Pituitary neuroendocrine tumors treated with stereotactic radiosurgery
Bibliographic record
Abstract
PURPOSE: Pituitary neuroendocrine tumors (pitNETs) are benign tumors that may recur after surgical resection or persist following medical management. The objective of this study was to evaluate outcomes and toxicities of patients with pitNETs treated with stereotactic radiosurgery (SRS) at a single institution. METHODS: We completed a retrospective, single-institution study of patients with pitNETs treated with frame-based, single-fraction, cobalt-60 SRS between September 2005 and June 2023. The primary endpoint was local tumor control. Secondary endpoints included endocrine control (for functional tumors), overall survival, and toxicities. RESULTS: A total of 88 lesions in 83 patients were treated with SRS. Most lesions (70%) were non-functional tumors. Of the 26 functioning tumors, 6 patients achieved endocrine remission with SRS alone (23%), and the remainder achieved remission with combined medical management. With a median patient follow-up of 4.7 years, no local tumor recurrences were observed with an estimated local control probability of 100%. Two- and five-year overall survival estimates were 97% (95% confidence interval [CI] 89-99) and 95% (95% CI 84-98), respectively. Causes of death were unrelated to PitNET or SRS. Twelve patients (14%) developed hypopituitarism after SRS. Despite the 34 lesions that were ≤ 3 mm from optic structures, no patients developed any optic neuropathy or visual decline post SRS. CONCLUSIONS: SRS is a highly effective modality for recurrent or residual pitNETs. This study observed a local control of 100% with no cases of optic toxicities after a median follow-up of 4.7 years. These observed findings suggest that dose de-escalation may be possible for future treatment of pitNETs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".