Silent Corticotroph Staining Pituitary Neuroendocrine Tumors: Prognostic Significance in Radiosurgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is conflicting evidence on the significance of adrenocorticotrophic hormone (ACTH) staining in the prognosis of nonfunctioning pituitary neuroendocrine tumors (NFpitNETs). The objective of this study was to define the effect of ACTH immunostaining on clinical and radiographic outcomes of stereotactic radiosurgery (SRS) for NFpitNETs. METHODS: This retrospective, multicenter study included patients managed with SRS for NFpitNET residuals. The patients were divided into 2 cohorts: (1) silent corticotroph (SC) for NFpitNETs with positive ACTH immunostaining and (2) non-SC NFpitNETs. Rates of local tumor control and the incidence of post-treatment pituitary and neurological dysfunction were documented. Factors associated with radiological and clinical outcomes were also analyzed. RESULTS: The cohort included 535 patients from 14 centers with 84 (15.7%) patients harboring silent corticotroph NFpitNETs (SCs). At last follow-up, local tumor progression occurred in 11.9% of patients in the SC compared with 8.1% of patients in the non-SC cohort (P = .27). No statistically significant difference was noted in new-onset hypopituitarism rates (10.7% vs 15.4%, P = .25) or visual deficits (3.6% vs 1.1%, P = .088) between the 2 cohorts at last follow-up. When controlling for residual tumor volume, maximum dose, and patient age and sex, positive ACTH immunostaining did not have a significant correlation with local tumor progression (hazard ratio = 1.69, 95% CI = 0.8-3.61, P = .17). CONCLUSION: In contemporary radiosurgical practice with a single fraction dose of 8-25 Gy (median 15 Gy), ACTH immunostaining in NFpitNETs did not appear to confer a significantly reduced rate of local tumor control after SRS.
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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 | 0.004 |
| 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".