Pituitary Metastases: A Case Series and Scoping Review
Bibliographic record
Abstract
Purpose: To understand the natural history and optimal treatment strategy for pituitary gland metastasis. Methods: We performed both a retrospective chart review of patients treated at our institution and a scoping review of the topic. Results: The retrospective review identified seven patients with an average age of 59.6 years. Primary histologies included breast cancer (4), melanoma (1), renal cell carcinoma (1), and sarcoma (1). Two patients had anterior pituitary endocrine dysfunction, one of whom was the only patient with visual symptoms. All patients were treated with radiosurgery and two also underwent surgical resection. Overall survival ranged from 6.5 to 117 months. Literature review identified 166 patients from 71 studies. The most common primary cancer was lung (27.7%), followed by breast (18.7%) and renal (14.5%) cancer. 107 presented with endocrine dysfunction, including 41 cases of diabetes insipidus and 55 cases of hypopituitarism. 110 presented with visual compromise. 107 patients received radiotherapy, 96 underwent surgical resection and 44 received systemic chemotherapy/immunotherapy. Surgery was significantly associated with an increased likelihood of vision improvement and a decreased likelihood of endocrine normalization. Radiographic regression predicted visual improvement. Median overall survival was 9.9 months (range: 0.2–96). Conclusions: This scoping review showed that both radiosurgery and surgical resection have been frequently used to treat pituitary metastases with good response. Vision improvement is more likely to happen following surgical resection, likely at the expense of endocrine dysfunction. Despite treatment and radiographic response, patient survival remains less than a year. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".