Outcomes Associated With Stereotactic Radiosurgery After Multiple Resections of Nonfunctioning Pituitary Macroadenomas: An International, Multicenter Case Series
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Stereotactic radiosurgery (SRS) represents an effective treatment for nonfunctioning pituitary adenomas (NFPAs). However, no data have yet been published regarding results of SRS on NFPAs after multiple previous resections. METHODS: Retrospective multicentric data of patients diagnosed with NFPA and who underwent multiple resections (≥2) before SRS were reviewed and analyzed. The treatment interval spanned the period of 1992 to 2022. Cox regression and Kaplan-Meier curves were used to assess predictive factors and the probability of tumor control and hypopituitarism. RESULTS: Among the 311 patients (median age: 50.2 [IQR: 18.0] years), 226 (72.7%) had undergone ≥2 previous resections. The median margin dose was 14 Gy (IQR: 4.0 Gy), and the median tumor volume 3.6 cm 3 (IQR: 4.8). Overall, the probability of tumor control after SRS was 93.3% (CI 95%: 89.9-96.9) and 86.7% (CI 95%: 81.1-92.6) at 5 and 10 years, respectively. A margin dose >14 Gy was associated with a decreased risk of tumor progression (hazard ratio = 0.33, CI 95% = 0.15-0.75, P = .008). At a last clinical follow-up of 4.1 (IQR 6.1) years, 10.1% (30/296) developed at least 1 new hormone deficiency after SRS. The cumulative probability of new hormone deficiency was 6.1% (95% CI: 3.0-9.1), 10.3% (95% CI: 5.8-14.6), and 18.9% (95% CI: 11.5-25.8) at 3, 5, and 10 years after SRS, respectively. The average latency between SRS and development of new hormone deficiencies was 3.3 years (IQR 4.1). A maximum point dose to the pituitary stalk >10 Gy was associated with a new deficiency (hazard ratio = 4.06, CI 95% = 1.57-10.5, P -value = .004). CONCLUSION: For patients with NFPA with multiple previous resections, SRS offers effective local tumor control and a low risk of delayed hypopituitarism for managing these challenging adenomas. SRS should be strongly considered in patients with NFPA with 2 previous resections compared with considering a third resection.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".