Treatment of pituitary neoplasms with temozolomide
Bibliographic record
Abstract
Temozolomide, an orally administered alkylating agent, is used to treat malignant gliomas. Recent reports also have documented its efficacy in the treatment of pituitary adenomas and carcinomas. Temozolomide methylates DNA and thereby exhibits an antitumor effect. O⁶-methylguanine-DNA methyltransferase (MGMT), a DNA repair enzyme, removes alkylating adducts induced by temozolomide, counteracting its effects. The authors of this review conducted a Medline database search regarding temozolomide in the treatment of pituitary tumors. Demographic characteristics, tumor types, and therapeutic responses were noted in all patients. Data regarding MGMT immunoexpression, which was documented in some studies, were correlated with information regarding clinical and radiologic responses. To date, there have been 19 reported cases of adenohypophyseal tumors treated with temozolomide, including 13 adenomas and 6 carcinomas. Ten of those 13 adenomas responded favorably, and 2 nonresponsive tumors had high-level MGMT immunoexpression. All 6 carcinomas responded to therapy, but data regarding MGMT expression were available for only 3 patients, and each had low MGMT expression. In 2 adenomas, morphologic studies were performed both before and after the patients received temozolomide. The responsive tumor had necrosis, hemorrhage, fibrosis, and neuronal differentiation. The nonresponsive tumor had no changes. There have been no reported complications attributable to temozolomide. The current results indicated that temozolomide is efficacious in the treatment of aggressive pituitary adenomas and pituitary carcinomas. Evidence indicated that low-level MGMT immunoexpression is correlated with a favorable response. A significant proportion of pituitary adenomas and carcinomas had low MGMT immunoexpression.
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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 | 0.000 |
| 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) | 0.001 | 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".