight systemic barriers to early detection, such as stigma and limited health literacy. However, given the retrospective nature of the study and the very limited number of identified breast cancer cases ( n = 4), the possibility that this association occurred by chance cannot be excluded. This limitation must be considered when interpreting the results. Nevertheless, the findings highlight the necessity for national guidelines on prolactin monitoring, proactive screening protocols, enhanced interdisciplinary collaboration, and specialized education for mental health professionals, particularly for patients with elevated prolactin levels or a family history of cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".